Funding opportunities

Generative AI for the Future of Learning

From generative AI platforms to adaptive learning programs and more precise feedback, AI is rapidly changing opportunities in teaching and learning. The use of AI in learning is also sparking challenging questions about what is taught and how and what kind of data needs to be collected.

Technology human touch background, modern remake of The Creation of Adam


Generative AI is artificial intelligence that can generate novel content by utilizing existing text, audio files, or images. Generative AI has now reached a tipping point where it can produce high quality output that can support many different kinds of tasks. For example, ChatGPT can write essays and code, DALL-E can create images and art, while other forms of generative AI can produce recipes, music, and videos. These new forms of generative AI have the capacity to change how we think, create, teach, and also learn. They may also change our perspective on what is important to learn.

Most generative AI tools were not built for educational purposes, and advances in the technology have outpaced research and design of their application to learning contexts. We invited scholars and students from across Stanford University to submit proposals for innovative designs and/or studies that explore how generative AI can be applied in novel ways to support learning and/or investigate critical issues in learning contexts.

There is an opportunity for research and design solutions to shape future applications of this emerging technology in an ethical, equitable, and safe manner. This seed grant funds early and exploratory stages of this work, such as designs, prototypes, and pilot studies that have the potential to scale or have broad impact. In line with this goal, we accepted the following types of proposals:

  • Design proposals which produce a working prototype of an AI-based learning tool or an intervention that applies an existing AI learning tool. Designs should be grounded in user/stakeholder contexts and needs.
  • Empirical research proposals that investigate questions or hypotheses around generative AI and the future of learning.
  • A combination of design and empirical work.

Proposals target any type of learner (e.g., worker, student, teacher, family) in any setting (e.g., workplaces, museums, classrooms, homes). We especially welcomed proposals that focus on one or more of the Accelerator’s areas of concentration including:

  • early childhood learning and development
  • learning differences and the future of special education
  • equity and learners
  • workforce learning

Applications are currently closed

2023 Awardees (Faculty)

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 Snyder, 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

2023 Awardees (Students, Staff, and Postdocs)

“Can You Hear Me?”: How Native Users of Non-Dominant Sociolects Adapt, Contort, and Remix LLMs

This team will investigate how native users of non-dominant sociolects engage with large language models in ways that replicate and contest dominant linguistic hierarchies. They will build theories about language and learning in a society with generative AI, exploring emerging concerns about the effect of LLMs like fears that AI will homogenize, formalize, and unduly arbitrate language.

Research team: Laura Hill-Bonnet, Parth Sarin

Neurodiversity and Generative AI: Enhancing Creative Self-Expression in Students with Disabilities in Bangladesh

This project will explore the use of generative AI in enhancing the learning experience of neurodiverse students, especially those from low-income backgrounds who have limited literacy skills and/or are non-verbal. The project will incorporate generative AI into the curriculum of “Joy of Computing” – a computer training program in Dhaka, Bangladesh. Students will be able to generate creative content that they will incorporate into their personalized coursework and homework.

Research team: Labib Tazwar Rahman

Documenting, Co-Designing, and Publishing Teachers’ Strategies for Teaching Writing with ChatGPT

AI applications like ChatGPT are often framed as threats to writing teachers; however, they also have tremendous potential to help teachers perform their jobs more effectively. This project aims to document strategies writing teachers are already using to leverage ChatGPT, co-design new strategies with teachers, and create a publicly accessible framework of strategies that teachers can use as a reference.

Research team: Chris Mah

Helping K-12 Teachers Plan Better Lessons in Less Time

Teachers spend an average of three hours planning lessons per day – time they could be spending directly supporting students or taking care of themselves. Scholars will build a new AI tool to drastically reduce the time teachers spend creating lesson materials. By leveraging AI, teachers will be able to rapidly edit and refine lesson materials with natural language prompts. The system will also make suggestions for how to incorporate evidence-informed pedagogical strategies into the lesson materials. In doing so, the tool helps teachers prepare better lesson materials in less time.

Research team: Rizwaan Malik, Claire Chen, Zaeem Bhanji, Stephanie Seidmon, Sonya Kotov, Manasi Sharma

Museum in the Classroom: Enhancing Learning Engagement and Comprehension of School Topics Through an AR-Based Educational App

In today’s digital-oriented world, the traditional classroom setup often fails to capture students’ attention and stimulate their curiosity. To address this, this project will develop an educational app that leverages augmented reality technology to increase classroom engagement. Educators can list what topics they are currently teaching and the app will use AR and artificial intelligence to display an AR museum filled with AI-generated artifacts that are related to the inputted topic and can be represented through common classroom items. The app will guide teachers to lightly rearrange the classroom to emulate a museum. Students then use an iPad to scan the classroom items, which will be associated with the artifacts. Students will receive information about them, as well as trivia-like questions to test their understanding.

Research team: Carina Ly, Alan Cheng, Andrea Cuadra

Developing Novice Programmers’ Capacity for Critical Reflection on Generative AI

This research explores how novice programmers can critically reflect on generative AI to effectively use GenAI tools to augment their programming processes. GenAI tools such as GitHub Copilot can help experienced programmers write, interpret, and test code. These tools may also be able to help the growing number of people interested in learning to code. However, GenAI tools have opaque limitations and biases that can lead to confusion, frustration, or diminished self-efficacy of learners, especially learners from minoritized groups. This project seeks to understand how novice programmers can use Copilot to support writing, interpreting, and evaluating code. This research will take a critical stance to GenAI, situating it as a problematic yet powerful tool that requires constant critical reflection to determine appropriate usage.

Research team: Benjamin Xie