HAI/Accelerator for Learning Partnership Grant
Learning through Creation with Generative AI
Request for Proposals
The Stanford Accelerator for Learning and the Stanford Institute for Human-Centered Artificial Intelligence invite research proposals advancing learning through creation with generative AI.
Nearly two years after the launch of ChatGPT, many applications of GenAI aim to automate current teaching & learning models and promote efficiencies in education. Yet, GenAI also offers a far bolder opportunity to transform the very way people learn: through creation. GenAI now presents learners with the exciting possibility of creating their own virtual worlds, simulations, chatbots, and other expressions of their developing knowledge.
The Stanford Accelerator for Learning and HAI invite proposals exploring GenAI’s potential to support learning through creative production, thought, or expression. This includes research on how genAI influences learning-by-making, imaginative exploration, or the development of creative abilities. Projects may target a wide range of creators, such as students, teachers, adults, or families, across various domains including STEM, arts, humanities and social sciences, and in diverse settings such as workplaces, museums, classrooms, and homes. Priority is given to proposals emphasizing creation or creativity in service of learning.
Funding covers early-stage work with scaling potential. We accept three types of proposals: (1) empirical research that investigates questions of GenAI and creation (2) design proposals that produce a working prototype of an AI-based tool or intervention or (3) a combination of design and empirical research.
Visit the Call for Proposals for criteria and eligibility. Applications were due on October 23, 2024. Please direct any questions to Catherine Chase at cchase@stanford.edu.
2024 Recipients
Faculty Projects
Artificial Intelligence (AI) tools are poised to transform human problem-solving but risk a number of unintended sociological consequences that may negatively impact innovation. Here we propose to test the impact of an mRNA vaccine design AI on Eterna, an existing online research platform for RNA molecule design. As Eterna citizen-scientists—self-selected volunteers—are beginning to utilize AI to design large RNA molecules, questions arise regarding the impact of these tools on human attitudes towards care, creativity, and engagement. This research will document whether AI enhances citizen-scientist productivity and autonomy in RNA design, thereby augmenting human capabilities, or instead inadvertently diminishes motivation and creative expression. By examining these dynamics through the framework of sociology rather than conventional productivity/learning metrics, the study seeks to understand the broader human impacts of AI on collaborative scientific efforts.
Name Role School Department Rhiju Das Main PI School of Medicine Biochemistry Building on MIT psychologist and Stanford Design Program founder John Arnold's method of teaching creative problem-solving through fictional scenarios, we propose developing an AI-powered learning platform that immerses students in future challenges to cultivate adaptive thinking. Led by our Stanford d.school team in partnership with Stergios Tegos and Enchatted—who design & develop conversational AI platforms—we aim to create an intuitive interface that enables educators & leaders to craft customized scenarios with dynamic plot twists and AI-generated characters, transforming how students develop anticipatory capabilities to navigate uncertain and novel situations.
Name Role School Department David Kelley Main PI School of Engineering Mechanical Engineering Leticia Britos Cavagnaro Team Member School of Engineering d.school Scott Doorley Team Member School of Engineering d.school Early encounters with mathematics have far-reaching impacts on later achievement. However, traditional instruction often fails to equip students with essential skills while alienating many from the subject. Educational games present a promising avenue for enhancing student engagement and learning, though evidence for their effectiveness is mixed. A key barrier to unlocking the promise of educational games might lie in the misalignment between what adult game creators think is appealing and effective, and what children actually want and need. For instance, a game might assume too much prior knowledge or fail to have an interesting premise. It is therefore critical to characterize what aspects of a game children think would make it both fun to play and helpful for learning. To close this gap, we propose empowering children of all ages to create personalized math games, thereby revealing childrenʼs preferences while enhancing their agency, interest, and learning. First, we will develop AI-augmented tools that make game design accessible to 6-9 year-olds. Next, we will develop a corpus of child-created math games to characterize both how childrenʼs interests and prior knowledge influence the games they create, as well as how this game-creation experience affects their own learning. Our long-term goal is to advance fundamental knowledge about childrenʼs ability to create engaging and personalized learning experiences, unlocking new opportunities for designing high-impact STEM learning environments.
Name Role School Department Judith Fan Main PI School of Humanities and Sciences Psychology Hyowon Gweon Co-PI School of Humanities and Sciences Psychology Nick Harber Co-PI Graduate School of Education Graduate School of Education Hariharan Subramonyam Co-PI School of Engineering Computer Science Junyi Chu Team Member School of Humanities and Sciences Psychology Ensonfication, a portmanteau of ensemble sonification, introduces a novel approach to human-AI collaborative music performance and data display. Traditional sonification involves mapping specific variables in the data (x,y,z) to auditory parameters (pitch, volume, timbre) that a computer then synthesizes into an auditory display—a series of sounds that facilitate listeners’ identification of patterns in the data. In complement, musicians/composers engaged in creative musical composition through transfer of their sonic improvisations into visual data—a series of symbols (F#, B\flat) that facilitate readers’ ability to re-interpret and learn those data. This project extends the learning potential of conventional sonification (data to sound) and composition (sound to data) practices with AI by developing a practical system that opens new possibilities for humans and AI to collaboratively create music performance and data display. Specifically, we will create a web-based AI application that learns and generates data-sound mappings in real time, supporting an ensemble of human learners and performers as they simultaneously develop their creative potential and understanding of the data.
Name Role School Department Nilam Ram Main PI School of Humanities and Sciences Communication, Psychology Chris Chafe Co-PI School of Humanities and Sciences Music Hongchan Choi Team Member School of Humanities and Sciences Music Tristan Peng Team Member School of Humanities and Sciences Music HarmonAI is a generative AI-based musical companion for children aged 3-8, designed to foster creativity through interactive play. While children naturally engage with music through improvisation and creativity, traditional music education often emphasizes technical skills and structured learning, which can restrict their creative expression. HarmonAI addresses this challenge by actively listening to children’s vocalizations and responding in ways that encourage musical exploration and creativity. This interaction fosters a playful and responsive musical dialogue, nurturing musical skills and cognitive development. HarmonAI is designed to be accessible to all children, ensuring that every young learner has the opportunity to develop foundational musical abilities in an encouraging and enjoyable manner.
Name Role School Department Elizabeth Schumann Main PI School of Humanities and Sciences Music Mark Cutkosky Co-PI School of Engineering Mechanical Engineering Hao Li Team Member School of Engineering Mechanical Engineering Chengyi Xing Team Member School of Humanities and Sciences Music Learner agency and experiential learning are key modern educational goals, especially for the field of language learning, for which learner autonomy and context for learning are paramount. Yet most language learning technologies either lack an experiential learning environment or are restricted to teaching a small subset of content relevant to the environment provided, greatly reducing learner agency. Towards addressing these limitations, we propose a system for crafting replayable immersive roleplay simulations using generative artificial intelligence and mixed reality. This tool will allow learners of all skill levels to create, learn, and practice through experiential learning scenarios designed with their personal context in mind.
Name Role School Department James Landay Main PI School of Engineering Computer Science Alan Cheng Team Member School of Engineering Computer Science Danilo Symonette Team Member School of Engineering Computer Science This project aims to explore the transformative potential of generative AI (GenAI) in enhancing creative learning and reskilling processes among professionals in advertising agencies, specifically focusing on creative roles such as graphic designers and copywriters. This proposed research uses inductive, ethnographic methods, based on a field study of an advertisement agency (BayCreative) with an emphasis on ‘learning by creating.’ Our research aims to examine how creative professionals not only utilize GenAI tools in their work but also create new Gen AI tools (e.g., customized chatbots) to ideate, formulate representations of creative problems, engage in creative problem-solving, and reskill themselves in the process, thereby transforming the nature and structure of their creative output and deepening their expertise. The insights gained from this study will be used to develop evidence-based frameworks to integrate AI tools for creative professionals (and beyond), enhancing both learning and productivity.
Name Role School Department Arvind Karunakaran Main PI School of Engineering Management Science and Engineering Devesh Narayanan Team Member School of Engineering Management Science and Engineering Patrick Sheehan Team Member School of Engineering Management Science and Engineering While platforms like Roblox and Scratch have demonstrated the effectiveness of learning to code through creation, they often present trade-offs between accessibility and creative freedom. We propose a flexible framework that combines the scaffolding capabilities of AI with the engaging nature of game development. Our system will support multiple modes of AI-learner interaction, from full code generation to guided modification, enabling learners to progressively develop both programming skills and computational thinking abilities. After developing a prototype and iterating it through initial feasibility, we will deploy it in a user study to evaluate usability and engagement.
Name Role School Department Nick Haber Main PI Graduate School of Education Graduate School of Education Tianyu Hua Team Member School of Engineering Computer Science Fan-Yun Sun Team Member School of Engineering Computer Science Violet Xiang Team Member School of Humanities Psychology Dementia affects over 55 million people worldwide, with Chinese American seniors being especially vulnerable due to cultural and language barriers that complicate caregiving and medical support. While reminiscence therapy has demonstrated benefits in enhancing cognitive function, emotional well-being, and overall quality of life for dementia patients, many existing interventions do not cater to the unique cultural and linguistic needs of this community.
To address this gap, we propose a novel intervention: AI-guided nostalgic writing tailored for Chinese American seniors in the early to middle stages of dementia. Participants will interact with AI assistants on the Perplexity.ai platform, specifically designed to communicate in Mandarin. These AI assistants will guide seniors through personalized autobiographical, creative, and legal writing exercises, providing prompts and feedback that reflect their local and historical contexts. The content generated during these sessions can be transferred to the Notion app and shared as website pages, maintaining personal and cultural legacies for families and communities.Name Role School Department Randall Stafford Main PI School of Medicine Center for Research in Disease Prevention This project explores possible roles for generative AI (GenAI) to augment (rather than automate) instructional planning, a collection of core practices in teaching that involves designing and adapting lesson materials by alignment of learning goals, assessments, and classroom activities in light of classroom context. The proposed project has two concurrent strands. Strand A explores how teaching methods instructors are already integrating GenAI into lesson creation, adaptation, and critical evaluation. Strand B conducts clinical interviews with pre-service and in-service teachers to examine their reasoning processes when engaging in various dimensions of planning using GenAI. This research contributes to foundational understanding of teacher decision-making in instructional planning and will produce a framework guiding responsible and effective use of GenAI use in teacher planning.
Name Role School Department Christina Krist Main PI Graduate School of Education Graduate School of Education Polly Diffenbaugh Co-Investigator Graduate School of Education Graduate School of Education This project aims to develop a creative design platform for users to co-create art and write, including story writing and building immersive virtual worlds. Our goal is to do this by developing a system that translates human physiological signals—like ECG, EEG, and pupil dilation—into text. This will then be fed into Large Language Models to drive real-time adaptive feedback in the collaborative design process. In this collaborative design environment, AI agents will use real-time physiological data to dynamically manipulate design elements. We aim to 1) develop a platform that integrates physiological feedback, with the goal to enhance the human-AI co-creation in the creative process of innovators, based on their emotional and cognitive states, and 2) study how to best utilize physiological data in the creative process.
Our interdisciplinary strengths in the Computer Science and Bioengineering departments, particularly our expertise in physiologic signal processing and human-computer interaction in designing AI systems, puts us in a unique position to develop this system. This physiological data-to-text-to-LLM system has potential to scale across industries like entertainment, virtual reality, game design, and classroom education. We aim to push the boundaries of the creative design process by incorporating AI systems that utilize human physiological states.Name Role School Department Todd Coleman Main PI School of Engineering Bioengineering Irawadee Thawornbut Team Member School of Engineering Computer Science
Student/Postdoc/Staff Projects
Metacognition and curiosity are foundational to deeper learning and the application of knowledge in real-world contexts. This project aims to explore how generative AI and voice intelligence can be used to facilitate multi-modal journaling for elementary learners. Multi-modal journaling involves using voice, text, imagery, and video for reflective journaling. Leveraging speech-to-text, generative AI, and natural language processing, the journal will allow elementary learners to create interactive journals about their everyday lives. The tool will transcribe children's spoken reflections, identify learning-related themes, and provide personalized prompts that encourage deeper inquiry, self-reflection on learning processes, and the development of metacognitive strategies. This tool aims to support non-English speaking students in South Asia in becoming more active and engaged learners by fostering a habit of reflective journaling in their native languages.
Name Role School Department Kavindya Thennakoon Lead Scholar Graduate School of Education Graduate School of Education AI is increasingly being integrated into educational settings, but its role in hands-on, maker-based learning environments is less explored. Our project seeks to investigate how learners respond to AI-powered guidance compared to human support when using various tools integral to maker education, starting with the sewing machine. We aim to design and develop a custom Makery chatbot trained to guide participants through using different makerspace tools during scheduled 'tool office hours.' Upon signing up, participants will be informed that they will either receive guidance from the AI tool guide bot or human support, with the possibility of receiving additional human assistance after their AI-guided session. This project will provide insights into the effectiveness of and student preferences for AI-powered guidance in maker spaces, contributing to broader knowledge about AI's role in education and hands-on learning environments.
Name Role School Department Jacob Ramirez Lead Scholar Graduate School of Education Graduate School of Education Jessica Ann Team Member Graduate School of Education Graduate School of Education Karin Forssell Team Member Graduate School of Education Graduate School of Education Our project explores how K-12 learners collaborate and engage with generative AI (GenAI) during creative problem-solving tasks, focusing on strategies that promote creative collaboration and examining GenAI’s impact on learners’ sense of creative agency. Learners in the project will complete a creative task under varying conditions of GenAI integrations: divergent-divergent-convergent, divergent-convergent-convergent, and divergent-convergent conditions. By analyzing the fluency, flexibility, originality, and elaboration of their ideas and solutions—as well as how they interact with GenAI—we aim to provide insights about productive learner-AI collaboration and the effective use of GenAI for creative problem solving in educational settings.
Name Role School Department Ibrahim Oluwajoba ('Joba) Adisa Lead Scholar Graduate School of Education Graduate School of Education This project explores how Generative AI (GenAI) can support learning by empowering Black male youth to create their own culturally sustaining digital personas at a Computer Science extracurricular program site in Oakland, California. Through activities such as searching, analyzing, and generating content about themselves using GenAI tools, learners will engage with datafied abstractions of their cultural identities as represented in GenAI systems. In addition, learners will participate in hands-on model tinkering and an accompanying reflection workshop to learn how AI systems exploit the datafication of their digital presence to construct these personas. The central goal is to develop critical algorithmic literacy, helping young learners understand how AI systems shape digital representations and how these systems implicitly align with particular cultural aesthetics while overlooking others.
Name Role School Department Jaylen Pittman Lead Scholar Graduate School of Education Graduate School of Education Roy Pea Team Member Graduate School of Education Graduate School of Education Ge (Tiffany) Wang Team Member Graduate School of Education Graduate School of Education
Previous Years
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
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