Evaluating language models as student simulators currently focuses on whether their dialogue looks realistic, yet it is unclear whether these models actually respond to the quality of teaching the way human students do. However, this has not deterred prior works from using a model’s uncertainty, measured as the log-likelihood of a fixed correct answer, as a proxy for student behaviour. This approach has been used to simulate student responses, estimate question difficulty, and study “learning". In this work, we test whether this proxy can detect sensitivity to instructional quality. We evaluate five language models on expert-rated tutoring dialogues under tightly controlled conditions. All models reliably distinguish relevant tutoring context from irrelevant context. However, this contextual sensitivity does not extend to pedagogical sensitivity: higher-quality instruction almost never increases the likelihood of the correct answer, and reasoning-oriented models sometimes show the opposite pattern. We argue that this failure is structural, and that exact token-level likelihood is too strict a measure as many different reasoning paths can be correct as opposed to just one fixed correct answer. Our results therefore question the use of target-answer likelihood as a validation metric for LLM-based student simulators and advocate for more robust metrics for learning proxies.

Multilingual dictionaries are among the most valuable documentary resources for low-resource and endangered languages, yet many remain available only as scans. For many decades their digitization and conversion into a machine-readable format was nearly impossible due to language-specific scripts, complex multi-column layouts full of entries with abbreviations and cross-references. Recent vision-language models offer a promising solution, but it is unclear how well they preserve characters, markup, and process lexicographic structure. We introduce MUDIDI, a two-stage framework for multi-lingual dictionary digitization. Stage 1 evaluates the quality of character recognition and markup preservation; Stage 2 focuses on dictionary entry segmentation with subsequent mapping into a machine-readable lexicographic schema, SIL’s Multi-Dictionary Formatter. We release a dataset that consists of human-annotated lexicographic entries collected from 30 public-domain dictionaries featuring diverse writing systems, language families, and formats. We benchmark OCR systems, general-purpose Large Language Models (LLMs), and Vision Language Models (VLMs) on the dataset, demonstrating superior performance of LLMs across most writing systems and languages in both stages, and provide practical guidelines on improving the results for more challenging scripts. Finally, we show that supplementing additional information such as dictionary introduction to the LLM can help improve the quality of the digitized dictionary.