MedSeek_userBehavior

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.