Datasets:

ballagb19
/

research-hunter

@@ -1,771 +1,827 @@
 {
   "papers": [
     {
-      "title": "Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages",
-      "abstract": "We present Afro-MNIST, a set of synthetic MNIST-style datasets for four orthographies used in Afro-Asiatic and Niger-Congo languages: Ge`ez (Ethiopic), Vai, Osmanya, and N'Ko. These datasets serve as \"drop-in\" replacements for MNIST. We also describe and open-source a method for synthetic MNIST-style dataset generation from single examples of each digit. These datasets can be found at https://github.com/Daniel-Wu/AfroMNIST. We hope that MNIST-style datasets will be developed for other numeral systems, and that these datasets vitalize machine learning education in underrepresented nations in the research community.",
       "authors": [
-        "Daniel J Wu",
-        "Andrew C Yang",
-        "Vinay U Prabhu"
       ],
-      "year": "2020",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2009.13509v1",
-      "citations": 0,
-      "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2009.13509v1",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Daniel J Wu et al. (2020). Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages. arXiv Preprint."
     },
     {
-      "title": "TinyTorch: Building Machine Learning Systems from First Principles",
-      "abstract": "Machine learning education faces a fundamental gap: students learn algorithms without understanding the systems that execute them. They study gradient descent without measuring memory, attention mechanisms without analyzing O(N^2) scaling, optimizer theory without knowing why Adam requires 3x the memory of SGD. This \"algorithm-systems divide\" produces practitioners who can train models but cannot debug memory failures, optimize inference latency, or reason about deployment trade-offs--the very skills industry demands as \"ML systems engineering.\" We present TinyTorch, a 20-module curriculum that closes this gap through \"implementation-based systems pedagogy\": students construct PyTorch's core components (tensors, autograd, optimizers, CNNs, transformers) in pure Python, building a complete framework where every operation they invoke is code they wrote. The design employs three patterns: \"progressive disclosure\" of complexity, \"systems-first integration\" of profiling from the first module, and \"build-to-validate milestones\" recreating 67 years of ML breakthroughs--from Perceptron (1958) through Transformers (2017) to MLPerf-style benchmarking. Requiring only 4GB RAM and no GPU, TinyTorch demonstrates that deep ML systems understanding is achievable without specialized hardware. The curriculum is available open-source at mlsysbook.ai/tinytorch.",
       "authors": [
-        "Vijay Janapa Reddi"
       ],
-      "year": "2026",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2601.19107v2",
-      "citations": 0,
-      "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2601.19107v2",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Vijay Janapa Reddi (2026). TinyTorch: Building Machine Learning Systems from First Principles. arXiv Preprint."
     },
     {
-      "title": "Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education",
-      "abstract": "The rapid advancement of Large Language Models (LLMs) has opened new avenues in education. This study examines the use of LLMs in supporting learning in machine learning education; in particular, it focuses on the ability of LLMs to identify common errors of practice (pitfalls) in machine learning code, and their ability to provide feedback that can guide learning. Using a portfolio of code samples, we consider four different LLMs: one closed model and three open models. Whilst the most basic pitfalls are readily identified by all models, many common pitfalls are not. They particularly struggle to identify pitfalls in the early stages of the ML pipeline, especially those which can lead to information leaks, a major source of failure within applied ML projects. They also exhibit limited success at identifying pitfalls around model selection, which is a concept that students often struggle with when first transitioning from theory to practice. This questions the use of current LLMs to support machine learning education, and also raises important questions about their use by novice practitioners. Nevertheless, when LLMs successfully identify pitfalls in code, they do provide feedback that includes advice on how to proceed, emphasising their potential role in guiding learners. We also compare the capability of closed and open LLM models, and find that the gap is relatively small given the large difference in model sizes. This presents an opportunity to deploy, and potentially customise, smaller more efficient LLM models within education, avoiding risks around cost and data sharing associated with commercial models.",
       "authors": [
-        "Smitha Kumar",
-        "Michael A. Lones",
-        "Manuel Maarek",
-        "Hind Zantout"
       ],
-      "year": "2025",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2505.18220v1",
-      "citations": 0,
       "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2505.18220v1",
       "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Smitha Kumar et al. (2025). Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education. arXiv Preprint."
     },
     {
-      "title": "Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification",
-      "abstract": "With the recent advances in A.I. methodologies and their application to medical imaging, there has been an explosion of related research programs utilizing these techniques to produce state-of-the-art classification performance. Ultimately, these research programs culminate in submission of their work for consideration in peer reviewed journals. To date, the criteria for acceptance vs. rejection is often subjective; however, reproducible science requires reproducible review. The Machine Learning Education Sub-Committee of SIIM has identified a knowledge gap and a serious need to establish guidelines for reviewing these studies. Although there have been several recent papers with this goal, this present work is written from the machine learning practitioners standpoint. In this series, the committee will address the best practices to be followed in an A.I.-based study and present the required sections in terms of examples and discussion of what should be included to make the studies cohesive, reproducible, accurate, and self-contained. This first entry in the series focuses on the task of image classification. Elements such as dataset curation, data pre-processing steps, defining an appropriate reference standard, data partitioning, model architecture and training are discussed. The sections are presented as they would be detailed in a typical manuscript, with content describing the necessary information that should be included to make sure the study is of sufficient quality to be considered for publication. The goal of this series is to provide resources to not only help improve the review process for A.I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study. We hope to provide quantitative metrics in what otherwise may be a qualitative review process.",
       "authors": [
-        "Timothy L. Kline",
-        "Felipe Kitamura",
-        "Ian Pan",
-        "Amine M. Korchi",
-        "Neil Tenenholtz",
-        "Linda Moy",
-        "Judy Wawira Gichoya",
-        "Igor Santos",
-        "Steven Blumer",
-        "Misha Ysabel Hwang",
-        "Kim-Ann Git",
-        "Abishek Shroff",
-        "Elad Walach",
-        "George Shih",
-        "Steve Langer"
       ],
-      "year": "2022",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2202.01863v1",
-      "citations": 0,
       "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2202.01863v1",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Timothy L. Kline et al. (2022). Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification. arXiv Preprint."
     },
     {
-      "title": "Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States",
       "authors": [
-        "Ninad Salastekar",
-        "Charles M. Maxfield",
-        "Tarek N. Hanna",
-        "Elizabeth A. Krupinski",
-        "Darel E. Heitkamp",
-        "Lars J. Grimm"
       ],
       "year": "2023",
-      "journal": "Academic Radiology",
-      "doi": "https://doi.org/10.1016/j.acra.2023.01.005",
-      "pdf_url": null,
-      "citations": 10,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.acra.2023.01.005",
-      "relevance": 0.05,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Ninad Salastekar et al. (2023). Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States. Academic Radiology. https://doi.org/https://doi.org/10.1016/j.acra.2023.01.005"
     },
     {
-      "title": "Exploring the impact of ChatGPT on education: A web mining and machine learning approach",
       "authors": [
-        "Abderahman Rejeb",
-        "Karim Rejeb",
-        "Andrea Appolloni",
-        "Horst Treiblmaier",
-        "Mohammad Iranmanesh"
       ],
       "year": "2024",
-      "journal": "The International Journal of Management Education",
-      "doi": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "pdf_url": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "citations": 7,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "relevance": 0.035,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Abderahman Rejeb et al. (2024). Exploring the impact of ChatGPT on education: A web mining and machine learning approach. The International Journal of Management Education. https://doi.org/https://doi.org/10.1016/j.ijme.2024.100932"
     },
     {
-      "title": "Predicting student dropouts with machine learning: An empirical study in Finnish higher education",
       "authors": [
-        "Matti Vaarma",
-        "Hongxiu Li"
       ],
-      "year": "2024",
-      "journal": "Technology in Society",
-      "doi": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "pdf_url": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "citations": 7,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "relevance": 0.035,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Matti Vaarma & Hongxiu Li (2024). Predicting student dropouts with machine learning: An empirical study in Finnish higher education. Technology in Society. https://doi.org/https://doi.org/10.1016/j.techsoc.2024.102474"
     },
     {
-      "title": "Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques",
       "authors": [
-        "Waleed Alsabhan"
       ],
       "year": "2023",
-      "journal": "Sensors",
-      "doi": "https://doi.org/10.3390/s23084149",
-      "pdf_url": "https://www.mdpi.com/1424-8220/23/8/4149/pdf?version=1682010179",
-      "citations": 6,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.3390/s23084149",
-      "relevance": 0.03,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Waleed Alsabhan (2023). Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques. Sensors. https://doi.org/https://doi.org/10.3390/s23084149"
     },
     {
-      "title": "Integrating AI and Machine Learning in STEM education: Challenges and opportunities",
       "authors": [
-        "Olatunbosun Bartholomew Joseph",
-        "Nwankwo Charles Uzondu"
       ],
-      "year": "2024",
-      "journal": "Computer Science & IT Research Journal",
-      "doi": "https://doi.org/10.51594/csitrj.v5i8.1379",
-      "pdf_url": "https://fepbl.com/index.php/csitrj/article/download/1379/1615",
-      "citations": 6,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.51594/csitrj.v5i8.1379",
-      "relevance": 0.03,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Olatunbosun Bartholomew Joseph & Nwankwo Charles Uzondu (2024). Integrating AI and Machine Learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal. https://doi.org/https://doi.org/10.51594/csitrj.v5i8.1379"
     },
     {
-      "title": "The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis",
       "authors": [
-        "Hassan Abuhassna"
       ],
       "year": "2024",
-      "journal": "International Journal of Information and Education Technology",
-      "doi": "https://doi.org/10.18178/ijiet.2024.14.5.2095",
-      "pdf_url": "https://www.ijiet.org/vol14/IJIET-V14N5-2095.pdf",
-      "citations": 5,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.18178/ijiet.2024.14.5.2095",
-      "relevance": 0.025,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Hassan Abuhassna (2024). The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis. International Journal of Information and Education Technology. https://doi.org/https://doi.org/10.18178/ijiet.2024.14.5.2095"
     },
     {
-      "title": "Personalized learning in education: a machine learning and simulation approach",
       "authors": [
-        "Ross Taylor",
-        "Masoud Fakhimi",
-        "Athina Ioannou",
-        "Konstantina Spanaki"
       ],
-      "year": "2024",
-      "journal": "Benchmarking An International Journal",
-      "doi": "https://doi.org/10.1108/bij-06-2023-0380",
-      "pdf_url": "https://hal.science/hal-04667986v2/document",
-      "citations": 5,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1108/bij-06-2023-0380",
-      "relevance": 0.025,
       "abstract": "",
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       "file_path": "",
-      "apa": "Ross Taylor et al. (2024). Personalized learning in education: a machine learning and simulation approach. Benchmarking An International Journal. https://doi.org/https://doi.org/10.1108/bij-06-2023-0380"
     },
     {
-      "title": "An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm",
       "authors": [
-        "Deming Li"
       ],
-      "year": "2024",
-      "journal": "Computers in Human Behavior",
-      "doi": "https://doi.org/10.1016/j.chb.2024.108211",
       "pdf_url": null,
-      "citations": 4,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.chb.2024.108211",
-      "relevance": 0.02,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Deming Li (2024). An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm. Computers in Human Behavior. https://doi.org/https://doi.org/10.1016/j.chb.2024.108211"
     },
     {
-      "title": "Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model",
       "authors": [
-        "Amit Malik",
-        "Edeh Michael Onyema",
-        "Surjeet Dalal",
-        "Umesh Kumar Lilhore",
-        "Darpan Anand",
-        "Ashish Sharma",
-        "Sarita Simaiya"
       ],
       "year": "2023",
-      "journal": "Array",
-      "doi": "https://doi.org/10.1016/j.array.2023.100303",
-      "pdf_url": "https://doi.org/10.1016/j.array.2023.100303",
-      "citations": 4,
       "source": "Unknown",
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-      "url": "https://doi.org/10.1016/j.array.2023.100303",
-      "relevance": 0.02,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Amit Malik et al. (2023). Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model. Array. https://doi.org/https://doi.org/10.1016/j.array.2023.100303"
     },
     {
-      "title": "TinyML4D: Scaling Embedded Machine Learning Education in the Developing World",
       "authors": [
-        "Brian Plancher",
-        "Sebastian Büttrich",
-        "Jeremy Ellis",
-        "Neena Goveas",
-        "Laila D. Kazimierski",
-        "Jesus Lopez Sotelo",
-        "Milan Lukić",
-        "Diego Méndez",
-        "Rosdiadee Nordin",
-        "Andrés Oliva Trevisan"
       ],
       "year": "2024",
-      "journal": "Proceedings of the AAAI Symposium Series",
-      "doi": "https://doi.org/10.1609/aaaiss.v3i1.31265",
-      "pdf_url": "https://ojs.aaai.org/index.php/AAAI-SS/article/download/31265/33425",
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1609/aaaiss.v3i1.31265",
-      "relevance": 0.015,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Brian Plancher et al. (2024). TinyML4D: Scaling Embedded Machine Learning Education in the Developing World. Proceedings of the AAAI Symposium Series. https://doi.org/https://doi.org/10.1609/aaaiss.v3i1.31265"
     },
     {
-      "title": "Performance and early drop prediction for higher education students using machine learning",
       "authors": [
-        "Vasileios Christou",
-        "Ioannis G. Tsoulos",
-        "Vasileios Loupas",
-        "Alexandros T. Tzallas",
-        "Christos Gogos",
-        "Petros Karvelis",
-        "Nikolaos Antoniadis",
-        "Euripidis Glavas",
-        "Νικόλαος Γιαννακέας"
       ],
       "year": "2023",
-      "journal": "Expert Systems with Applications",
-      "doi": "https://doi.org/10.1016/j.eswa.2023.120079",
-      "pdf_url": null,
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.eswa.2023.120079",
-      "relevance": 0.015,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Vasileios Christou et al. (2023). Performance and early drop prediction for higher education students using machine learning. Expert Systems with Applications. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120079"
     },
     {
-      "title": "Predicting Student Retention in Higher Education Using Machine Learning",
       "authors": [
-        "Said A. Salloum",
-        "Azza Basiouni",
-        "Raghad Alfaisal",
-        "Ayham Salloum",
-        "Khaled Shaalan"
       ],
-      "year": "2024",
-      "journal": "Communications in computer and information science",
-      "doi": "https://doi.org/10.1007/978-3-031-65996-6_17",
-      "pdf_url": null,
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1007/978-3-031-65996-6_17",
-      "relevance": 0.015,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Said A. Salloum et al. (2024). Predicting Student Retention in Higher Education Using Machine Learning. Communications in computer and information science. https://doi.org/https://doi.org/10.1007/978-3-031-65996-6_17"
     },
     {
-      "title": "Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach",
       "authors": [
-        "William Villegas-Ch",
-        "Jaime Govea",
-        "Solange Revelo-Tapia"
       ],
-      "year": "2023",
-      "journal": "Sustainability",
-      "doi": "https://doi.org/10.3390/su151914512",
-      "pdf_url": "https://www.mdpi.com/2071-1050/15/19/14512/pdf?version=1696522635",
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.3390/su151914512",
-      "relevance": 0.015,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "William Villegas-Ch et al. (2023). Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach. Sustainability. https://doi.org/https://doi.org/10.3390/su151914512"
     },
     {
-      "title": "The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences",
       "authors": [
-        "Rudra Tiwari"
       ],
-      "year": "2023",
-      "journal": "INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT",
-      "doi": "https://doi.org/10.55041/ijsrem17645",
-      "pdf_url": "https://ijsrem.com/download/the-integration-of-ai-and-machine-learning-in-education-and-its-potential-to-personalize-and-improve-student-learning-experiences/?wpdmdl=13950&refresh=6659d6293e77e1717163561",
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.55041/ijsrem17645",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Rudra Tiwari (2023). The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. https://doi.org/https://doi.org/10.55041/ijsrem17645"
     },
     {
-      "title": "Secure Preschool Education Using Machine Learning and Metaverse Technologies",
       "authors": [
-        "Qifen Zhang"
       ],
       "year": "2023",
-      "journal": "Applied Artificial Intelligence",
-      "doi": "https://doi.org/10.1080/08839514.2023.2222496",
-      "pdf_url": "https://www.tandfonline.com/doi/pdf/10.1080/08839514.2023.2222496?needAccess=true&role=button",
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1080/08839514.2023.2222496",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Qifen Zhang (2023). Secure Preschool Education Using Machine Learning and Metaverse Technologies. Applied Artificial Intelligence. https://doi.org/https://doi.org/10.1080/08839514.2023.2222496"
     },
     {
-      "title": "Evolutionary machine learning builds smart education big data platform: Data-driven higher education",
       "authors": [
-        "Lu Zheng",
-        "Cong Wang",
-        "Xue Chen",
-        "Yihang Song",
-        "Zihan Meng",
-        "Ru Zhang"
       ],
-      "year": "2023",
-      "journal": "Applied Soft Computing",
-      "doi": "https://doi.org/10.1016/j.asoc.2023.110114",
-      "pdf_url": null,
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.asoc.2023.110114",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Lu Zheng et al. (2023). Evolutionary machine learning builds smart education big data platform: Data-driven higher education. Applied Soft Computing. https://doi.org/https://doi.org/10.1016/j.asoc.2023.110114"
     },
     {
-      "title": "SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE",
       "authors": [
-        "Ahmad Aburayya",
-        "Said A. Salloum",
-        "Khaled Younis Alderbashi",
-        "Fanar Shwedeh",
-        "Yara Shaalan",
-        "Raghad Alfaisal",
-        "Sawsan JM Malaka",
-        "Khaled Shaalan"
       ],
       "year": "2023",
-      "journal": "International Journal of Data and Network Science",
-      "doi": "https://doi.org/10.5267/j.ijdns.2023.3.005",
-      "pdf_url": "https://doi.org/10.5267/j.ijdns.2023.3.005",
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.5267/j.ijdns.2023.3.005",
-      "relevance": 0.01,
       "abstract": "",
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+        "Owen H.T. Lu"
       ],
+      "year": "2023",
+      "journal": "Smart Learning Environments",
+      "doi": "https://doi.org/10.1186/s40561-023-00260-y",
+      "pdf_url": "https://slejournal.springeropen.com/counter/pdf/10.1186/s40561-023-00260-y",
+      "citations": 52,
+      "source": "Unknown",
+      "quartile": "Q2",
+      "url": "https://doi.org/10.1186/s40561-023-00260-y",
+      "relevance": 0.26,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Chien-Chang Lin et al. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments. https://doi.org/https://doi.org/10.1186/s40561-023-00260-y"
     },
     {
+      "title": "The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research",
       "authors": [
+        "Tariq Alqahtani",
+        "Hisham A. Badreldin",
+        "Mohammed Alrashed",
+        "Abdulrahman Alshaya",
+        "Sahar S. Alghamdi",
+        "Khalid Bin Saleh",
+        "Shuroug A. Alowais",
+        "Omar A. Alshaya",
+        "Ishrat Rahman",
+        "Majed S. Al Yami"
       ],
+      "year": "2023",
+      "journal": "Research in Social and Administrative Pharmacy",
+      "doi": "https://doi.org/10.1016/j.sapharm.2023.05.016",
       "pdf_url": null,
+      "citations": 48,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.sapharm.2023.05.016",
+      "relevance": 0.24,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Tariq Alqahtani et al. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. https://doi.org/https://doi.org/10.1016/j.sapharm.2023.05.016"
     },
     {
+      "title": "Managing the Strategic Transformation of Higher Education through Artificial Intelligence",
       "authors": [
+        "Babu George",
+        "Ontario S. Wooden"
       ],
       "year": "2023",
+      "journal": "Administrative Sciences",
+      "doi": "https://doi.org/10.3390/admsci13090196",
+      "pdf_url": "https://www.mdpi.com/2076-3387/13/9/196/pdf?version=1693319334",
+      "citations": 45,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/admsci13090196",
+      "relevance": 0.225,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Babu George & Ontario S. Wooden (2023). Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences. https://doi.org/https://doi.org/10.3390/admsci13090196"
     },
     {
+      "title": "Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions",
       "authors": [
+        "Ammar Abulibdeh",
+        "Esmat Zaidan",
+        "Rawan Abulibdeh"
       ],
       "year": "2024",
+      "journal": "Journal of Cleaner Production",
+      "doi": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "pdf_url": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "citations": 45,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "relevance": 0.225,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Ammar Abulibdeh et al. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.140527"
     },
     {
+      "title": "Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities",
       "authors": [
+        "Jiahong Su",
+        "Davy Tsz Kit Ng",
+        "Samuel Kai Wah Chu"
       ],
       "year": "2023",
+      "journal": "Computers and Education Artificial Intelligence",
+      "doi": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "pdf_url": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "citations": 42,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "relevance": 0.21,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Jiahong Su et al. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education Artificial Intelligence. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100124"
     },
     {
+      "title": "Citizenship Challenges in Artificial Intelligence Education",
+      "abstract": "This chapter addresses the citizenship challenges related to AI in education, particularly concerning students, teachers, and other educational stakeholders in the context of AI integration. We first explore how to foster AI awareness and education, along with various strategies to promote a socio-critical approach to AI training, aiming to identify relevant and ethical uses to prioritise. In the second part, we discuss critical thinking and computational thinking skills that can be mobilised within certain AI-supported educational activities, depending on the degree of creative and transformative engagement those activities require.",
       "authors": [
+        "Margarida Romero"
       ],
+      "year": "2025",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2506.18955v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2506.18955v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Margarida Romero (2025). Citizenship Challenges in Artificial Intelligence Education. arXiv Preprint."
     },
     {
+      "title": "Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program",
+      "abstract": "The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following \"blue sky\" questions:   * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum?   * How could we teach AI topics at an early undergraduate or a secondary school level?   * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?   This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.",
       "authors": [
+        "Eric Eaton",
+        "Sven Koenig",
+        "Claudia Schulz",
+        "Francesco Maurelli",
+        "John Lee",
+        "Joshua Eckroth",
+        "Mark Crowley",
+        "Richard G. Freedman",
+        "Rogelio E. Cardona-Rivera",
+        "Tiago Machado",
+        "Tom Williams"
       ],
+      "year": "2017",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/1702.00137v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/1702.00137v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Eric Eaton et al. (2017). Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program. arXiv Preprint."
     },
     {
+      "title": "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates",
+      "abstract": "Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.",
       "authors": [
+        "David Johnson",
+        "Mohammad Alsharid",
+        "Rasheed El-Bouri",
+        "Nigel Mehdi",
+        "Farah Shamout",
+        "Alexandre Szenicer",
+        "David Toman",
+        "Saqr Binghalib"
       ],
+      "year": "2022",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2202.01281v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2202.01281v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "David Johnson et al. (2022). An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates. arXiv Preprint."
     },
     {
+      "title": "Use Scenarios & Practical Examples of AI Use in Education",
+      "abstract": "This report presents a set of use scenarios based on existing resources that teachers can use as inspiration to create their own, with the aim of introducing artificial intelligence (AI) at different pre-university levels, and with different goals. The Artificial Intelligence Education field (AIEd) is very active, with new resources and tools arising continuously. Those included in this document have already been tested with students and selected by experts in the field, but they must be taken just as practical examples to guide and inspire teachers creativity.",
       "authors": [
+        "Dara Cassidy",
+        "Yann-Aël Le Borgne",
+        "Francisco Bellas",
+        "Riina Vuorikari",
+        "Elise Rondin",
+        "Madhumalti Sharma",
+        "Jessica Niewint-Gori",
+        "Johanna Gröpler",
+        "Anne Gilleran",
+        "Lidija Kralj"
       ],
       "year": "2023",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2309.12320v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2309.12320v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Dara Cassidy et al. (2023). Use Scenarios & Practical Examples of AI Use in Education. arXiv Preprint."
     },
     {
+      "title": "Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments",
+      "abstract": "This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly.",
       "authors": [
+        "Sahan Bulathwela",
+        "Meghana Verma",
+        "Maria Perez-Ortiz",
+        "Emine Yilmaz",
+        "John Shawe-Taylor"
       ],
+      "year": "2022",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2207.01504v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2207.01504v1",
+      "relevance": 0.6,
+      "downloaded": false,
+      "file_path": "",
+      "apa": "Sahan Bulathwela et al. (2022). Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. arXiv Preprint."
+    },
+    {
+      "title": "Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform",
+      "abstract": "High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology & artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challenge 2025, a landmark initiative to develop accurate brain tumor segmentation algorithms. Fifty-six medical students & radiology trainees volunteered to annotate brain tumor MR images for the BraTS challenges of 2023 & 2024, guided by faculty-led didactics on neuropathology MRI. Among the 56 annotators, 14 select volunteers were then paired with neuroradiology faculty for guided one-on-one annotation sessions for BraTS 2025. Lectures on neuroanatomy, pathology & AI, journal clubs & data scientist-led workshops were organized online. Annotators & audience members completed surveys on their perceived knowledge before & after annotations & lectures respectively. Fourteen coordinators, each paired with a neuroradiologist, completed the data annotation process, averaging 1322.9+/-760.7 hours per dataset per pair and 1200 segmentations in total. On a scale of 1-10, annotation coordinators reported significant increase in familiarity with image segmentation software pre- and post-annotation, moving from initial average of 6+/-2.9 to final average of 8.9+/-1.1, and significant increase in familiarity with brain tumor features pre- and post-annotation, moving from initial average of 6.2+/-2.4 to final average of 8.1+/-1.2. We demonstrate an innovative offering for providing neuroradiology & AI education through an image segmentation challenge to enhance understanding of algorithm development, reinforce the concept of data reference standard, and diversify opportunities for AI-driven image analysis among future physicians.",
+      "authors": [
+        "Raisa Amiruddin",
+        "Nikolay Y. Yordanov",
+        "Nazanin Maleki",
+        "Pascal Fehringer",
+        "Athanasios Gkampenis",
+        "Anastasia Janas",
+        "Kiril Krantchev",
+        "Ahmed Moawad",
+        "Fabian Umeh",
+        "Salma Abosabie",
+        "Sara Abosabie",
+        "Albara Alotaibi",
+        "Mohamed Ghonim",
+        "Mohanad Ghonim",
+        "Sedra Abou Ali Mhana",
+        "Nathan Page",
+        "Marko Jakovljevic",
+        "Yasaman Sharifi",
+        "Prisha Bhatia",
+        "Amirreza Manteghinejad",
+        "Melisa Guelen",
+        "Michael Veronesi",
+        "Virginia Hill",
+        "Tiffany So",
+        "Mark Krycia",
+        "Bojan Petrovic",
+        "Fatima Memon",
+        "Justin Cramer",
+        "Elizabeth Schrickel",
+        "Vilma Kosovic",
+        "Lorenna Vidal",
+        "Gerard Thompson",
+        "Ichiro Ikuta",
+        "Basimah Albalooshy",
+        "Ali Nabavizadeh",
+        "Nourel Hoda Tahon",
+        "Karuna Shekdar",
+        "Aashim Bhatia",
+        "Claudia Kirsch",
+        "Gennaro D'Anna",
+        "Philipp Lohmann",
+        "Amal Saleh Nour",
+        "Andriy Myronenko",
+        "Adam Goldman-Yassen",
+        "Janet R. Reid",
+        "Sanjay Aneja",
+        "Spyridon Bakas",
+        "Mariam Aboian"
+      ],
+      "year": "2025",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2509.17281v1",
+      "citations": 0,
+      "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2509.17281v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Raisa Amiruddin et al. (2025). Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform. arXiv Preprint."
     },
     {
+      "title": "Artificial intelligence in education",
       "authors": [
+        "W. Holmes",
+        "Maya Bialik",
+        "Charles Fadel"
       ],
       "year": "2023",
+      "journal": "",
+      "doi": "https://doi.org/10.58863/20.500.12424/4276068",
+      "pdf_url": "https://repository.globethics.net/bitstream/20.500.12424/4276068/2/GE_Global_18_isbn9782889315239_ch42.pdf",
+      "citations": 39,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.58863/20.500.12424/4276068",
+      "relevance": 0.195,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "W. Holmes et al. (2023). Artificial intelligence in education. https://doi.org/https://doi.org/10.58863/20.500.12424/4276068"
     },
     {
+      "title": "Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education",
       "authors": [
+        "John V. Pavlik"
       ],
+      "year": "2023",
+      "journal": "Journalism & Mass Communication Educator",
+      "doi": "https://doi.org/10.1177/10776958221149577",
       "pdf_url": null,
+      "citations": 34,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1177/10776958221149577",
+      "relevance": 0.17,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "John V. Pavlik (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator. https://doi.org/https://doi.org/10.1177/10776958221149577"
     },
     {
+      "title": "Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice",
       "authors": [
+        "Tom Farrelly",
+        "Nick Baker"
       ],
+      "year": "2023",
+      "journal": "Education Sciences",
+      "doi": "https://doi.org/10.3390/educsci13111109",
+      "pdf_url": "https://www.mdpi.com/2227-7102/13/11/1109/pdf?version=1699079828",
+      "citations": 32,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/educsci13111109",
+      "relevance": 0.16,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Tom Farrelly & Nick Baker (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences. https://doi.org/https://doi.org/10.3390/educsci13111109"
     },
     {
+      "title": "The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers",
       "authors": [
+        "Günther Eysenbach"
       ],
       "year": "2023",
+      "journal": "JMIR Medical Education",
+      "doi": "https://doi.org/10.2196/46885",
+      "pdf_url": "https://mededu.jmir.org/2023/1/e46885/PDF",
+      "citations": 30,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.2196/46885",
+      "relevance": 0.15,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Günther Eysenbach (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR Medical Education. https://doi.org/https://doi.org/10.2196/46885"
     },
     {
+      "title": "Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix",
       "authors": [
+        "Lena Ivannova Ruiz-Rojas",
+        "Patricia Acosta-Vargas",
+        "Javier De-Moreta-Llovet",
+        "Mario González"
       ],
+      "year": "2023",
+      "journal": "Sustainability",
+      "doi": "https://doi.org/10.3390/su151511524",
+      "pdf_url": "https://www.mdpi.com/2071-1050/15/15/11524/pdf?version=1690354809",
+      "citations": 22,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/su151511524",
+      "relevance": 0.11,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Lena Ivannova Ruiz-Rojas et al. (2023). Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability. https://doi.org/https://doi.org/10.3390/su151511524"
     },
     {
+      "title": "Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education",
       "authors": [
+        "Miao Yue",
+        "Morris Siu–Yung Jong",
+        "Davy Tsz Kit Ng"
       ],
+      "year": "2024",
+      "journal": "Education and Information Technologies",
+      "doi": "https://doi.org/10.1007/s10639-024-12621-2",
+      "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10639-024-12621-2.pdf",
+      "citations": 21,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1007/s10639-024-12621-2",
+      "relevance": 0.105,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Miao Yue et al. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies. https://doi.org/https://doi.org/10.1007/s10639-024-12621-2"
     },
     {
+      "title": "Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers",
       "authors": [
+        "Faye Yu Ci Ng",
+        "Arun James Thirunavukarasu",
+        "Haoran Cheng",
+        "Ting Fang Tan",
+        "Laura Gutiérrez",
+        "Yanyan Lan",
+        "Jasmine Chiat Ling Ong",
+        "Yap Seng Chong",
+        "Kee Yuan Ngiam",
+        "Dean Ho"
       ],
+      "year": "2023",
+      "journal": "Cell Reports Medicine",
+      "doi": "https://doi.org/10.1016/j.xcrm.2023.101230",
+      "pdf_url": "http://www.cell.com/article/S266637912300407X/pdf",
+      "citations": 19,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1016/j.xcrm.2023.101230",
+      "relevance": 0.095,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Faye Yu Ci Ng et al. (2023). Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Reports Medicine. https://doi.org/https://doi.org/10.1016/j.xcrm.2023.101230"
     },
     {
+      "title": "Artificial Intelligence, Education, and Entrepreneurship",
       "authors": [
+        "MICHAEL GOFMAN",
+        "Zhao Jin"
       ],
+      "year": "2023",
+      "journal": "The Journal of Finance",
+      "doi": "https://doi.org/10.1111/jofi.13302",
+      "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jofi.13302",
+      "citations": 16,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1111/jofi.13302",
+      "relevance": 0.08,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "MICHAEL GOFMAN & Zhao Jin (2023). Artificial Intelligence, Education, and Entrepreneurship. The Journal of Finance. https://doi.org/https://doi.org/10.1111/jofi.13302"
     },
     {
+      "title": "A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education",
       "authors": [
+        "Michael R. King",
+        "chatGPT"
       ],
       "year": "2023",
+      "journal": "Cellular and Molecular Bioengineering",
+      "doi": "https://doi.org/10.1007/s12195-022-00754-8",
+      "pdf_url": "https://link.springer.com/content/pdf/10.1007/s12195-022-00754-8.pdf",
+      "citations": 16,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1007/s12195-022-00754-8",
+      "relevance": 0.08,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Michael R. King & chatGPT (2023). A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education. Cellular and Molecular Bioengineering. https://doi.org/https://doi.org/10.1007/s12195-022-00754-8"
     },
     {
+      "title": "Artificial intelligence in healthcare and education",
       "authors": [
+        "Manàs Dave",
+        "Neil Patel"
       ],
       "year": "2023",
+      "journal": "BDJ",
+      "doi": "https://doi.org/10.1038/s41415-023-5845-2",
+      "pdf_url": "https://www.nature.com/articles/s41415-023-5845-2.pdf",
+      "citations": 13,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1038/s41415-023-5845-2",
+      "relevance": 0.065,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Manàs Dave & Neil Patel (2023). Artificial intelligence in healthcare and education. BDJ. https://doi.org/https://doi.org/10.1038/s41415-023-5845-2"
     },
     {
+      "title": "An evidence-based approach to artificial intelligence education for medical students: A systematic review",
       "authors": [
+        "Nikola Pupic",
+        "Aryan Ghaffari-zadeh",
+        "Ricky Hu",
+        "Rohit Singla",
+        "Kathryn E. Darras",
+        "Anna Karwowska",
+        "Bruce B. Forster"
       ],
+      "year": "2023",
+      "journal": "PLOS Digital Health",
+      "doi": "https://doi.org/10.1371/journal.pdig.0000255",
+      "pdf_url": "https://doi.org/10.1371/journal.pdig.0000255",
+      "citations": 10,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1371/journal.pdig.0000255",
+      "relevance": 0.05,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Nikola Pupic et al. (2023). An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS Digital Health. https://doi.org/https://doi.org/10.1371/journal.pdig.0000255"
     },
     {
+      "title": "Implementing artificial intelligence education for middle school technology education in Republic of Korea",
       "authors": [
+        "Woongbin Park",
+        "Hyuksoo Kwon"
       ],
+      "year": "2023",
+      "journal": "International Journal of Technology and Design Education",
+      "doi": "https://doi.org/10.1007/s10798-023-09812-2",
+      "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10798-023-09812-2.pdf",
+      "citations": 8,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1007/s10798-023-09812-2",
+      "relevance": 0.04,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Woongbin Park & Hyuksoo Kwon (2023). Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education. https://doi.org/https://doi.org/10.1007/s10798-023-09812-2"
     },
     {
+      "title": "Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education",
       "authors": [
+        "Ismaila Temitayo Sanusi",
+        "Musa Adekunle Ayanwale",
+        "Thomas K. F. Chiu"
       ],
+      "year": "2023",
+      "journal": "Education and Information Technologies",
+      "doi": "https://doi.org/10.1007/s10639-023-12250-1",
+      "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10639-023-12250-1.pdf",
+      "citations": 8,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1007/s10639-023-12250-1",
+      "relevance": 0.04,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Ismaila Temitayo Sanusi et al. (2023). Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education. Education and Information Technologies. https://doi.org/https://doi.org/10.1007/s10639-023-12250-1"
     },
     {
+      "title": "Artificial intelligence education for young children: A case study of technology‐enhanced embodied learning",
       "authors": [
+        "Weipeng Yang",
+        "Xinyun Hu",
+        "Ibrahim H. Yeter",
+        "Jiahong Su",
+        "Yuqin Yang",
+        "John Chi‐Kin Lee"
       ],
+      "year": "2023",
+      "journal": "Journal of Computer Assisted Learning",
+      "doi": "https://doi.org/10.1111/jcal.12892",
+      "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcal.12892",
+      "citations": 7,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1111/jcal.12892",
+      "relevance": 0.035,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Weipeng Yang et al. (2023). Artificial intelligence education for young children: A case study of technology‐enhanced embodied learning. Journal of Computer Assisted Learning. https://doi.org/https://doi.org/10.1111/jcal.12892"
     }
   ],
   "meta": {
+    "title": "artificial intelligence education",
     "field": "Research",
     "year_from": 2023,
     "year_to": 2026

index.html CHANGED Viewed

@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html>
 <head>
-    <title>Research Results - machine learning education</title>
     <style>
         body { font-family: Arial, sans-serif; margin: 40px; background: #f5f5f5; }
         h1 { color: #1F3864; text-align: center; }
@@ -22,225 +22,231 @@
     <h1>🔍 Research Results</h1>
     <div class="stats">
         <h2>Summary</h2>
-        <p><strong>Title:</strong> machine learning education</p>
-        <p><strong>Generated:</strong> 2026-04-04 02:48:56</p>
         <p><strong>Field:</strong> Research</p>
-        <p><strong>Total Papers:</strong> 34</p>
         <p><strong>Downloaded:</strong> 0</p>
     </div>
     <h2>📊 Quartile Distribution</h2>
     <table>
         <tr><th>Quartile</th><th>Count</th></tr>
-        <tr><td>Q1 (Top 25%)</td><td>0</td></tr>
-        <tr><td>Q2 (25-50%)</td><td>0</td></tr>
-        <tr><td>Q3 (50-75%)</td><td>4</td></tr>
-        <tr><td>Q4 (Bottom 25%)</td><td>30</td></tr>
     </table>
-    <h2>📄 All Papers (34)</h2>
-    <div class="paper q3">
-        <strong>1. Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages</strong><br>
-        <small>Daniel J Wu, Andrew C Yang, Vinay U Prabhu | 2020 | Unknown | Q3</small>
     </div>
-    <div class="paper q3">
-        <strong>2. TinyTorch: Building Machine Learning Systems from First Principles</strong><br>
-        <small>Vijay Janapa Reddi | 2026 | Unknown | Q3</small>
     </div>
-    <div class="paper q3">
-        <strong>3. Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education</strong><br>
-        <small>Smitha Kumar, Michael A. Lones, Manuel Maarek | 2025 | Unknown | Q3</small>
     </div>
-    <div class="paper q3">
-        <strong>4. Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification</strong><br>
-        <small>Timothy L. Kline, Felipe Kitamura, Ian Pan | 2022 | Unknown | Q3</small>
     </div>
-    <div class="paper q4">
-        <strong>5. Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States</strong><br>
-        <small>Ninad Salastekar, Charles M. Maxfield, Tarek N. Hanna | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>6. Exploring the impact of ChatGPT on education: A web mining and machine learning approach</strong><br>
-        <small>Abderahman Rejeb, Karim Rejeb, Andrea Appolloni | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>7. Predicting student dropouts with machine learning: An empirical study in Finnish higher education</strong><br>
-        <small>Matti Vaarma, Hongxiu Li | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>8. Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques</strong><br>
-        <small>Waleed Alsabhan | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>9. Integrating AI and Machine Learning in STEM education: Challenges and opportunities</strong><br>
-        <small>Olatunbosun Bartholomew Joseph, Nwankwo Charles Uzondu | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>10. The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis</strong><br>
-        <small>Hassan Abuhassna | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>11. Personalized learning in education: a machine learning and simulation approach</strong><br>
-        <small>Ross Taylor, Masoud Fakhimi, Athina Ioannou | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>12. An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm</strong><br>
-        <small>Deming Li | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>13. Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model</strong><br>
-        <small>Amit Malik, Edeh Michael Onyema, Surjeet Dalal | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>14. TinyML4D: Scaling Embedded Machine Learning Education in the Developing World</strong><br>
-        <small>Brian Plancher, Sebastian Büttrich, Jeremy Ellis | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>15. Performance and early drop prediction for higher education students using machine learning</strong><br>
-        <small>Vasileios Christou, Ioannis G. Tsoulos, Vasileios Loupas | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>16. Predicting Student Retention in Higher Education Using Machine Learning</strong><br>
-        <small>Said A. Salloum, Azza Basiouni, Raghad Alfaisal | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>17. Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach</strong><br>
-        <small>William Villegas-Ch, Jaime Govea, Solange Revelo-Tapia | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>18. The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences</strong><br>
-        <small>Rudra Tiwari | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>19. Secure Preschool Education Using Machine Learning and Metaverse Technologies</strong><br>
-        <small>Qifen Zhang | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>20. Evolutionary machine learning builds smart education big data platform: Data-driven higher education</strong><br>
-        <small>Lu Zheng, Cong Wang, Xue Chen | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>21. SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE</strong><br>
-        <small>Ahmad Aburayya, Said A. Salloum, Khaled Younis Alderbashi | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>22. PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education</strong><br>
-        <small>Hong Gao, Yiyang Xie, Enkelejda Kasneci | 2025 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>23. Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education</strong><br>
-        <small>Kingsley Okoye, Julius T. Nganji, José Escamilla | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>24. A comprehensive overview of artificial intelligence and machine learning in education pedagogy: 21 Years (2000–2021) of research indexed in the scopus database</strong><br>
-        <small>Ekene Francis Okagbue, Ujunwa Perpetua Ezeachikulo, Tosin Yinka Akintunde | 2023 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>25. Generative AI &amp; machine learning in surgical education</strong><br>
-        <small>Diana A Hla, David Hindin | 2024 | Unknown | Q4</small>
     </div>
-    <div class="paper q4">
-        <strong>26. How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review</strong><br>
-        <small>Agostinho Sousa Pinto, António Abreu, Eusébio Costa | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>27. Unplugged Decision Tree Learning – A Learning Activity for Machine Learning Education in K-12</strong><br>
-        <small>Lukas Lehner, Martina Landman | 2024 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>28. Predicting Student Adaptability to Online Education Using Machine Learning</strong><br>
-        <small>Said A. Salloum, Ayham Salloum, Raghad Alfaisal | 2024 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>29. The Pedagogical Challenge of Machine Learning Education</strong><br>
-        <small>Orit Hazzan, Koby Mike | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>30. Early detection of students at risk of poor performance in Rwanda higher education using machine learning techniques</strong><br>
-        <small>Emmanuel Masabo, Joseph Nzabanita, Innocent Ngaruye | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>31. Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-package</strong><br>
-        <small>Zilong Zhong, Hui Guo, Kun Qian | 2024 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>32. AI and Machine Learning in Smart Education</strong><br>
-        <small>Pawan Kumar Goel, Amit Singhal, Shailendra Singh Bhadoria | 2024 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>33. Modeling with Primary Sources: An Approach to Teach Data Bias for Artificial Intelligence and Machine Learning Education</strong><br>
-        <small>Jeanne McClure, Juan Zheng, Franziska Bickel | 2024 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
-        <strong>34. Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education</strong><br>
-        <small>Wang Jun, Muhammad Shahid Iqbal, Rashid Abbasi | 2024 | Unknown | Q4</small>
     </div>

 <!DOCTYPE html>
 <html>
 <head>
+    <title>Research Results - artificial intelligence education</title>
     <style>
         body { font-family: Arial, sans-serif; margin: 40px; background: #f5f5f5; }
         h1 { color: #1F3864; text-align: center; }
     <h1>🔍 Research Results</h1>
     <div class="stats">
         <h2>Summary</h2>
+        <p><strong>Title:</strong> artificial intelligence education</p>
+        <p><strong>Generated:</strong> 2026-04-04 17:35:17</p>
         <p><strong>Field:</strong> Research</p>
+        <p><strong>Total Papers:</strong> 35</p>
         <p><strong>Downloaded:</strong> 0</p>
     </div>
     <h2>📊 Quartile Distribution</h2>
     <table>
         <tr><th>Quartile</th><th>Count</th></tr>
+        <tr><td>Q1 (Top 25%)</td><td>4</td></tr>
+        <tr><td>Q2 (25-50%)</td><td>7</td></tr>
+        <tr><td>Q3 (50-75%)</td><td>16</td></tr>
+        <tr><td>Q4 (Bottom 25%)</td><td>8</td></tr>
     </table>
+    <h2>📄 All Papers (35)</h2>
+    <div class="paper q1">
+        <strong>1. Artificial intelligence in higher education: the state of the field</strong><br>
+        <small>Helen Crompton, Diane Burke | 2023 | Unknown | Q1</small>
     </div>
+    <div class="paper q1">
+        <strong>2. Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education</strong><br>
+        <small>Yoshija Walter | 2024 | Unknown | Q1</small>
     </div>
+    <div class="paper q1">
+        <strong>3. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning</strong><br>
+        <small>David Baidoo-Anu, Leticia Owusu Ansah | 2023 | Unknown | Q1</small>
     </div>
+    <div class="paper q1">
+        <strong>4. Artificial intelligence in education: A systematic literature review</strong><br>
+        <small>Shan Wang, Fang Wang, Zhen Zhu | 2024 | Unknown | Q1</small>
     </div>
+    <div class="paper q2">
+        <strong>5. New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution</strong><br>
+        <small>Firuz Kamalov, David Santandreu Calonge, Ikhlaas Gurrib | 2023 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>6. A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour</strong><br>
+        <small>Melissa Bond, Hassan Khosravi, Maarten de Laat | 2024 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>7. Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence</strong><br>
+        <small>Grant Cooper | 2023 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>8. Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis</strong><br>
+        <small>Zied Bahroun, Chiraz Anane, Vian Ahmed | 2023 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>9. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity</strong><br>
+        <small>Christy Boscardin, Brian C. Gin, Polo Black Golde | 2023 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>10. Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education</strong><br>
+        <small>Ramteja Sajja, Yusuf Sermet, Muhammed Cikmaz | 2024 | Unknown | Q2</small>
     </div>
+    <div class="paper q2">
+        <strong>11. Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review</strong><br>
+        <small>Chien-Chang Lin, Anna Y.Q. Huang, Owen H.T. Lu | 2023 | Unknown | Q2</small>
     </div>
+    <div class="paper q3">
+        <strong>12. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research</strong><br>
+        <small>Tariq Alqahtani, Hisham A. Badreldin, Mohammed Alrashed | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>13. Managing the Strategic Transformation of Higher Education through Artificial Intelligence</strong><br>
+        <small>Babu George, Ontario S. Wooden | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>14. Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions</strong><br>
+        <small>Ammar Abulibdeh, Esmat Zaidan, Rawan Abulibdeh | 2024 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>15. Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities</strong><br>
+        <small>Jiahong Su, Davy Tsz Kit Ng, Samuel Kai Wah Chu | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>16. Citizenship Challenges in Artificial Intelligence Education</strong><br>
+        <small>Margarida Romero | 2025 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>17. Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program</strong><br>
+        <small>Eric Eaton, Sven Koenig, Claudia Schulz | 2017 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>18. An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates</strong><br>
+        <small>David Johnson, Mohammad Alsharid, Rasheed El-Bouri | 2022 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>19. Use Scenarios & Practical Examples of AI Use in Education</strong><br>
+        <small>Dara Cassidy, Yann-Aël Le Borgne, Francisco Bellas | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>20. Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments</strong><br>
+        <small>Sahan Bulathwela, Meghana Verma, Maria Perez-Ortiz | 2022 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>21. Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform</strong><br>
+        <small>Raisa Amiruddin, Nikolay Y. Yordanov, Nazanin Maleki | 2025 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>22. Artificial intelligence in education</strong><br>
+        <small>W. Holmes, Maya Bialik, Charles Fadel | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>23. Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education</strong><br>
+        <small>John V. Pavlik | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>24. Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice</strong><br>
+        <small>Tom Farrelly, Nick Baker | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>25. The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers</strong><br>
+        <small>Günther Eysenbach | 2023 | Unknown | Q3</small>
     </div>
+    <div class="paper q3">
+        <strong>26. Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix</strong><br>
+        <small>Lena Ivannova Ruiz-Rojas, Patricia Acosta-Vargas, Javier De-Moreta-Llovet | 2023 | Unknown | Q3</small>
+    </div>
+    <div class="paper q3">
+        <strong>27. Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education</strong><br>
+        <small>Miao Yue, Morris Siu–Yung Jong, Davy Tsz Kit Ng | 2024 | Unknown | Q3</small>
     </div>
     <div class="paper q4">
+        <strong>28. Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers</strong><br>
+        <small>Faye Yu Ci Ng, Arun James Thirunavukarasu, Haoran Cheng | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>29. Artificial Intelligence, Education, and Entrepreneurship</strong><br>
+        <small>MICHAEL GOFMAN, Zhao Jin | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>30. A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education</strong><br>
+        <small>Michael R. King, chatGPT | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>31. Artificial intelligence in healthcare and education</strong><br>
+        <small>Manàs Dave, Neil Patel | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>32. An evidence-based approach to artificial intelligence education for medical students: A systematic review</strong><br>
+        <small>Nikola Pupic, Aryan Ghaffari-zadeh, Ricky Hu | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>33. Implementing artificial intelligence education for middle school technology education in Republic of Korea</strong><br>
+        <small>Woongbin Park, Hyuksoo Kwon | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>34. Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education</strong><br>
+        <small>Ismaila Temitayo Sanusi, Musa Adekunle Ayanwale, Thomas K. F. Chiu | 2023 | Unknown | Q4</small>
     </div>
     <div class="paper q4">
+        <strong>35. Artificial intelligence education for young children: A case study of technology‐enhanced embodied learning</strong><br>
+        <small>Weipeng Yang, Xinyun Hu, Ibrahim H. Yeter | 2023 | Unknown | Q4</small>
     </div>

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@@ -1,771 +1,827 @@
 {
   "papers": [
     {
-      "title": "Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages",
-      "abstract": "We present Afro-MNIST, a set of synthetic MNIST-style datasets for four orthographies used in Afro-Asiatic and Niger-Congo languages: Ge`ez (Ethiopic), Vai, Osmanya, and N'Ko. These datasets serve as \"drop-in\" replacements for MNIST. We also describe and open-source a method for synthetic MNIST-style dataset generation from single examples of each digit. These datasets can be found at https://github.com/Daniel-Wu/AfroMNIST. We hope that MNIST-style datasets will be developed for other numeral systems, and that these datasets vitalize machine learning education in underrepresented nations in the research community.",
       "authors": [
-        "Daniel J Wu",
-        "Andrew C Yang",
-        "Vinay U Prabhu"
       ],
-      "year": "2020",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2009.13509v1",
-      "citations": 0,
-      "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2009.13509v1",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Daniel J Wu et al. (2020). Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages. arXiv Preprint."
     },
     {
-      "title": "TinyTorch: Building Machine Learning Systems from First Principles",
-      "abstract": "Machine learning education faces a fundamental gap: students learn algorithms without understanding the systems that execute them. They study gradient descent without measuring memory, attention mechanisms without analyzing O(N^2) scaling, optimizer theory without knowing why Adam requires 3x the memory of SGD. This \"algorithm-systems divide\" produces practitioners who can train models but cannot debug memory failures, optimize inference latency, or reason about deployment trade-offs--the very skills industry demands as \"ML systems engineering.\" We present TinyTorch, a 20-module curriculum that closes this gap through \"implementation-based systems pedagogy\": students construct PyTorch's core components (tensors, autograd, optimizers, CNNs, transformers) in pure Python, building a complete framework where every operation they invoke is code they wrote. The design employs three patterns: \"progressive disclosure\" of complexity, \"systems-first integration\" of profiling from the first module, and \"build-to-validate milestones\" recreating 67 years of ML breakthroughs--from Perceptron (1958) through Transformers (2017) to MLPerf-style benchmarking. Requiring only 4GB RAM and no GPU, TinyTorch demonstrates that deep ML systems understanding is achievable without specialized hardware. The curriculum is available open-source at mlsysbook.ai/tinytorch.",
       "authors": [
-        "Vijay Janapa Reddi"
       ],
-      "year": "2026",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2601.19107v2",
-      "citations": 0,
-      "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2601.19107v2",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Vijay Janapa Reddi (2026). TinyTorch: Building Machine Learning Systems from First Principles. arXiv Preprint."
     },
     {
-      "title": "Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education",
-      "abstract": "The rapid advancement of Large Language Models (LLMs) has opened new avenues in education. This study examines the use of LLMs in supporting learning in machine learning education; in particular, it focuses on the ability of LLMs to identify common errors of practice (pitfalls) in machine learning code, and their ability to provide feedback that can guide learning. Using a portfolio of code samples, we consider four different LLMs: one closed model and three open models. Whilst the most basic pitfalls are readily identified by all models, many common pitfalls are not. They particularly struggle to identify pitfalls in the early stages of the ML pipeline, especially those which can lead to information leaks, a major source of failure within applied ML projects. They also exhibit limited success at identifying pitfalls around model selection, which is a concept that students often struggle with when first transitioning from theory to practice. This questions the use of current LLMs to support machine learning education, and also raises important questions about their use by novice practitioners. Nevertheless, when LLMs successfully identify pitfalls in code, they do provide feedback that includes advice on how to proceed, emphasising their potential role in guiding learners. We also compare the capability of closed and open LLM models, and find that the gap is relatively small given the large difference in model sizes. This presents an opportunity to deploy, and potentially customise, smaller more efficient LLM models within education, avoiding risks around cost and data sharing associated with commercial models.",
       "authors": [
-        "Smitha Kumar",
-        "Michael A. Lones",
-        "Manuel Maarek",
-        "Hind Zantout"
       ],
-      "year": "2025",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2505.18220v1",
-      "citations": 0,
       "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2505.18220v1",
       "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Smitha Kumar et al. (2025). Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education. arXiv Preprint."
     },
     {
-      "title": "Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification",
-      "abstract": "With the recent advances in A.I. methodologies and their application to medical imaging, there has been an explosion of related research programs utilizing these techniques to produce state-of-the-art classification performance. Ultimately, these research programs culminate in submission of their work for consideration in peer reviewed journals. To date, the criteria for acceptance vs. rejection is often subjective; however, reproducible science requires reproducible review. The Machine Learning Education Sub-Committee of SIIM has identified a knowledge gap and a serious need to establish guidelines for reviewing these studies. Although there have been several recent papers with this goal, this present work is written from the machine learning practitioners standpoint. In this series, the committee will address the best practices to be followed in an A.I.-based study and present the required sections in terms of examples and discussion of what should be included to make the studies cohesive, reproducible, accurate, and self-contained. This first entry in the series focuses on the task of image classification. Elements such as dataset curation, data pre-processing steps, defining an appropriate reference standard, data partitioning, model architecture and training are discussed. The sections are presented as they would be detailed in a typical manuscript, with content describing the necessary information that should be included to make sure the study is of sufficient quality to be considered for publication. The goal of this series is to provide resources to not only help improve the review process for A.I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study. We hope to provide quantitative metrics in what otherwise may be a qualitative review process.",
       "authors": [
-        "Timothy L. Kline",
-        "Felipe Kitamura",
-        "Ian Pan",
-        "Amine M. Korchi",
-        "Neil Tenenholtz",
-        "Linda Moy",
-        "Judy Wawira Gichoya",
-        "Igor Santos",
-        "Steven Blumer",
-        "Misha Ysabel Hwang",
-        "Kim-Ann Git",
-        "Abishek Shroff",
-        "Elad Walach",
-        "George Shih",
-        "Steve Langer"
       ],
-      "year": "2022",
-      "journal": "arXiv Preprint",
-      "doi": "",
-      "pdf_url": "https://arxiv.org/pdf/2202.01863v1",
-      "citations": 0,
       "source": "Unknown",
-      "quartile": "Q3",
-      "url": "https://arxiv.org/pdf/2202.01863v1",
-      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
-      "apa": "Timothy L. Kline et al. (2022). Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification. arXiv Preprint."
     },
     {
-      "title": "Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States",
       "authors": [
-        "Ninad Salastekar",
-        "Charles M. Maxfield",
-        "Tarek N. Hanna",
-        "Elizabeth A. Krupinski",
-        "Darel E. Heitkamp",
-        "Lars J. Grimm"
       ],
       "year": "2023",
-      "journal": "Academic Radiology",
-      "doi": "https://doi.org/10.1016/j.acra.2023.01.005",
-      "pdf_url": null,
-      "citations": 10,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.acra.2023.01.005",
-      "relevance": 0.05,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Ninad Salastekar et al. (2023). Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States. Academic Radiology. https://doi.org/https://doi.org/10.1016/j.acra.2023.01.005"
     },
     {
-      "title": "Exploring the impact of ChatGPT on education: A web mining and machine learning approach",
       "authors": [
-        "Abderahman Rejeb",
-        "Karim Rejeb",
-        "Andrea Appolloni",
-        "Horst Treiblmaier",
-        "Mohammad Iranmanesh"
       ],
       "year": "2024",
-      "journal": "The International Journal of Management Education",
-      "doi": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "pdf_url": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "citations": 7,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.ijme.2024.100932",
-      "relevance": 0.035,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Abderahman Rejeb et al. (2024). Exploring the impact of ChatGPT on education: A web mining and machine learning approach. The International Journal of Management Education. https://doi.org/https://doi.org/10.1016/j.ijme.2024.100932"
     },
     {
-      "title": "Predicting student dropouts with machine learning: An empirical study in Finnish higher education",
       "authors": [
-        "Matti Vaarma",
-        "Hongxiu Li"
       ],
-      "year": "2024",
-      "journal": "Technology in Society",
-      "doi": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "pdf_url": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "citations": 7,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.techsoc.2024.102474",
-      "relevance": 0.035,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Matti Vaarma & Hongxiu Li (2024). Predicting student dropouts with machine learning: An empirical study in Finnish higher education. Technology in Society. https://doi.org/https://doi.org/10.1016/j.techsoc.2024.102474"
     },
     {
-      "title": "Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques",
       "authors": [
-        "Waleed Alsabhan"
       ],
       "year": "2023",
-      "journal": "Sensors",
-      "doi": "https://doi.org/10.3390/s23084149",
-      "pdf_url": "https://www.mdpi.com/1424-8220/23/8/4149/pdf?version=1682010179",
-      "citations": 6,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.3390/s23084149",
-      "relevance": 0.03,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Waleed Alsabhan (2023). Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques. Sensors. https://doi.org/https://doi.org/10.3390/s23084149"
     },
     {
-      "title": "Integrating AI and Machine Learning in STEM education: Challenges and opportunities",
       "authors": [
-        "Olatunbosun Bartholomew Joseph",
-        "Nwankwo Charles Uzondu"
       ],
-      "year": "2024",
-      "journal": "Computer Science & IT Research Journal",
-      "doi": "https://doi.org/10.51594/csitrj.v5i8.1379",
-      "pdf_url": "https://fepbl.com/index.php/csitrj/article/download/1379/1615",
-      "citations": 6,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.51594/csitrj.v5i8.1379",
-      "relevance": 0.03,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Olatunbosun Bartholomew Joseph & Nwankwo Charles Uzondu (2024). Integrating AI and Machine Learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal. https://doi.org/https://doi.org/10.51594/csitrj.v5i8.1379"
     },
     {
-      "title": "The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis",
       "authors": [
-        "Hassan Abuhassna"
       ],
       "year": "2024",
-      "journal": "International Journal of Information and Education Technology",
-      "doi": "https://doi.org/10.18178/ijiet.2024.14.5.2095",
-      "pdf_url": "https://www.ijiet.org/vol14/IJIET-V14N5-2095.pdf",
-      "citations": 5,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.18178/ijiet.2024.14.5.2095",
-      "relevance": 0.025,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Hassan Abuhassna (2024). The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis. International Journal of Information and Education Technology. https://doi.org/https://doi.org/10.18178/ijiet.2024.14.5.2095"
     },
     {
-      "title": "Personalized learning in education: a machine learning and simulation approach",
       "authors": [
-        "Ross Taylor",
-        "Masoud Fakhimi",
-        "Athina Ioannou",
-        "Konstantina Spanaki"
       ],
-      "year": "2024",
-      "journal": "Benchmarking An International Journal",
-      "doi": "https://doi.org/10.1108/bij-06-2023-0380",
-      "pdf_url": "https://hal.science/hal-04667986v2/document",
-      "citations": 5,
-      "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1108/bij-06-2023-0380",
-      "relevance": 0.025,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Ross Taylor et al. (2024). Personalized learning in education: a machine learning and simulation approach. Benchmarking An International Journal. https://doi.org/https://doi.org/10.1108/bij-06-2023-0380"
     },
     {
-      "title": "An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm",
       "authors": [
-        "Deming Li"
       ],
-      "year": "2024",
-      "journal": "Computers in Human Behavior",
-      "doi": "https://doi.org/10.1016/j.chb.2024.108211",
       "pdf_url": null,
-      "citations": 4,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.chb.2024.108211",
-      "relevance": 0.02,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Deming Li (2024). An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm. Computers in Human Behavior. https://doi.org/https://doi.org/10.1016/j.chb.2024.108211"
     },
     {
-      "title": "Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model",
       "authors": [
-        "Amit Malik",
-        "Edeh Michael Onyema",
-        "Surjeet Dalal",
-        "Umesh Kumar Lilhore",
-        "Darpan Anand",
-        "Ashish Sharma",
-        "Sarita Simaiya"
       ],
       "year": "2023",
-      "journal": "Array",
-      "doi": "https://doi.org/10.1016/j.array.2023.100303",
-      "pdf_url": "https://doi.org/10.1016/j.array.2023.100303",
-      "citations": 4,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.array.2023.100303",
-      "relevance": 0.02,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Amit Malik et al. (2023). Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model. Array. https://doi.org/https://doi.org/10.1016/j.array.2023.100303"
     },
     {
-      "title": "TinyML4D: Scaling Embedded Machine Learning Education in the Developing World",
       "authors": [
-        "Brian Plancher",
-        "Sebastian Büttrich",
-        "Jeremy Ellis",
-        "Neena Goveas",
-        "Laila D. Kazimierski",
-        "Jesus Lopez Sotelo",
-        "Milan Lukić",
-        "Diego Méndez",
-        "Rosdiadee Nordin",
-        "Andrés Oliva Trevisan"
       ],
       "year": "2024",
-      "journal": "Proceedings of the AAAI Symposium Series",
-      "doi": "https://doi.org/10.1609/aaaiss.v3i1.31265",
-      "pdf_url": "https://ojs.aaai.org/index.php/AAAI-SS/article/download/31265/33425",
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1609/aaaiss.v3i1.31265",
-      "relevance": 0.015,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Brian Plancher et al. (2024). TinyML4D: Scaling Embedded Machine Learning Education in the Developing World. Proceedings of the AAAI Symposium Series. https://doi.org/https://doi.org/10.1609/aaaiss.v3i1.31265"
     },
     {
-      "title": "Performance and early drop prediction for higher education students using machine learning",
       "authors": [
-        "Vasileios Christou",
-        "Ioannis G. Tsoulos",
-        "Vasileios Loupas",
-        "Alexandros T. Tzallas",
-        "Christos Gogos",
-        "Petros Karvelis",
-        "Nikolaos Antoniadis",
-        "Euripidis Glavas",
-        "Νικόλαος Γιαννακέας"
       ],
       "year": "2023",
-      "journal": "Expert Systems with Applications",
-      "doi": "https://doi.org/10.1016/j.eswa.2023.120079",
-      "pdf_url": null,
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.eswa.2023.120079",
-      "relevance": 0.015,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Vasileios Christou et al. (2023). Performance and early drop prediction for higher education students using machine learning. Expert Systems with Applications. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120079"
     },
     {
-      "title": "Predicting Student Retention in Higher Education Using Machine Learning",
       "authors": [
-        "Said A. Salloum",
-        "Azza Basiouni",
-        "Raghad Alfaisal",
-        "Ayham Salloum",
-        "Khaled Shaalan"
       ],
-      "year": "2024",
-      "journal": "Communications in computer and information science",
-      "doi": "https://doi.org/10.1007/978-3-031-65996-6_17",
-      "pdf_url": null,
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1007/978-3-031-65996-6_17",
-      "relevance": 0.015,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Said A. Salloum et al. (2024). Predicting Student Retention in Higher Education Using Machine Learning. Communications in computer and information science. https://doi.org/https://doi.org/10.1007/978-3-031-65996-6_17"
     },
     {
-      "title": "Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach",
       "authors": [
-        "William Villegas-Ch",
-        "Jaime Govea",
-        "Solange Revelo-Tapia"
       ],
-      "year": "2023",
-      "journal": "Sustainability",
-      "doi": "https://doi.org/10.3390/su151914512",
-      "pdf_url": "https://www.mdpi.com/2071-1050/15/19/14512/pdf?version=1696522635",
-      "citations": 3,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.3390/su151914512",
-      "relevance": 0.015,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "William Villegas-Ch et al. (2023). Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach. Sustainability. https://doi.org/https://doi.org/10.3390/su151914512"
     },
     {
-      "title": "The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences",
       "authors": [
-        "Rudra Tiwari"
       ],
-      "year": "2023",
-      "journal": "INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT",
-      "doi": "https://doi.org/10.55041/ijsrem17645",
-      "pdf_url": "https://ijsrem.com/download/the-integration-of-ai-and-machine-learning-in-education-and-its-potential-to-personalize-and-improve-student-learning-experiences/?wpdmdl=13950&refresh=6659d6293e77e1717163561",
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.55041/ijsrem17645",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Rudra Tiwari (2023). The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. https://doi.org/https://doi.org/10.55041/ijsrem17645"
     },
     {
-      "title": "Secure Preschool Education Using Machine Learning and Metaverse Technologies",
       "authors": [
-        "Qifen Zhang"
       ],
       "year": "2023",
-      "journal": "Applied Artificial Intelligence",
-      "doi": "https://doi.org/10.1080/08839514.2023.2222496",
-      "pdf_url": "https://www.tandfonline.com/doi/pdf/10.1080/08839514.2023.2222496?needAccess=true&role=button",
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1080/08839514.2023.2222496",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Qifen Zhang (2023). Secure Preschool Education Using Machine Learning and Metaverse Technologies. Applied Artificial Intelligence. https://doi.org/https://doi.org/10.1080/08839514.2023.2222496"
     },
     {
-      "title": "Evolutionary machine learning builds smart education big data platform: Data-driven higher education",
       "authors": [
-        "Lu Zheng",
-        "Cong Wang",
-        "Xue Chen",
-        "Yihang Song",
-        "Zihan Meng",
-        "Ru Zhang"
       ],
-      "year": "2023",
-      "journal": "Applied Soft Computing",
-      "doi": "https://doi.org/10.1016/j.asoc.2023.110114",
-      "pdf_url": null,
-      "citations": 2,
       "source": "Unknown",
-      "quartile": "Q4",
-      "url": "https://doi.org/10.1016/j.asoc.2023.110114",
-      "relevance": 0.01,
-      "abstract": "",
       "downloaded": false,
       "file_path": "",
-      "apa": "Lu Zheng et al. (2023). Evolutionary machine learning builds smart education big data platform: Data-driven higher education. Applied Soft Computing. https://doi.org/https://doi.org/10.1016/j.asoc.2023.110114"
     },
     {
-      "title": "SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE",
       "authors": [
-        "Ahmad Aburayya",
-        "Said A. Salloum",
-        "Khaled Younis Alderbashi",
-        "Fanar Shwedeh",
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-        "Raghad Alfaisal",
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+      "citations": 55,
+      "source": "Unknown",
+      "quartile": "Q2",
+      "url": "https://doi.org/10.3390/info15100596",
+      "relevance": 0.275,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Ramteja Sajja et al. (2024). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. Information. https://doi.org/https://doi.org/10.3390/info15100596"
     },
     {
+      "title": "Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review",
       "authors": [
+        "Chien-Chang Lin",
+        "Anna Y.Q. Huang",
+        "Owen H.T. Lu"
       ],
+      "year": "2023",
+      "journal": "Smart Learning Environments",
+      "doi": "https://doi.org/10.1186/s40561-023-00260-y",
+      "pdf_url": "https://slejournal.springeropen.com/counter/pdf/10.1186/s40561-023-00260-y",
+      "citations": 52,
+      "source": "Unknown",
+      "quartile": "Q2",
+      "url": "https://doi.org/10.1186/s40561-023-00260-y",
+      "relevance": 0.26,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Chien-Chang Lin et al. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments. https://doi.org/https://doi.org/10.1186/s40561-023-00260-y"
     },
     {
+      "title": "The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research",
       "authors": [
+        "Tariq Alqahtani",
+        "Hisham A. Badreldin",
+        "Mohammed Alrashed",
+        "Abdulrahman Alshaya",
+        "Sahar S. Alghamdi",
+        "Khalid Bin Saleh",
+        "Shuroug A. Alowais",
+        "Omar A. Alshaya",
+        "Ishrat Rahman",
+        "Majed S. Al Yami"
       ],
+      "year": "2023",
+      "journal": "Research in Social and Administrative Pharmacy",
+      "doi": "https://doi.org/10.1016/j.sapharm.2023.05.016",
       "pdf_url": null,
+      "citations": 48,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.sapharm.2023.05.016",
+      "relevance": 0.24,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Tariq Alqahtani et al. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. https://doi.org/https://doi.org/10.1016/j.sapharm.2023.05.016"
     },
     {
+      "title": "Managing the Strategic Transformation of Higher Education through Artificial Intelligence",
       "authors": [
+        "Babu George",
+        "Ontario S. Wooden"
       ],
       "year": "2023",
+      "journal": "Administrative Sciences",
+      "doi": "https://doi.org/10.3390/admsci13090196",
+      "pdf_url": "https://www.mdpi.com/2076-3387/13/9/196/pdf?version=1693319334",
+      "citations": 45,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/admsci13090196",
+      "relevance": 0.225,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Babu George & Ontario S. Wooden (2023). Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences. https://doi.org/https://doi.org/10.3390/admsci13090196"
     },
     {
+      "title": "Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions",
       "authors": [
+        "Ammar Abulibdeh",
+        "Esmat Zaidan",
+        "Rawan Abulibdeh"
       ],
       "year": "2024",
+      "journal": "Journal of Cleaner Production",
+      "doi": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "pdf_url": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "citations": 45,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.jclepro.2023.140527",
+      "relevance": 0.225,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Ammar Abulibdeh et al. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.140527"
     },
     {
+      "title": "Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities",
       "authors": [
+        "Jiahong Su",
+        "Davy Tsz Kit Ng",
+        "Samuel Kai Wah Chu"
       ],
       "year": "2023",
+      "journal": "Computers and Education Artificial Intelligence",
+      "doi": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "pdf_url": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "citations": 42,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1016/j.caeai.2023.100124",
+      "relevance": 0.21,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Jiahong Su et al. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education Artificial Intelligence. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100124"
     },
     {
+      "title": "Citizenship Challenges in Artificial Intelligence Education",
+      "abstract": "This chapter addresses the citizenship challenges related to AI in education, particularly concerning students, teachers, and other educational stakeholders in the context of AI integration. We first explore how to foster AI awareness and education, along with various strategies to promote a socio-critical approach to AI training, aiming to identify relevant and ethical uses to prioritise. In the second part, we discuss critical thinking and computational thinking skills that can be mobilised within certain AI-supported educational activities, depending on the degree of creative and transformative engagement those activities require.",
       "authors": [
+        "Margarida Romero"
       ],
+      "year": "2025",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2506.18955v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2506.18955v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Margarida Romero (2025). Citizenship Challenges in Artificial Intelligence Education. arXiv Preprint."
     },
     {
+      "title": "Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program",
+      "abstract": "The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following \"blue sky\" questions:   * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum?   * How could we teach AI topics at an early undergraduate or a secondary school level?   * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?   This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.",
       "authors": [
+        "Eric Eaton",
+        "Sven Koenig",
+        "Claudia Schulz",
+        "Francesco Maurelli",
+        "John Lee",
+        "Joshua Eckroth",
+        "Mark Crowley",
+        "Richard G. Freedman",
+        "Rogelio E. Cardona-Rivera",
+        "Tiago Machado",
+        "Tom Williams"
       ],
+      "year": "2017",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/1702.00137v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/1702.00137v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Eric Eaton et al. (2017). Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program. arXiv Preprint."
     },
     {
+      "title": "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates",
+      "abstract": "Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.",
       "authors": [
+        "David Johnson",
+        "Mohammad Alsharid",
+        "Rasheed El-Bouri",
+        "Nigel Mehdi",
+        "Farah Shamout",
+        "Alexandre Szenicer",
+        "David Toman",
+        "Saqr Binghalib"
       ],
+      "year": "2022",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2202.01281v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2202.01281v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "David Johnson et al. (2022). An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates. arXiv Preprint."
     },
     {
+      "title": "Use Scenarios & Practical Examples of AI Use in Education",
+      "abstract": "This report presents a set of use scenarios based on existing resources that teachers can use as inspiration to create their own, with the aim of introducing artificial intelligence (AI) at different pre-university levels, and with different goals. The Artificial Intelligence Education field (AIEd) is very active, with new resources and tools arising continuously. Those included in this document have already been tested with students and selected by experts in the field, but they must be taken just as practical examples to guide and inspire teachers creativity.",
       "authors": [
+        "Dara Cassidy",
+        "Yann-Aël Le Borgne",
+        "Francisco Bellas",
+        "Riina Vuorikari",
+        "Elise Rondin",
+        "Madhumalti Sharma",
+        "Jessica Niewint-Gori",
+        "Johanna Gröpler",
+        "Anne Gilleran",
+        "Lidija Kralj"
       ],
       "year": "2023",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2309.12320v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2309.12320v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Dara Cassidy et al. (2023). Use Scenarios & Practical Examples of AI Use in Education. arXiv Preprint."
     },
     {
+      "title": "Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments",
+      "abstract": "This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly.",
       "authors": [
+        "Sahan Bulathwela",
+        "Meghana Verma",
+        "Maria Perez-Ortiz",
+        "Emine Yilmaz",
+        "John Shawe-Taylor"
       ],
+      "year": "2022",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2207.01504v1",
+      "citations": 0,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2207.01504v1",
+      "relevance": 0.6,
+      "downloaded": false,
+      "file_path": "",
+      "apa": "Sahan Bulathwela et al. (2022). Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. arXiv Preprint."
+    },
+    {
+      "title": "Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform",
+      "abstract": "High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology & artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challenge 2025, a landmark initiative to develop accurate brain tumor segmentation algorithms. Fifty-six medical students & radiology trainees volunteered to annotate brain tumor MR images for the BraTS challenges of 2023 & 2024, guided by faculty-led didactics on neuropathology MRI. Among the 56 annotators, 14 select volunteers were then paired with neuroradiology faculty for guided one-on-one annotation sessions for BraTS 2025. Lectures on neuroanatomy, pathology & AI, journal clubs & data scientist-led workshops were organized online. Annotators & audience members completed surveys on their perceived knowledge before & after annotations & lectures respectively. Fourteen coordinators, each paired with a neuroradiologist, completed the data annotation process, averaging 1322.9+/-760.7 hours per dataset per pair and 1200 segmentations in total. On a scale of 1-10, annotation coordinators reported significant increase in familiarity with image segmentation software pre- and post-annotation, moving from initial average of 6+/-2.9 to final average of 8.9+/-1.1, and significant increase in familiarity with brain tumor features pre- and post-annotation, moving from initial average of 6.2+/-2.4 to final average of 8.1+/-1.2. We demonstrate an innovative offering for providing neuroradiology & AI education through an image segmentation challenge to enhance understanding of algorithm development, reinforce the concept of data reference standard, and diversify opportunities for AI-driven image analysis among future physicians.",
+      "authors": [
+        "Raisa Amiruddin",
+        "Nikolay Y. Yordanov",
+        "Nazanin Maleki",
+        "Pascal Fehringer",
+        "Athanasios Gkampenis",
+        "Anastasia Janas",
+        "Kiril Krantchev",
+        "Ahmed Moawad",
+        "Fabian Umeh",
+        "Salma Abosabie",
+        "Sara Abosabie",
+        "Albara Alotaibi",
+        "Mohamed Ghonim",
+        "Mohanad Ghonim",
+        "Sedra Abou Ali Mhana",
+        "Nathan Page",
+        "Marko Jakovljevic",
+        "Yasaman Sharifi",
+        "Prisha Bhatia",
+        "Amirreza Manteghinejad",
+        "Melisa Guelen",
+        "Michael Veronesi",
+        "Virginia Hill",
+        "Tiffany So",
+        "Mark Krycia",
+        "Bojan Petrovic",
+        "Fatima Memon",
+        "Justin Cramer",
+        "Elizabeth Schrickel",
+        "Vilma Kosovic",
+        "Lorenna Vidal",
+        "Gerard Thompson",
+        "Ichiro Ikuta",
+        "Basimah Albalooshy",
+        "Ali Nabavizadeh",
+        "Nourel Hoda Tahon",
+        "Karuna Shekdar",
+        "Aashim Bhatia",
+        "Claudia Kirsch",
+        "Gennaro D'Anna",
+        "Philipp Lohmann",
+        "Amal Saleh Nour",
+        "Andriy Myronenko",
+        "Adam Goldman-Yassen",
+        "Janet R. Reid",
+        "Sanjay Aneja",
+        "Spyridon Bakas",
+        "Mariam Aboian"
+      ],
+      "year": "2025",
+      "journal": "arXiv Preprint",
+      "doi": "",
+      "pdf_url": "https://arxiv.org/pdf/2509.17281v1",
+      "citations": 0,
+      "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://arxiv.org/pdf/2509.17281v1",
+      "relevance": 0.6,
       "downloaded": false,
       "file_path": "",
+      "apa": "Raisa Amiruddin et al. (2025). Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform. arXiv Preprint."
     },
     {
+      "title": "Artificial intelligence in education",
       "authors": [
+        "W. Holmes",
+        "Maya Bialik",
+        "Charles Fadel"
       ],
       "year": "2023",
+      "journal": "",
+      "doi": "https://doi.org/10.58863/20.500.12424/4276068",
+      "pdf_url": "https://repository.globethics.net/bitstream/20.500.12424/4276068/2/GE_Global_18_isbn9782889315239_ch42.pdf",
+      "citations": 39,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.58863/20.500.12424/4276068",
+      "relevance": 0.195,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "W. Holmes et al. (2023). Artificial intelligence in education. https://doi.org/https://doi.org/10.58863/20.500.12424/4276068"
     },
     {
+      "title": "Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education",
       "authors": [
+        "John V. Pavlik"
       ],
+      "year": "2023",
+      "journal": "Journalism & Mass Communication Educator",
+      "doi": "https://doi.org/10.1177/10776958221149577",
       "pdf_url": null,
+      "citations": 34,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1177/10776958221149577",
+      "relevance": 0.17,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "John V. Pavlik (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator. https://doi.org/https://doi.org/10.1177/10776958221149577"
     },
     {
+      "title": "Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice",
       "authors": [
+        "Tom Farrelly",
+        "Nick Baker"
       ],
+      "year": "2023",
+      "journal": "Education Sciences",
+      "doi": "https://doi.org/10.3390/educsci13111109",
+      "pdf_url": "https://www.mdpi.com/2227-7102/13/11/1109/pdf?version=1699079828",
+      "citations": 32,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/educsci13111109",
+      "relevance": 0.16,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Tom Farrelly & Nick Baker (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences. https://doi.org/https://doi.org/10.3390/educsci13111109"
     },
     {
+      "title": "The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers",
       "authors": [
+        "Günther Eysenbach"
       ],
       "year": "2023",
+      "journal": "JMIR Medical Education",
+      "doi": "https://doi.org/10.2196/46885",
+      "pdf_url": "https://mededu.jmir.org/2023/1/e46885/PDF",
+      "citations": 30,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.2196/46885",
+      "relevance": 0.15,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Günther Eysenbach (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR Medical Education. https://doi.org/https://doi.org/10.2196/46885"
     },
     {
+      "title": "Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix",
       "authors": [
+        "Lena Ivannova Ruiz-Rojas",
+        "Patricia Acosta-Vargas",
+        "Javier De-Moreta-Llovet",
+        "Mario González"
       ],
+      "year": "2023",
+      "journal": "Sustainability",
+      "doi": "https://doi.org/10.3390/su151511524",
+      "pdf_url": "https://www.mdpi.com/2071-1050/15/15/11524/pdf?version=1690354809",
+      "citations": 22,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.3390/su151511524",
+      "relevance": 0.11,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Lena Ivannova Ruiz-Rojas et al. (2023). Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability. https://doi.org/https://doi.org/10.3390/su151511524"
     },
     {
+      "title": "Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education",
       "authors": [
+        "Miao Yue",
+        "Morris Siu–Yung Jong",
+        "Davy Tsz Kit Ng"
       ],
+      "year": "2024",
+      "journal": "Education and Information Technologies",
+      "doi": "https://doi.org/10.1007/s10639-024-12621-2",
+      "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10639-024-12621-2.pdf",
+      "citations": 21,
       "source": "Unknown",
+      "quartile": "Q3",
+      "url": "https://doi.org/10.1007/s10639-024-12621-2",
+      "relevance": 0.105,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Miao Yue et al. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies. https://doi.org/https://doi.org/10.1007/s10639-024-12621-2"
     },
     {
+      "title": "Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers",
       "authors": [
+        "Faye Yu Ci Ng",
+        "Arun James Thirunavukarasu",
+        "Haoran Cheng",
+        "Ting Fang Tan",
+        "Laura Gutiérrez",
+        "Yanyan Lan",
+        "Jasmine Chiat Ling Ong",
+        "Yap Seng Chong",
+        "Kee Yuan Ngiam",
+        "Dean Ho"
       ],
+      "year": "2023",
+      "journal": "Cell Reports Medicine",
+      "doi": "https://doi.org/10.1016/j.xcrm.2023.101230",
+      "pdf_url": "http://www.cell.com/article/S266637912300407X/pdf",
+      "citations": 19,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1016/j.xcrm.2023.101230",
+      "relevance": 0.095,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "Faye Yu Ci Ng et al. (2023). Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Reports Medicine. https://doi.org/https://doi.org/10.1016/j.xcrm.2023.101230"
     },
     {
+      "title": "Artificial Intelligence, Education, and Entrepreneurship",
       "authors": [
+        "MICHAEL GOFMAN",
+        "Zhao Jin"
       ],
+      "year": "2023",
+      "journal": "The Journal of Finance",
+      "doi": "https://doi.org/10.1111/jofi.13302",
+      "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jofi.13302",
+      "citations": 16,
       "source": "Unknown",
       "quartile": "Q4",
+      "url": "https://doi.org/10.1111/jofi.13302",
+      "relevance": 0.08,
       "abstract": "",
       "downloaded": false,
       "file_path": "",
+      "apa": "MICHAEL GOFMAN & Zhao Jin (2023). Artificial Intelligence, Education, and Entrepreneurship. The Journal of Finance. https://doi.org/https://doi.org/10.1111/jofi.13302"
     },
     {
+      "title": "A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education",
       "authors": [
+        "Michael R. King",
+        "chatGPT"
       ],
       "year": "2023",
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       "authors": [
+        "Weipeng Yang",
+        "Xinyun Hu",
+        "Ibrahim H. Yeter",
+        "Jiahong Su",
+        "Yuqin Yang",
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   ],
   "meta": {
+    "title": "artificial intelligence education",
     "field": "Research",
     "year_from": 2023,
     "year_to": 2026

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