LearnAI：大学跨学科即时AI协同创作
LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University
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中文总结 AI 辅助
该研究提出LearnAI两层AI协同创作框架，试点后发现其能帮助不同能力学习者转变对AI的认知，贡献了可实用的AI教育框架及初步证据。
中文摘要 AI 辅助
随着生成式AI重塑专业与教育实践，机构面临一项挑战：如何支持从非编程者到高阶学生的各类学习者，建立对AI辅助问题解决的信心并开展实践。多数机构的应对措施分化为面向普通受众的概念性工作坊，或面向计算机科学专业学生的技术课程，几乎没有为不同能力水平的学习者提供空间，使其能在匹配自身既有经验的水平上参与常见AI任务。本经验报告介绍了LearnAI Framework，这是在一所综合性教学大学试点的两层即时AI协同创作模型。广覆盖层在现有课程中嵌入简短演示，以规模化建立AI认知，覆盖了五个学科的18门课程的学生与教师。定制协同创作层提供可选的一对一会话，客户与受过训练的本科生导师共同遵循五阶段教学脚本开展工作：问题构建、工具-任务映射、迭代协同提示、部署与验证，以及伦理反思。两个学期以来，35位客户协同创作了36个作品集网站和20多个已部署的网络应用。对5位客户和2位导师的访谈显示，客户对AI使用的描述出现反复变化，从将AI视为被动答案机器，转变为将其作为人类指导下的协作工具。小型配对前后AI准备度数据集（N=7）提供了初步描述性背景，导师记录了该教学脚本如何在不同客户类型中实施与调整。本文报告了边界案例，包括感到不堪重负的客户和刻意拒绝使用AI的受访者，贡献了一个实用、可采用的框架，并提供了来自单一机构的初步证据。
英文摘要
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.
