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