Adaptive Agentic Tutoring with Evolving Learner Profiles

Tutoring a learner through a question and choosing what they should practice next require different operations, but both depend on the same course material and evidence of that learner’s difficulties. We present DeepTutor, which separates problem tutoring from practice generation while sharing course retrieval and an evolving learner profile revised from interaction traces. The tutoring pipeline investigates, solves with tools, and writes an evidence-backed explanation; the practice pipeline selects a learning target before generating and validating question–answer–explanation items. We introduce TutorBench to evaluate both outputs in multi-turn dialogue: 270 tasks across five disciplines pair source-grounded knowledge gaps with 90 benchmark learner specifications, and a first-person student simulator elicits tutoring and follow-up practice. Experiments on TutorBench show that DeepTutor attains the highest average scores for both tutoring and generated practice. Ablations show that course grounding helps explanations and practice stay faithful to source material, while the evolving learner profile helps tailor them to learners’ difficulties. In a blind comparison with an agentic tutoring baseline, human raters and the LLM judge both favor DeepTutor across all ten evaluation dimensions. These results support coordinating distinct tutoring and practice pipelines through shared course evidence and an evolving learner profile.