Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation

Computer Science > Artificial Intelligence [Submitted on 13 Apr 2026] Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation Candace Walkington, Theodora Beauchamp, Fareya Ikram, Merve Koçyiğit Gürbüz, Fangli Xia, Margan Lee, Andrew Lan Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personalizing middle school math problems. The teacher enters a base problem and desired topic, the LLM generates the problem, and then four AI agents evaluate the problem using criteria that each specializes in (mathematical accuracy, authenticity, readability, and realism). Eight middle school mathematics teachers created 212 problems in ASSISTments using the system and assigned these problems to their students. We find that both teachers and students wanted to modify the fine-grained personalized elements of the real-world context of the problems, signaling issues with authenticity and fit. Although the agents detected many issues with realism as the problems were being written, there were few realism issues noted by teachers and students in the final versions. Issues with readability and mathematical hallucinations were also somewhat rare. Implications for multi-agent systems for personalization that support teacher control are given. Comments: Paper accepted to AIED 2026 - South Korea Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2604.12066 [cs.AI]   (or arXiv:2604.12066v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.12066 Focus to learn more Submission history From: Candace Walkington [view email] [v1] Mon, 13 Apr 2026 21:10:52 UTC (280 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)