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metadata
title: Schema Study
emoji: 📚
colorFrom: green
colorTo: gray
sdk: streamlit
sdk_version: 1.52.2
app_file: app.py
pinned: true
license: gpl-3.0
short_description: AI-enhanced study app for students

Schema Study: An AI-Enhanced Study App for Biology Students

Schema Study is a free, no-code, open-source web application that turns a spreadsheet of course terms into an AI-powered study coach. Designed for asynchronous student learning and inquiry, Schema Study helps biology students master core course concepts through evidence-based AI-powered conversations. The app leverages OpenAI's latest GPT models via the Responses API to provide instant formative feedback, Socratic questioning, and personalized study support.

Key Pedagogical Approach: Schema Study uses a Socratic questioning method that (1) withholds direct solutions while providing brief, targeted feedback, (2) poses exactly one scenario-grounded follow-up question per turn, and (3) presses for mechanistic reasoning, justification, and connections between concepts. The tool provides formative practice through question-led dialogue.

Features

  • No-Code Setup: Upload course terms via a single CSV spreadsheet - no programming required
  • Password Protection: Secure access for your class or group
  • Customizable Terms: Use your own CSV file of terms and definitions
  • Evidence-Based Pedagogy: Implements Socratic questioning and formative feedback strategies
  • Prompt Templates: Five customizable conversation starters (Misconception Check, Two Truths & a Lie, Connect Terms, Schema Map, Create Study Plan)
  • AI-Enhanced Feedback: Get instant, formative feedback and guidance using GPT-5.2 (default), GPT-5.1, or GPT-4.1
  • Web Search Support: Optional web search functionality for current information and citations (configurable in config.py)
  • Real-Time Streaming: Live token-by-token response streaming with visual typing indicator
  • Professional, Accessible UI: Clean, modern design with a color palette for clarity and focus
  • Open-Source & Free: Fully open-source under GNU GPL-3 License, no paywalls or proprietary services required

Evaluation Versions

Before creating your own copy, explore the evaluation versions:

Note: These are evaluation versions for testing. You'll need your OpenAI API key to interact with the chatbot on these evaluation versions. The production version can be found at https://huggingface.co/spaces/keefereuther/Schema_Study.

How to Use (Students)

  1. Access the App: Go to your Hugging Face Space URL. Enter the password provided by your instructor.
  2. Select a Term: Use the dropdown to pick a course term.
  3. Start Studying: Respond to the prompt or use a template button to begin your session. Ask questions, answer prompts, and explore the term in depth.

How to Use (Instructors)

Prerequisites

Before starting, you'll need:

  • A web browser (Chrome, Firefox, or Safari)
  • An email address
  • A credit card to purchase $5-10 of credits for OpenAI API usage
  • Your course syllabus or a list of course learning objectives
  • About 1-2 hours to create your customized copy of Schema Study

Setup Instructions

For comprehensive setup instructions, including account creation, terms CSV file creation, Hugging Face Space configuration, customization options, and sharing/embedding, please see the Schema Study User Manual included in this repository.

The user manual provides step-by-step guidance for:

  • Setting up Hugging Face and OpenAI accounts
  • Creating and customizing your terms CSV file
  • Configuring your Hugging Face Space
  • Customizing all app settings through config.py
  • Sharing and embedding Schema Study in your LMS
  • Model selection and pricing information

Configuration

AI Model Settings (config.py)

  • Default Model: GPT-5.2 with reasoning="none" for faster responses
  • Alternative Models: GPT-5.1 (reasoning model) or GPT-4.1 (non-reasoning model with temperature control)
  • Web Search: Configurable via enable_web_search (default: True)
  • Reasoning Effort: Configurable for GPT-5.2 and GPT-5.1 (options: "none", "minimal", "low", "medium")
  • Temperature: Configurable for GPT-4.1 (0.0-2.0)

Pacing and Question Control

Schema Study enforces structured pacing to prevent cognitive overload. The system prompt enforces one focused question plus one short, realistic application example per turn to reduce confusion from multiple simultaneous follow-ups.

Universal Design for Learning (UDL) Adjustments:

  • For additional scaffolding: Modify term_prompt to allow brief clarifying questions
  • For advanced students: Add instructions to keep responses focused and move efficiently toward connections

The five prompt templates provide structured entry points for students who need additional support initiating conversations.

Other Settings

  • All settings are in config.py (title, instructions, prompt templates, resources, AI model parameters, etc.)
  • Theming is managed via .streamlit/config.toml and custom CSS in app.py
  • Dependencies are listed in requirements.txt

Technical Details

API & Models

  • API Framework: OpenAI Responses API (streaming-enabled)
  • Supported Models: GPT-5.2 (default), GPT-5.1, GPT-4.1
  • Streaming: Real-time token-by-token response streaming
  • Inactivity Guard: Streaming stops after 60s of no server deltas

File Structure

  • app.py — Main Streamlit app with Responses API integration
  • config.py — All app settings and customization (model selection, web search, prompt templates, system prompt)
  • requirements.txt — Python dependencies
  • terms.csv — Your course terms and definitions (CSV format: term, context)
  • example_syllabus.pdf — Example resource file (replace with your own syllabus)
  • LICENSE — GNU GPL-3 License file
  • S2_user_manual.pdf — Comprehensive user manual with detailed setup and configuration instructions

License

This project is licensed under the GNU GPL-3 License. See the LICENSE file for details.

Best Practices for Integration

Based on classroom testing and iterative refinement, here are recommended best practices:

  • Clear Structured Messaging: Provide students with clear instructions on how to use Schema Study and its role in your course
  • AI Literacy Training: Include brief training on AI literacy and appropriate use of AI tools in educational settings
  • Curricular Alignment: Ensure your terms and context align with your learning objectives - update content regularly as you progress through the course
  • Scaffolded Active Learning: Embed Schema Study within structured assignments rather than as an optional tool
  • Formative vs. Summative: Use Schema Study for formative practice and feedback; evaluate independent performance in secure assessments outside the app

Research & Citation

This app, its corresponding manuscript, and all documentation was authored, edited, and tested by Keefe Reuther, Liam O Mueller, Grace Constantian, and Albert Nguyen.

Schema Study was developed to address critical challenges in undergraduate biology education: providing immediate, personalized formative feedback to increasingly large, diverse classes. The app uses evidence-based teaching practices and Socratic questioning to deepen understanding, correct misconceptions, and encourage students to find connections among course concepts.

Research Evidence: During Winter 2025, Schema Study was integrated into an introductory biology course with 225 students. Pre- and post-surveys indicated strong student satisfaction, with 72% of students reporting they would reuse Schema Study in future biology courses. Each additional day per week students used Schema Study more than doubled the likelihood they would recommend it. Schema Study enhanced students' AI self-efficacy and their belief that AI is relevant to their education and careers.

If you use this app in your research or teaching, please cite the associated manuscript:

Reuther, K., Mueller, L. O., Constantian, G., & Nguyen, A. (2025). Schema Study: A Large Language Model (LLM) Application for Asynchronous Student Learning and Inquiry. CourseSource Teaching Tools and Strategies.

The production version of this app can be found at https://huggingface.co/spaces/keefereuther/Schema_Study.

Acknowledgments

This work was supported by University of California, San Diego intramural grants TG114333 and RG113974.

Support

For questions about creating your own version of this application for use in your classroom, please email kdreuther@ucsd.edu.