Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study
On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning
Abstract (arXiv)
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 adapt to different learner profiles when providing mathematical instructions. Current VLMs lack a systematic evaluation framework for this adaptivity to different learner profiles in mathematics tutoring tasks. To address this gap, we draw on the learner model from the adaptive learning framework (Shute and Towle, 2018) 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 12 of its 44 references that are in the map. First posted on arXiv 15 May 2026.
Builds on
12 of 44 references.
Closest ancestors · 4
Same direction, newest first
- 2025DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Images24 Jan
- 2024A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges16 Dec
- 2024Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset22 Feb
- 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts3 Oct
Other references · 8
- 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 662
- 2024Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMscited by 46
- 2025Beyond Final Answers: Evaluating Large Language Models for Math Tutoringcited by 23
- 2024UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMscited by 21
- 2025Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Studycited by 18
- 2024Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiadscited by 16
- 2021Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Descriptioncited by 12
- 2024Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AIcited by 5
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- 2026The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors
- 2023Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning
- 2025Assessment of Evolving Large Language Models in Upper Secondary Mathematics
- 2025Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles
- 2024DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models
- 2025Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback
- 2025Seeing the Big Picture: Evaluating Multimodal LLMs' Ability to Interpret and Grade Handwritten Student Work
- 2024Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning