HAI/Accelerator for Learning Partnership Grant
HAI/Accelerator for Learning Partnership Grant
Generative AI for the Future of Learning: The Stanford Institute for Human-Centered Artificial Intelligence and the Stanford Accelerator for Learning invited proposals for designs and/or studies that explore how generative AI can be applied in novel ways to support learning or investigate critical issues in learning contexts. Faculty, academic staff, postdocs, and students from across the university were invited to apply for seed grant awards ranging from $5,000 to $100,000. The call for proposals is now closed.
Generative AI, or artificial intelligence that can generate novel content through text, audio files, or images, has 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.
In the future, we expect Stanford will be a hub for generative AI-based research and solutions that are scalable. This seed grant will fund early and exploratory stages of this work, such as designs, prototypes, and pilot studies that may have the potential to scale or have broad impact in the future. Proposals may target any type of learner (e.g., worker, student, teacher, family) in any setting (e.g., workplaces, museums, classrooms, homes).
2023 Recipients
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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.
Name Role School Department Hari Subramonyam Main PI Graduate School of Education Graduate School of Education Nick Haber Co-PI Graduate School of Education Graduate School of Education Shima Salehi Co-PI Graduate School of Education Graduate School of Education Maneesh Agrawala Co-PI School of Engineering Computer Science Roy Pea Co-PI Graduate School of Education Graduate School of 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.
Name Role School Department Christopher Potts Main PI School of Humanities and Sciences Linguistics Judith Fan Co-PI School of Humanities and Sciences Psychology -
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.
Name Role School Department Dora Demszky Main PI Graduate School of Education Graduate School of Education -
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 gen-AI ethical principles and AI challenges for education.
Name Role School Department John Mitchell Main PI School of Engineering Computer Science Jennifer Langer-Osuna Co-PI Graduate School of Education Graduate School of Education -
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.
Name Role School Department Adam Banks Main PI Graduate School of Education Graduate School of Education Harriet Jernigan Co-PI Vice Provost for Undergraduate Education Writing and Rhetoric -
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 (SDO) platform (shorturl.at/aIJKS) 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.
Name Role School Department Michael Snyder Main PI School of Medicine Genetics Anshul Kundaje Co-PI School of Medicine Genetics Amir Bahmani Co-PI School of Medicine Genetics -
Large language models (LLMs) 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 scholarship 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 towards 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.
Name Role School Department Chelsea Finn Main PI School of Engineering Computer Science Christopher Manning Co-PI School of Engineering Computer Science -
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.
Name Role School Department Carl Wieman Main PI School of Humanities and Sciences Physics Shima Salehi Co-PI Graduate School of Education Graduate School of Education Nick Haber Co-PI Graduate School of Education Graduate School of Education -
This project aims to use conversational AI and Virtual Reality (VR) 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 under-represented and under-resourced learners.
Name Role School Department Sakti Srivastava Main PI School of Medicine Surgery - Anatomy Ken Salisbury Co-PI School of Engineering Computer Science Joel Sadler Co-PI School of Medicine Surgery - Anatomy -
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 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.
Name Role School Department Russell Berman Main PI School of Humanities and Sciences German Ruth Starkman Co-PI Vice Provost for Undergraduate Education Writing and Rhetoric