arXiv AI
Delegating Before Learning: Where Generative AI Sits in Students' Professional Communication
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Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement
arXiv:2607. 11749v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly reshaping workplace communication, yet whether AI-assisted writing changes how recipients actually behave, and through what channel, remains unknown.
arXiv AI
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Does AI Assistance Leave a Temporal Fingerprint? Detecting Overreliance in AI-Assisted Writing and Programming
The study investigates whether AI assistance leaves a temporal fingerprint in writing and programming tasks. By analyzing keystroke-level data from three corpora, the authors find that AI contributions appear in distinct bursts and that temporal patterns can almost perfectly distinguish wholesale delegation from authentic work, though ordinary collaboration remains hard to detect. The research suggests that process visibility could serve as a basis for academic integrity checks.
arXiv AI
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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.
arXiv AI
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Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
arXiv:2606. 28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them.
Hugging Face Trending Papers
Aug 12
Making AI-Generated Feedback Matter: From Provision to Student Enactment
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.
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.