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: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...
The paper presents a tutoring platform that combines a generative AI chatbot with a reinforcement learning algorithm to adaptively sequence practice problems for students learning Python. In a five‑month field study across ten high schools, the adaptive sequencing improved unassisted final exam performance by 0.15 standard deviations, with mediation analysis indicating that higher engagement drove the gains. The study demonstrates that signals from student‑chatbot interactions can be leveraged to personalize and optimize learning at scale.
arXiv:2609.28470v1 Announce Type: new Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanc...
TutorTrace is a new dataset and behavioral abstraction pipeline that captures learners’ low‑level IDE telemetry to make their behavioral context visible and computable in real time. The dataset, collected across 480 students in two introductory Python courses, includes 180 K telemetry events, 13 633 behavioral segments, and 27 continuously computed metrics, and it underpins a taxonomy of learner activity before, between, and after AI queries. Preliminary classroom tests show that behavior‑aware prompts reduce the time between queries, and the system can predict upcoming queries with AUROC scores of .726 and .717 on two held‑out tasks.
arXiv:2604. 26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs).
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:2608. 07509v1 Announce Type: cross Abstract: LLMs are increasingly used for conversational tutoring, but effective tutoring requires more than correct answers.
arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.
arXiv:2608. 11259v1 Announce Type: cross Abstract: Many AI tutors leverage large language models (LLMs) today.
arXiv:2606. 18617v1 Announce Type: cross Abstract: There exist numerous tutor training platforms.
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:2607. 13041v1 Announce Type: cross Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them.