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
arXiv:2607. 01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps.
By Aastha Sapkota, M. G. Sarwar Murshed
arXiv:2608.28626v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This st...
By Emad Alharbi
arXiv:2603.17839v4 Announce Type: replace-cross
Abstract: Verbal confidence -- prompting LLMs to state their confidence as a number or category -- is widely used to extract uncertainty estimates from...
By Dharshan Kumaran, Arthur Conmy, Federico Barbero, Simon Osindero, Viorica Patraucean, Petar Veli\v{c}kovi\'c
The paper examines whether state‑of‑the‑art large language models (LLMs) produce feedback that aligns with expert teachers’ pedagogical practices, focusing on feedback type and adaptivity. Using a refined taxonomy of seven feedback focus types, the authors annotate and compare teacher and LLM‑generated feedback from three university writing courses, creating the FeedType benchmark. Their analysis shows that while most LLMs cover many feedback types, they do not match teachers’ distribution patterns or adaptive behavior across draft stages and student performance levels.
By Norah Almousa, Shayan Peyghambari Oskoui, Raquel Coelho, Gayle Rogers, Xiang Lorraine Li, Diane Litman
The paper reports a structured case study in which six frontier language models were asked to match handwritten exam answers to the ages of the writers. Three of the models performed at chance on the matching task while still reporting high confidence, and the youngest writer’s answer was misattributed to an adult in up to 90% of trials. Removing the multiple‑choice scaffold and changing institutional framing further degraded accuracy, while a single audience‑awareness instruction improved performance for one model but had no effect on the weakest models.
By Brianne Lee