Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study

ACL ARR 2026 January Submission5632 Authors

Keywords: Adaptive Learning, Vision Language models, Adaptivity, Learner Model, Mathematics Education

Abstract: Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual learners' learning performance. It is increasingly recognized as critical for developing an effective learning support tool. Vision Language Models (VLMs) have seen adoption in mathematics education, and students have been using them as learning aids for personalized instruction. However, it is unknown whether VLMs have the ability to provide appropriate mathematical instruction based on different learner profiles. Current VLMs lack a systematic evaluation framework for this assessment in mathematics education. To address this gap, we draw on the learner model from the adaptive learning framework and propose a learner model-based rubric. Our rubric formalizes adaptivity assessment into three aspects: cognitive aspects, motivational aspects, and complexity. We also evaluate two additional dimensions of VLM responses: correctness (of answers and solutions) and quality (of the response itself). Our experimental results show measurable differences in adaptivity across models, and also reveal that current VLMs struggle to consistently produce learner model-based instructional responses, especially when receiving limited learner information.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: educational applications

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 5632