aceLLM assistance for mathematical modeling by preservice teachers in a German university course
Source article: 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
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…
Contested: both sides are scored from claims and sources, not community votes.
"student_ipad_school - 232" by flickingerbrad, CC BY 2.0.
By April 2026, researchers studied 150 mastere28099s-level preservice teachers at a German university as they collaboratively solved three authentic mathematical modeling problems with large language models, analyzing interaction worksheets, surveys, and interviews.
The pattern matters for teacher education because efficiency in early modeling phases coexisted with concerns about inaccuracies and overreliance, leaving open how to teach critical AI literacy and validation skills for responsible classroom use.
- Study analyzed 150 preservice teachers in a mastere28099s-level mathematics course at a German university working on three authentic modeling tasks.
- Data included worksheets documenting LLM-user interactions, open-ended survey responses, and semi-structured interviews.
- LLMs were most used in mathematization and understanding/simplifying phases to support assumption-making and model development.
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.
The rundown
The analysis drew on worksheets of LLM interactions, surveys, and interviews from 150 mastere28099s students at a German university tackling three authentic tasks.
Perceived affordances centered on real-time explanations and reasoning support, while challenges centered on trustworthiness judgments and reduced utility for validation and reflection.
Findings are bounded to a single teacher-education context and task set, with limited support observed for reflective modeling work.
Sources
- Peer-reviewedZDM – Mathematics Education2026-04-07
How should this claim be treated?
The debate