Neighbor Gap Bridge (NGB) advances ethical, adaptive learning by creating experimental spaces where emerging technologies, institutions, and practices can be examined, tested, and reconfigured before they become fixed or irreversible. NGB operates through experiments:

Otis College Courseware – AI PLAY Creative Action/ 300F

The AI PLAY course empowers students to explore AI as a cultural, ethical, and creative force, not just a tool. In the Neighbor Gap Bridge Lab, students prototype AI-driven innovations—from sustainable fashion and post-AI vehicle design to autonomous learning bots and multi-generational ethics GPTs—while centering human values, agency, and iterative experimentation. Faculty and visiting professionals provide mentorship and workshops, ensuring deep ethical and technical engagement. AI is encouraged when cited but never required, giving students freedom to experiment responsibly. The lab employs lightweight reflective instruments to track shifts in students’ beliefs about agency, creativity, and authorship in relation to AI over time.This approach positions the college at the forefront of integrating creativity, ethics, and technology in higher education.

Limit No Limit – Design Research Conference

Neighbor Gap Bridge (NGB) an experimental research and pedagogical framework presented at Limit No Limit (Sorbonne, 2024) that examines how learning and creativity operate under conditions of acceleration, crisis, and systemic instability. The project proposes an alternative pedagogical model grounded in liminality, non-instrumental practices, and multi-generational studio inquiry, prioritizing sense-making over solutionism. Drawing from urban studies, design education, and AI literacy, the work reframes learning as a living inquiry that supports human agency and ethical engagement with complex systems. 

Bearly Ethical: Narrative-Based Approaches to Ethical Sense-Making in AI
Literacy

Bearly Ethical is a research-based educational project that examines how narrative, metaphor, and playful inquiry support ethical sense-making in AI literacy. The project uses a custom-designed GPT—trained on children’s moral fables alongside classical and contemporary ethical frameworks—as a pedagogical artifact to study how learners reason about responsibility, care, and decision-making in socio-technical systems. Rather than treating AI as a tool to be mastered, the project positions AI as a site for reflective dialogue, enabling learners to articulate values, confront ambiguity, and negotiate ethical tensions.

This work contributes to education research by exploring how narrative-based and affectively engaging approaches can deepen ethical understanding and agency in emerging technological contexts. add diversity dd heterogeniety of source content (GPT—trained on children’s moral fables alongside classical and contemporary ethical frameworks) and age span of users.

RED: A Research Training Artifact for Divergent Inquiry

RED is a pedagogical research artifact developed for an Honors Research Methodologies course at the University of Oregon, where color was used as a deliberately unconventional prompt to initiate inquiry. Rather than beginning with established topics or research questions, the presentation employs color as a generative constraint designed to disrupt familiar pathways and engineer novelty at the outset of research formation. Moving across biology, politics, media, culture, and values, the work invites divergent associations that resist premature categorization.

Grounded in the belief that if students enter research through familiar categories, they reproduce familiar knowledge, the project uses disorientation as a productive epistemic moment—particularly for high-achieving students trained in right-answer paradigms. The artifact functions both as a tool for expanding possible research trajectories and as a training intervention that supports pluralistic, design-based approaches to inquiry aligned with contemporary models of researcher preparation.

MIT / HTGAA: How to Grow Almost Anything

in progress

The projects featured here are representative experiments within a broader inquiry framework. NGB (Neighbor Gap Bridge) supports additional work that is iterative, provisional, and context-specific.