arXiv AI By Seth Bernstein, Naaz Sibia

Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education

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arXiv AI
Aug 13

Making AI-Generated Feedback Matter: From Provision to Student Enactment

arXiv:2608. 11625v1 Announce Type: new Abstract: Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively.

By Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge, Dragan Ga\v{s}evi'c, Naomi Winstone, Hassan Khosravi
arXiv AI
Sep 7

Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

The study explores how undergraduate computing students in Saudi Arabia perceive AI‑generated writing feedback when they are explicitly told that ChatGPT, not a human instructor, produced the score and comments. Through qualitative reflections, four themes emerged: students found the feedback useful for surface‑level revisions, recognized AI’s contextual and pedagogical limits, trusted the feedback conditionally—separating its utility from its authority—and reaffirmed the human instructor’s role as the ultimate grading authority. The findings highlight a clear distinction students make between feedback usefulness and evaluative authority, treating them as separate judgments rather than opposing ends of a single approval scale.

By Rayed AlGhamdi