The paper introduces an interpretable, difficulty-aware knowledge tracing framework for tutor‑student dialogues that leverages large language models. It explicitly models student ability and question difficulty at each turn, using the original question text and the next tutor‑posed task to estimate knowledge state. By integrating Item Response Theory, the framework translates LLM outputs into cognitive‑theory‑based parameters, achieving superior performance over existing baselines while producing interpretable predictions.
arXiv:2610.19532v1 Announce Type: cross Abstract: Existing student models in educational applications largely focus on student responses to questions. However, as tutoring becomes more accessible at...
arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.
The paper introduces Practical Integrated Cross-consistent Knowledge Tracing (PICKT), a model that incorporates multiple feature types to improve Knowledge Tracing robustness when new questions lack interaction history. It evaluates the impact of difficulty, textual, and knowledge‑map relational features, finding that difficulty is especially informative for hard questions, while fused text and map features help estimate unseen questions by leveraging similar ones seen during training. The study concludes that prioritizing feature annotation aligned with educational service characteristics is essential for maintaining robust diagnostics in Intelligent Tutoring Systems.
arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students u...
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.
arXiv:2606. 10736v1 Announce Type: cross Abstract: Large online courses generate thousands of student questions directed at conversational AI teaching assistants, yet these interaction logs remain largely untapped as diagnostic signals.
The paper proposes a response‑free method for estimating difficulty of reading‑comprehension multiple‑choice items by fine‑tuning a transformer on item wording. It introduces two extensions to a baseline joint‑encoding model: a component‑wise variant that encodes passage, question, and options separately, and a multi‑task variant that adds a question‑answering auxiliary task. Experiments on a corpus of nearly 30,000 items show that both extensions outperform the baseline, especially the multi‑task variant across all metrics and the component‑wise variant in rank ordering, even with limited training data.
arXiv:2603. 02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions.
TutorCaseBench is a new benchmark designed to evaluate large language model (LLM) tutors on their ability to support students exhibiting challenging behaviors. It is built from qualitative case studies in pedagogy, educational psychology, and special education, converting documented student categories into 923 case-specific dialogues and rubrics. The benchmark tests 11 state‑of‑the‑art LLMs on open‑ended response generation and multiple‑choice selection, finding that even the best model meets rubric criteria only 55.8% of the time for open‑ended cases, highlighting a gap between recognizing interventions and adapting to context.
arXiv:2605.30051v2 Announce Type: replace Abstract: A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as stude...
arXiv:2511. 21692v3 Announce Type: replace-cross Abstract: We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation.
arXiv:2602. 02414v2 Announce Type: replace-cross Abstract: Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors.