Medical Student GenAI Self-Study Practices from Qualitative Interviews

A qualitative study by Olivia Ng, last updated in May 2026, examines how clinical year medical students use Generative AI chatbots in their self-study. Data includes semi-structured interviews and recorded chat interactions from twenty participants. The analysis applies an Epistemic Entanglement Framework to understand knowledge co-construction and cognitive offloading.

Use Cases

• Analyze themes of epistemic co-agency based on descriptions of student-AI interaction.
• Study shifts in self-study practices based on reported new and discontinued learning behaviors.
• Investigate perceptions of AI reliability based on participant accounts of accuracy and contextual understanding issues.

Strengths

• Includes qualitative data from twenty clinical year medical student participants.
• Data collection combines semi-structured interviews with actual recorded chat interactions with GenAI tools.
• Analysis is informed by a specific theoretical framework (Epistemic Entanglement Framework).

Limitations

• Dataset size is 36.0 KB, indicating a very limited textual scope.
• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The sample of twenty students may limit generalizability of findings.

Provenance

Source

figshare

Collection Method

Semi-structured interviews and screen-recorded chat interactions, analyzed with thematic and abductive coding.

Freshness

Last updated 2026-05-11 05:27:37; freshness should be verified.