Beyond Benchmarking: Scenario-Based Evaluation of Large Language Models for Personalized Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
The paper introduces a prompt‑engineering framework that personalizes large language model (LLM) teaching assistants across disciplines by tailoring responses to six learner‑specific dimensions, creating 96 distinct learner profiles. It also analyzes student queries through Bloom’s Taxonomy to gauge cognitive complexity, encoding both learner attributes and cognitive assessments into structured prompts that condition the LLM without retraining. Experiments using NLP metrics and a small human study demonstrate that this approach yields perceptible differences in response style and structure, with statistical evidence linking specific learner attributes to measurable changes.
EduDial is a large-scale multi-turn teacher‑student dialogue corpus covering 345 core knowledge points and 34,250 dialogue sessions, designed around Bloom’s taxonomy and ten questioning strategies such as situational, ZPD, and metacognitive questioning. The dataset includes differentiated teaching strategies for students at varying cognitive levels to provide targeted guidance. Using EduDial, the authors trained EduDial‑LLM 32B and introduced an 11‑dimensional evaluation framework that measures teaching quality and content quality, showing that most mainstream LLMs struggle with student‑centered teaching while EduDial‑LLM outperforms all baselines across all metrics.
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RegulaTutor is a theory-informed framework for long‑term personalized tutoring that links a learner’s historical evidence to instructional support. Guided by self‑regulated learning research, it uses a cognitive, affective, metacognitive, and motivational (CAMM) framework to derive evidence‑supported learner interpretations and teaching policies that are updated as new observations arrive. The framework includes PairRoute, an offline refinement procedure that distinguishes harness, model, and mixed failures, proposes targeted harness revisions, and constructs stage‑level supervision for confirmed model failures, improving historical‑evidence accuracy, learner diagnosis, and teaching quality across evaluated model configurations.
arXiv:2604. 26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs).