aceThe use of large language models to solve mathematical modeling problems: preservice mathematics teachers’ use practices, perceived affordances and challenges, and trustworthiness judgments of AI-generated outputs
Abstract Mathematical modeling is a core component of mathematics education, enabling learners to connect mathematical ideas with real-world phenomena. Yet, it remains challenging for preservice teachers (PSTs), who must develop their own modeling competence while preparing to guide future students in reasoning about authentic situations. Large language models (LLMs) offer promising, though underexplored, potential to scaffold these complex learning processes by providing real-time explanations, supporting reaso…
Preservice teachers reported that LLMs helped during early modeling work by supporting assumption-making, formula retrieval, and building models and solution strategies.
Same preservice teachers raised concerns that LLM outputs contained inaccuracies, encouraged overreliance, and were less useful for validation phases of modeling.
Findings are bounded to a single teacher-education context and task set, with limited support observed for reflective modeling work.
Evidence
- Peer-reviewedZDM – Mathematics Education2026-04-07
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Truvace Impact Record TRV-2026-0148, v1: “The use of large language models to solve mathematical modeling problems: preservice mathematics teachers’ use practices, perceived affordances and challenges, and trustworthiness judgments of AI-generated outputs.” Truvace, 2026-07-13. /record/TRV-2026-0148 (accessed at citation time). sha256 5919f938271c030b…
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