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report_data.json

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+{
+  "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",
+        "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",
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