arXiv:2610.27448v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into educational technologies for personalized support, creating opportunities for real-time learner modeling from dialogue. This study evaluates whether GPT-4o and Gemini 2.0 Flash can infer three Felder-Silverman Learning Style Model dimensions (Active-Reflective, Sensing-Intuitive, and Sequential-Global) from short, task-focused student-AI problem-solving dialogues. Using questionnaire-derived Index of Learning Styles labels from 153 undergraduate participants, we compared deterministic zero-shot and few-shot prompting under five-fold cross-validation. Although accuracy and support-weighted F1 were moderate for Sensing-Intuitive and Sequential-Global, majority-class baselines, macro-F1, balanced accuracy, and per-class recall showed that performance was often driven by dominant-label prediction rather than reliable recognition of both classes. The models substantially under-identified Intuitive and Global learners; few-shot prompting produced mixed effects and sometimes amplified majority-class bias. Active-Reflective classification remained unstable across models. These findings indicate that LLM-generated learner-style labels should be treated as uncertain, bias-prone signals rather than stable learner profiles, and that dialogue-based personalization systems require bias-aware evaluation and uncertainty-sensitive design.