MedSeek_userBehavior
Description
The target dataset contains de-identified, high-resolution interaction information from MedSeek, a large-language-model (LLM) platform optimised for medical education. Built on the DeepSeek architecture and fine-tuned with >200 M clinically curated instruction–response pairs, MedSeek achieves state-of-the-art accuracy on multiple medical NLP benchmarks, including MedQA (78.6 %), PubMedQA (83.9 %), MedMCQA (67.4 %), MedBullets (72.1 %), MMLU (81.2 %), MMLU-Pro (79.5 %) and CARE-QA (74.8 %). This dataset captures usage patterns from a medical education large language model (LLM) platform, representing interaction behaviors during Q2 2025. It contains anonymized observational records of platform engagement across diverse medical education contexts. #### Dataset Components: 1. **User Profiles** (`medical_llm_users.csv`) * 1,454 anonymized participant records * Role distribution: Educators (2.2%), Medical students (97.8%) * Discipline representation: Clinical Medicine (39%), Pharmacy (18%), Public Health (11%), Basic Medicine (26%), Nursing (4%), Medical Humanities (2%) * Engagement tiers: High-engagement (15%), Regular (25%), Low-frequency (40%), Dormant (20%) 2. **Session Records** (`medical_llm_sessions.csv`) * Platform access sessions with temporal metadata * Device access patterns (mobile/desktop/tablet) * Duration metrics and temporal distribution * Special annotation for examination period (May 10-24, 2025) 3. **Interaction Logs** (`medical_llm_interactions.csv`) * Question-Answer exchanges across medical domains * Six knowledge domains with topic classifications * Interaction types: Initial queries (35%), Follow-ups (25%), Answer review (20%), Clarifications (10%), Content saving (5%), Feedback (5%) * Complexity engagement metrics #### Data Harness: Data was harnessed through parameterized behavioral modeling based on established medical education frameworks. The process incorporates: * Professionally validated medical education taxonomies * Temporal usage distributions reflecting academic calendars * Device access patterns aligned with mobility studies * Knowledge domain representations mirroring standard curricula #### Potential Research Applications: * Medical education technology adoption studies * Temporal analysis of learning behaviors * Domain-specific knowledge retrieval patterns * Adaptive learning system development * Educational data mining methodology validation #### Ethical Compliance: All identifiers represent anonymized entities. Content follows medical education standards without including real patient information or personally identifiable data. Generated text reflects generalized medical education scenarios without specific case references.