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LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University

● high · 2026-08-20 · ai-literacy, higher-ed, curriculum-design, instructional-design, generative-ai, human-in-the-loop-ai, agency, self-efficacy, prompt-engineering, faculty-development

Synthesis: LearnAI is a two-layer institutional framework for just-in-time AI co-creation that bridges the gap between one-off conceptual workshops and technical CS courses. A Wide-Exposure Layer builds AI awareness at scale by embedding short presentations in existing courses (18 courses across five disciplines), while a Customized Co-Creation Layer offers opt-in one-on-one sessions where mixed-ability learners work with trained undergraduate tutors through a 5-Stage pedagogical Script (Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, Ethical Reflection). Over two semesters 35 clients co-created 36 portfolio websites and 20+ deployed web applications, with interviews suggesting a shift from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction — a concrete Human In The Loop AI reframing of over-reliance.

Key Contributions

  • Two-layer LearnAI framework for differentiated AI Literacy development: a Wide-Exposure Layer for broad awareness at scale and a Customized Co-Creation Layer for hands-on, individualized practice.
  • 5-Stage Pedagogical Script structuring tutor-led co-creation sessions: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection.
  • Mixed-ability access model using trained undergraduate tutors rather than technical courses, addressing the common bifurcation between general workshops and CS-major coursework.
  • Evidence of perspective shift: clients moved from seeing AI as a passive answer machine to a collaborative tool under human direction, connecting to Agency and Self Efficacy.
  • Boundary cases documented: clients who felt overwhelmed and respondents who deliberately rejected AI use — relevant to Reducing AI Misuse and Ethics.

Institutional Design and Just-in-Time Learning

The framework's key insight is that effective AI education must be just-in-time and differentiated rather than one-size-fits-all. LearnAI embeds awareness-building in the curriculum of existing courses (the Wide-Exposure Layer) while offering opt-in, individualized co-creation sessions (the Customized Layer). This directly addresses a gap the authors identify: most institutions offer either conceptual workshops for general audiences or technical courses for CS majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience.

The model leans on trained undergraduate tutors as peer facilitators, an instructional approach that distributes the teaching burden and creates authentic near-peer learning. The pedagogical script sequences task-oriented co-prompting with explicit human oversight and a final ethical reflection stage, deliberately weaving responsibility and critical reflection into the technical workflow rather than treating ethics as an add-on.

Evidence and Limits

The study is an experience report with preliminary evidence: a small paired pre/post AI-readiness dataset (N = 7), qualitative interviews with five clients and two tutors, and tutor accounts of how the script was enacted. It is explicitly a single-institution, adoptable-framework contribution rather than a controlled efficacy trial. Its value lies in the practical, replicable structure and the documented boundary cases (overwhelm, deliberate AI rejection) that inform Reducing AI Misuse and self-regulation design.

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Citation

Qu, W., Zheng, L., Buzaid, C., & Crawford, D. (2026). LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University. arXiv:2608.19164 [cs.CY, cs.HC].