SafeTutors：AI 辅导系统中的教学安全基准测试

Computer Science > Computation and Language arXiv:2603.17373 (cs) [Submitted on 18 Mar 2026 (v1), last revised 23 Sep 2026 (this version, v2)] Title:SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems Authors:Rima Hazra, Bikram Ghuku, Ilona Marchenko, Yaroslava Tokarieva, Sayan Layek, Somnath Banerjee, Julia Stoyanovich, Mykola Pechenizkiy View a PDF of the paper titled SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems, by Rima Hazra and Bikram Ghuku and Ilona Marchenko and Yaroslava Tokarieva and Sayan Layek and Somnath Banerjee and Julia Stoyanovich and Mykola Pechenizkiy View PDF HTML (experimental) Abstract:Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture whether a model is simultaneously pedagogically effective and safe across student-tutor interaction. We argue that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. To systematically study this failure mode, we introduce SafeTutors, a benchmark that jointly evaluates safety and pedagogy across mathematics, physics, and chemistry. SafeTutors is organized around a theoretically grounded risk taxonomy comprising 11 harm dimensions and 48 sub-risks drawn from learning-science literature. We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%. Harms also vary by subject, so mitigations must be discipline-aware, and single-turn "safe/helpful" results can mask systematic tutor failure over extended interaction. Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2603.17373 [cs.CL] (or arXiv:2603.17373v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.17373 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rima Hazra [view email] [v1] Wed, 18 Mar 2026 05:33:50 UTC (480 KB) [v2] Wed, 23 Sep 2026 16:42:06 UTC (480 KB) Full-text links: Access Paper: View a PDF of the paper titled SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems, by Rima Hazra and Bikram Ghuku and Ilona Marchenko and Yaroslava Tokarieva and Sayan Layek and Somnath Banerjee and Julia Stoyanovich and Mykola Pechenizkiy View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)