Tanvi Namjoshi will present her MSE thesis "Major Decisions: Evaluating LLM Homogenization and Personalization in Educational Advisory Tasks" on Friday, April 24th, 2026 in CS 401 at 2:30 pm. Advisor: Lydia T. Liu Reader: Manoel Horta Ribeiro All are welcome to attend. Abstract: Large language models (LLMs) are increasingly being used for advisory tasks in high-stakes settings such as education. While researchers have evaluated their agreement with human advisors, there has been limited work looking at the challenges that emerge with adoption at scale. Unlike factual question-answering, where homogeneous outputs are desirable, and creative generation, where scale is rarely a concern, advisory tasks require recommendations that are both individually relevant and collectively diverse. In this work, we introduce a formal evaluation framework for homogenization and personalization for this class of problems, and apply it to the task of undergraduate major selection. To study the homogenization of responses we use a Shannon entropy-based approach to study 17 open-source LLMs across three levels of provided student context. We find that global recommendation entropy remains below our real-world enrollment baseline across all context levels, and several majors -- disproportionately humanities disciplines -- are never recommended by most models. While models are semantically responsive to the individual student profiles, they exhibit systematic narrowing; across all models, over 50% of the recommendations are concentrated within the top five majors per model. These findings suggest that naive prompt-based advising is insufficient for institutional deployment and highlight the need for population-level metrics to complement capability assessments in future AI system evaluations.