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Generative AI tools have spread through first-year university classrooms faster than institutions have produced guidance for their use, and current responses tend to focus on detecting AI-generated work rather than teaching students how to use AI responsibly. This research-in-progress proposal reframes responsible AI use as a teachable academic skill in the first year of university. The argument builds on Haidt's (2024) account of Generation Z as overprotected in the physical world and underprotected in the digital one, and extends it to higher education: a cohort already shaped by an underregulated digital environment now meets generative AI under similarly unregulated conditions. If first-year courses are to develop the cognitive capacities they claim to develop, AI use within them needs to be regulated at the course level rather than left to individual students. The proposal also draws on cognitive offloading, self-regulated learning, retrieval practice, and recent work on cognitive debt to ground its design.
The proposed action research study introduces three mechanisms in first-year undergraduate courses: optional source restrictions that anchor AI-assisted work to course materials, in-progress comprehension check-ins that function as embedded retrieval practice, and brief personalised in-class verification activities derived from each student's own submission. A short AI literacy sequence frames these mechanisms as skill development rather than surveillance. A quasi-experimental mixed-methods design will compare structured-engagement and unstructured-access conditions, measuring critical thinking, self-regulated learning behaviours, responsible AI use, and course performance, with instructor interviews and student focus groups providing qualitative depth. No outcome data are reported. The anticipated contribution is a practice-ready protocol, deliverable through standard learning management system tools, for teaching responsible AI use as a first-year university skill.
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