arXiv AI By Lydia Manikonda, Mei Si, Sirajam Munira, Oshani Seneviratne, Kristin Bennett

The Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance, Evaluation Literacy, and Course Policy in AI-related Courses

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The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.

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arXiv AI
Oct 7

Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course

The study examined the impact of expert‑verified AI‑generated study materials on first‑year university economics students. In a two‑cohort difference‑in‑differences design, half of the 170 students received podcasts, FAQs, and quiz‑based guides that were produced by a source‑grounded model and checked by a graduate teaching assistant. The intervention produced a 2.34‑mark advantage on a 50‑mark component, reduced the share of marks below the upper‑second classification boundary by 24.7 percentage points, and had the largest gains for students in the bottom quintile. Interviews with 36 students suggested that the verification label encouraged engagement while preserving critical scrutiny of the AI output.

By Canh Thien Dang, An Nguyen