{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:04:27Z","timestamp":1773806667402,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"39","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Large Language Models (LLMs) show significant potential in AI mathematical tutoring, yet current evaluations often rely on simplistic metrics or narrow pedagogical scenarios, failing to assess comprehensive, multi-turn teaching effectiveness. In this paper, we introduce KMP-Bench, a comprehensive K-8 Mathematical Pedagogical Benchmark designed to assess LLMs from two complementary perspectives. The first module, KMP-Dialogue, evaluates holistic pedagogical capabilities against six core principles (e.g., Challenge, Explanation, Feedback), leveraging a novel multi-turn dialogue dataset constructed by weaving together diverse pedagogical components. The second module, KMP-Skills, provides a granular assessment of foundational tutoring abilities, including multi-turn problem-solving, error detection and correction, and problem generation. Our evaluations on KMP-Bench reveal a key disparity: while leading LLMs excel at tasks with verifiable solutions, they struggle with the nuanced application of pedagogical principles. Additionally, we present KMP-Pile, a large-scale (150K) dialogue dataset. Models fine-tuned on KMP-Pile show substantial improvement on KMP-Bench, underscoring the value of pedagogically-rich training data for developing more effective AI math tutors.<\/jats:p>","DOI":"10.1609\/aaai.v40i39.40578","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:04:04Z","timestamp":1773803044000},"page":"32965-32973","source":"Crossref","is-referenced-by-count":0,"title":["From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench"],"prefix":"10.1609","volume":"40","author":[{"given":"Weikang","family":"Shi","sequence":"first","affiliation":[]},{"given":"Houxing","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Junting","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Aojun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zimu","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yunqiao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yuxuan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Linda","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Mingjie","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Hongsheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40578\/44539","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40578\/44539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:04:07Z","timestamp":1773803047000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/40578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"39","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i39.40578","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}