arXiv AI

Beyond Helpfulness: A Teaching-over-Solving Diagnostic for Measuring Educational Impact in LLM Tutors

arXiv AI
Aug 5

EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners

arXiv:2608. 03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS).

By Unggi Lee, Sookbun Lee, Yeil Jeong, Eunjoo Lee, Minchul Shin, Hoilym Kwon
arXiv AI
Jun 6

SAGE: Scalable AI Governance & Evaluation

arXiv:2602. 07840v3 Announce Type: replace-cross Abstract: Evaluating relevance in large-scale search systems is fundamentally constrained by the governance gap between nuanced, resource-constrained human oversight and the high-throughput requirements of production systems.

By Benjamin Le, Xueying Lu, Nick Stern, Wenqiong Liu, Igor Lapchuk, Xiang Li, Baofen Zheng, Kevin Rosenberg, Jiewen Huang, Zhe Zhang, Abraham Cabangbang, Satej Milind Wagle, Jianqiang Shen, Raghavan Muthuregunathan, Abhinav Gupta, Mathew Teoh, Andrew Kirk, Thomas Kwan, Jingwei Wu, Wenjing Zhang
arXiv Computation and Language
Aug 25

LLM Pedagogical Behavior in AI Tutoring Interactions

arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students u...

By Suhyeon Lee, Juneha Baek, Jaehyeong Park, Donghyuk Shin
arXiv AI
Jul 24

AI Assistants Overassist

arXiv:2607. 21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems.

By Verona Teo, Raghav Jain, Tobias Gerstenberg, Max Kleiman-Weiner
arXiv AI
Jun 6

From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment

arXiv:2606. 05180v1 Announce Type: cross Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced.

By Ivo Bueno, Babette B\"uhler, Philipp Stark, Tim F\"utterer, Ulrich Trautwein, Dorottya Demszky, Heather Hill, Enkelejda Kasneci