LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University
2026-08-19 • Computers and Society
Computers and SocietyHuman-Computer Interaction
AI summaryⓘ
The authors created the LearnAI Framework to help students with different levels of AI knowledge learn to work with AI tools together. They used two parts: brief presentations in many courses to raise general awareness, and personalized tutoring sessions where students worked step-by-step with undergraduates to build AI projects. Their work showed that students began to see AI as a team helper rather than just a tool for answers. They also reported some challenges, like students feeling overwhelmed or choosing not to use AI. This framework was tested at one university and could be used by others.
Generative AIAI-supported problem solvingAI co-creationPedagogical frameworkIterative co-promptingAI readinessPortfolio websiteEthical reflectionUndergraduate tutorsMixed-ability learners
Authors
Weihao Qu, Ling Zheng, Chris Buzaid, Daniel Crawford
Abstract
As generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience. This experience report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university. The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines. The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection. Over two semesters, 35 clients co-created 36 portfolio websites and over 20 deployed web applications. Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction. A small paired pre/post AI readiness dataset (N = 7) provides preliminary descriptive context, and tutor accounts document how the pedagogical script was enacted and adapted across client types. We report on boundary cases including clients who felt overwhelmed and respondents who deliberately rejected AI use. This paper contributes a practical, adoptable framework with initial evidence from a single institution.