arXiv:2509. 15035v2 Announce Type: replace Abstract: This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States.
arXiv:2609.23264v1 Announce Type: new Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
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.
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. Generative AI offers a credible means of addressing the provision challenge, but students' uptake of AI-generated feedback remains limited.
As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions. We construct a controlled corpus of 4,200 full-paper manuscripts derived from 120 anonymized ICLR 2026 submissions.
arXiv:2608. 08975v1 Announce Type: cross Abstract: As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions.
arXiv:2608. 11625v2 Announce Type: replace 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.
Exposía is the first public dataset linking academic writing and feedback in higher education, comprising student research project proposals, peer and instructor comments, and free-text reviews collected from a Computer Science course. It includes human assessment scores based on a fine‑grained, pedagogically‑grounded schema for both writing and feedback. The dataset is used to benchmark large language models on automated scoring of proposals and student reviews, revealing that different LLMs excel at each task and that closed‑source models outperform open‑weight ones, while a multi‑aspect prompting strategy proves most effective for classroom deployment.
arXiv:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.
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.
arXiv:2606. 06025v1 Announce Type: cross Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback.
arXiv:2602. 05493v2 Announce Type: replace-cross Abstract: Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification.
arXiv:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.