researchGrant
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 |
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 |
Main PI | School of Engineering | Mechanical Engineering | |
Team Member | School of Engineering | d.school | |
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 |
Main PI | School of Humanities and Sciences | Psychology | |
Co-PI | School of Humanities and Sciences | Psychology | |
Co-PI | Graduate School of Education | Graduate School of Education | |
Co-PI | School of Engineering | Computer Science | |
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 |
Main PI | School of Humanities and Sciences | Communication, Psychology | |
Co-PI | School of Humanities and Sciences | Music | |
Team Member | School of Humanities and Sciences | Music | |
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 |
Main PI | School of Humanities and Sciences | Music | |
Co-PI | School of Engineering | Mechanical Engineering | |
Team Member | School of Engineering | Mechanical Engineering | |
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 |
Main PI | School of Engineering | Computer Science | |
Team Member | School of Engineering | Computer Science | |
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 |
Main PI | School of Engineering | Management Science and Engineering | |
Team Member | School of Engineering | Management Science and Engineering | |
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 |
Main PI | Graduate School of Education | Graduate School of Education | |
Tianyu Hua | Team Member | School of Engineering | Computer Science |
Team Member | School of Engineering | Computer Science | |
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 |
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 |
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 |
Main PI | School of Engineering | Bioengineering | |
Team Member | School of Engineering | Computer Science |
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 |
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 |
Lead Scholar | Graduate School of Education | Graduate School of Education | |
Team Member | Graduate School of Education | Graduate School of Education | |
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 |
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 |
Lead Scholar | Graduate School of Education | Graduate School of Education | |
Team Member | Graduate School of Education | Graduate School of Education | |
Team Member | Graduate School of Education | Graduate School of Education |