Authoring Interactive Simulations with Generative AI for Culturally Sustaining Pedagogy
Educational simulations such as PhET can increase learner engagement and promote generative learning. Yet, it is nearly impossible for educators to create their own interactive simulations to support their learners. These scholars will develop the Simulations Adaptive Learning Tool (SALT), a tool that leverages generative AI capabilities to allow educators to create or adapt interactive content to meet learners’ needs. Using SALT, educators can personalize the simulations in ways that nurture students’ cultural and linguistic diversity, which can enhance the effectiveness of their learning experience.
Research team: Hari Subramonyam, Nick Haber, Shima Salehi, Maneesh Agrawala, Roy Pea
Generating Descriptions of Data Visualizations to Improve Accessibility and Learning Outcomes in STEM Education
Data visualizations are indispensable for communicating patterns in quantitative data and are crucial in STEM learning contexts. Unfortunately, these visualizations are only rarely accompanied by high-quality descriptions that would make them more accessible to blind and low-vision learners. These scholars will use research in AI, cognition, and education to make complex data visualizations more accessible through development of datasets containing high-quality descriptions of many kinds of data visualizations; training of AI systems that generate descriptions for novel data visualizations; and measurement of the impact of human and model-generated descriptions on learner comprehension.
Research team: Christopher Potts, Judith Fan, Elisa Kreiss
Teach M-Powered: A Tool for Teachers to Support Students’ Learning Mindset Through Written Feedback
Providing timely, personalized, and mindset-supportive feedback to students is an integral part of high-quality instruction, yet it is a nontrivial and extremely time-intensive task. These scholars will develop Teach M-Powered, a generative AI-powered tool that assists teachers with writing effective feedback to students.
Research team: Dora Demszky, Mei Tan, Rose Wang
Humanizing AI for Better Collaborative Learning
This project aims to explore various approaches to AI-assisted collaborative learning and develop and evaluate sample uses over the coming year. The team will develop an action research community where students collaborate on toolkits that will be offered to Stanford project partners for their teaching and learning environments next academic year, while examining GenAI ethical principles and AI challenges for education.
Research team: John Mitchell, Jennifer Langer-Osuna, Glenn Fajardo
College Writing with the BlackRhetorics Corpus for Generative Models
Since ChatGPT was released in November 2022 and many other models followed, researchers have studied their inability to generate African American English (AAE) in conversation with Black student communities. Such a deficit arises from the corpora that commercial generative models deploy. These scholars will build on the TwitterAAE and CORAAL corpora with their own data set called BlackRhetorics and use NLP transfer learning and dialect techniques to improve the tools for Black student research. This Black research team demonstrates how to deploy generative models for inclusive Black language pedagogies.
Research team: Adam Banks, Harriet Jernigan, Tolulope Ogunremi, Onyothi Nekoto
Unlocking Precision Medicine: Innovative Training & AI Chatbot for Self-Paced Learning in Underserved Communities
This project addresses the challenge of limited access to quality education and software in the rapidly growing field of biomedical data, which generates vast amounts of data requiring advanced computational skills to process. The team proposes expanding the Stanford Data Ocean platform with AI chatbots like ChatGPT to support interdisciplinary concepts in learning Precision Medicine. Their integrated curricula will be customized to address major challenges in accessing quality education for underserved communities.
Research team: Michael Synder, Anshul Kundaje, Amir Bahmani
Detecting AI-Generated Text in the Classroom
Large language models like ChatGPT are tempting tools for students to use to complete various forms of assessments, from rhetorical writing to programming. Inspired by this problem, this team recently released DetectGPT, which uses an LLM to automatically detect its own outputs. While DetectGPT and related systems recently developed by OpenAI and Turnitin are promising steps toward automated detection of machine-generated text, standardized measurements of detector quality are missing, making comparison of detectors impossible and leaving educators in the dark about whether a detector is trustworthy. The research team proposes a new benchmark for machine-generated text detectors, addressing blind spots in existing evaluations. They will use this evaluation suite to develop the next generation of detection algorithms.
Research team: Chelsea Finn, Christopher Manning, Eric Mitchell
Evaluating ChatGPT’s Capability in Supporting and Augmenting Real-World Problem Solving
This project aims to examine the potential of generative AI models in facilitating authentic problem solving in science and engineering domains, and to determine the extent to which college students can learn to leverage AI to enhance their problem-solving practices and outcomes. The research team will also explore how science and engineering experts use ChatGPT to augment their problem solving, which will lead to a framework of AI-human collaborative problem-solving practices. The research will have important implications for STEM education and how to prepare students for a future of human-AI collaboration.
Research team: Carl Wieman, Shima Salehi, Nick Haber, Karen Wang
MAI-TA: A Medical AI Teaching Assistant Using Conversational GPT-3 and Virtual Reality for Remote Medical Education
This project aims to use conversational AI and virtual reality to create interactive 3D avatars of medical virtual teaching assistants that can simulate real-world medical training in virtual environments. This team proposes MAI-TA, a medical conversational virtual agent that can supplement in-person teaching for personalized exploratory learning. Leveraging prior work on educational VR with anatomy photogrammetry scans, they will integrate OpenAI’s GPT-3 to afford students a conversational way to explore digital anatomical specimens with customized guidance in a virtual lab setting. This project builds on previous research demonstrating that VR and digital anatomy labs can broaden access to medical training with underrepresented and underresourced learners.
Research team: Sakti Srivastava, Ken Salisbury, Joel Sadler, Christoph Leuze, Samrawit Gebregziabher
Novel Pedagogy and Assessment Using Generative Models
This project uses generative models to engage students in the invaluable process of critical thinking and writing. The research team proposes deploying ChatGPT in their Stanford course ESF 17/17A What Can You Do for Your Country?, which asks students to read historically important texts about public service, from John F. Kennedy’s speech to Thucydides’ “Pericles’ Funeral Oration,” Lincoln’s “Gettysburg Address,” Frederick Douglass’ “What to the Slave Is the Fourth of July,” and many others. Thus far, they have seen that generative models can help students better learn and articulate their ideas about public service. The team expects that building new pedagogical approaches and assessment including generative models will add pedagogical value.
Research team: Russell Berman, Ruth Starkman