Funding opportunities

Learning through Creation with Generative AI

The Stanford Accelerator for Learning and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) invited research proposals advancing learning through creation with generative AI.

Photo: Nigel Hoare

Overview

Nearly two years after the launch of ChatGPT, many applications of generative AI aim to automate current teaching & learning models and promote efficiencies in education. Yet, generative AI also offers a far bolder opportunity to transform the very way people learn: through creation. Generative AI 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 invited proposals exploring generative AI’s potential to support learning through creative production, thought, or expression. This includes research on how generative AI 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 was given to proposals emphasizing creation or creativity in service of learning.

Funding covers early-stage work with scaling potential. We accepted three types of proposals: (1) empirical research that investigates questions of generative AI 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.

 

 

Application

Applications are currently closed

2025 Awardees (Faculty)

AI, Citizen Science, and Sociology: A Study of Collaborative Innovation on Eterna

AI tools are poised to transform human problem-solving, but their use risks a number of unintended sociological consequences that may negatively impact innovation. This project proposes to test the impact of an AI-assisted mRNA vaccine design on Eterna, an online research platform for RNA molecule design. As Eterna citizen-scientists begin 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.

Research Team: Rhiju Das

Beyond the Horizon: AI-Driven World-Building to Develop Adaptive Creativity

Building on MIT psychologist and Stanford Design Program founder John Arnold’s method of teaching creative problem-solving through fictional scenarios, researchers will work to develop an AI-powered learning platform that immerses students in future challenges to cultivate adaptive thinking. Led by the Stanford d.school team in collaboration with researcher Stergios Tegos and Enchatted—an agency that designs and develops conversational AI platforms—the project aims to create an intuitive interface that enables educators and 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.

Research Team: David Kelley, Leticia Britos Cavagnaro, Scott Doorley

Enhancing Math Learning and Engagement Through Game Creation

Early encounters with mathematics have a far-reaching impact on later achievement, but 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. The key to unlocking their potential may lie in resolving the misalignment between what adult game creators think is appealing and effective, and what children actually want and need. To close this gap, the scholars propose empowering children to create personalized math games, thereby revealing childrenʼs preferences while also supporting their agency, interest, and learning. Researchers will develop AI-augmented tools to make game design accessible to 6-9 year-olds. The resulting corpus of child-created math games will demonstrate both how childrenʼs interests and prior knowledge influence the games they create and how this game-design experience affects their own learning. The project’s long-term goal is to advance fundamental knowledge about childrenʼs ability to create engaging personalized learning experiences, paving new pathways for designing high-impact STEM learning environments.

Research Team: Judith Fan, Hyowon Gweon, Nick Haber, Hariharan Subramonyam, Junyi Chu

Ensonification: Ensemble Sonification of Temporal Data Through Human-AI Collaboration

Ensonification, 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 to auditory parameters, creating a series of sounds that facilitates identification of patterns in the data. Inversely, composers translate their music into visual data, i.e. sheet music — a series of symbols that allows readers to re-interpret and learn those compositions. This project expands the learning potential of conventional sonification (data to sound) and composition (sound to data) practices by developing 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.

Research Team: Nilam Ram, Chris Chafe, Hongchan Choi, Tristan Peng

HarmonAI: A Musical Companion for Children to Play, Create, and Explore

HarmonAI is a generative AI-based musical companion designed to foster creativity through interactive play for children aged 3-8. 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.

Research Team: Elizabeth Schumann, Mark Cutkosky, Chengyi Xing, Hao Li

Language Learning Through the Creation of Immersive Role Play Simulations

Learner agency and experiential learning are key elements of successful language learning, which demands learners achieve a degree of autonomy and receive linguistic and cultural context. However, most language learning technologies either lack an experiential learning environment or are restricted to teaching a small subset of content relevant to a single given environment, greatly reducing learner agency. To address these limitations, the researchers propose a system for crafting replayable immersive role play 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.

Research Team: James Landay, Danilo Symonette, Alan Cheng

Learning and Reskilling Pathways for Creative Professionals in the Age of Generative AI

This project aims to explore generative AI’s potential in enhancing creative learning and reskilling processes among professionals in advertising agencies, specifically for creative roles such as graphic designers and copywriters. This field study aims to examine how creative professionals not only utilize generative AI tools in their work but also create new 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 production, enhancing both learning and productivity.

Research Team: Arvind Karunakaran, Devesh Narayanan, Patrick Sheehan

Learning to Code Through Game Creation

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. These researchers propose a flexible framework that combines the scaffolding capabilities of AI with the engaging nature of game development. This 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, the researchers will deploy it in a user study to evaluate usability and engagement.

Research Team: Nick Haber, Fan-Yun Sun, Tianyu Hua, Violet Xiang

MyBook.fyi: Nostalgic Writing Therapy for Early to Middle Stage Dementia

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, the scholars 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.

Research Team: Randall Stafford

Teacher Planning Using Generative AI

This project explores possible roles for generative AI to augment (rather than automate) instructional planning, a process that involves designing and adapting lesson materials according to learning goals, assessments, and classroom activities, given classroom context. The proposed project has two concurrent strands. Strand A explores how teaching-methods instructors are already integrating generative AI 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 generative AI. This research contributes to foundational understanding of teacher decision-making in instructional planning and will produce a framework guiding responsible and effective use of generative AI use in teacher planning.

Research Team: Christina Krist, Polly Diffenbaugh

Translating Human Physiology into Generative AI Feedback for Creative Design

This project aims to develop a creative design platform for users to co-create art and writing, including story-writing and building immersive virtual worlds. This innovative system will translate human physiological signals—like ECG, EEG, and pupil dilation—into text, which 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. The researchers’ primary goal is to develop a platform that integrates creators’ emotional and cognitive states, via physiological feedback, in order to enhance the human-AI co-creative process. Secondarily, the project aims to study how best to utilize physiological data in the creative process. This physiological data-to-text-to-LLM system has the potential to scale across industries like entertainment, virtual reality, game design, and classroom education. 

Research Team: Todd Coleman, Irawadee Thawornbut

2025 Awardees (Students, Staff, and Postdocs)

Generative AI and Voice Intelligence for Cultivating Curiosity and Metacognition Through Reflective Journaling

Metacognition and curiosity are foundational to deeper learning and to the application of knowledge in real-world contexts. This project aims to explore how generative AI and voice intelligence (AI-based voice recognition and processing) 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.

Research Team: Kavindya Thennakoon

Generative AI in Maker Spaces: Investigating AI-Powered Guidance in Learning by Doing

AI is becoming increasingly integrated into educational settings, but its role in hands-on, maker-based learning environments is less explored. This 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. This project will center on the design and development of 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 receive either 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 insight 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.

Research Team: Jacob Ramirez, Jessica Ann, Karin Forssell

Learner-AI Collaboration in Education: Enhancing Creative Problem-Solving Through GenAI

The project explores how K-12 learners collaborate and engage with generative AI during creative problem-solving tasks, with a focus on promoting creative collaboration and examining generative AI’s impact on learners’ sense of creative agency. Learners in the project will complete a creative task under varying conditions of genAI integrations. By analyzing the fluency, flexibility, originality, and elaboration of their ideas and solutions—as well as how they interact with generative AI—the scholars aim to provide insights about productive learner-AI collaboration and the effective use of generative AI for creative problem solving in educational settings.

Research Team: Ibrahim Oluwajoba (‘Joba) Adisa

Towards Culturally Sustaining Technologies for Critical Algorithmic Literacy: A Case Study with AI Personas

This project explores how generative AI can support algorithmic literacy by empowering Black male youth to create their own culturally-sustaining digital personas at a Computer Science extracurricular program site in Oakland, California. By searching, analyzing, and generating content about themselves using generative AI tools, learners will engage with datafied abstractions of their cultural identities as represented in generative AI 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 support the development of critical algorithmic literacy, helping young learners understand how AI systems shape digital representations, implicitly aligning with particular cultural aesthetics while overlooking others.

Research Team: Jaylen Pittman, Ge (Tiffany) Wang, Roy Pea