JMIR Publications

ABSTRACT

Background:

Clinical reasoning competency development is central to veterinary education. Generative artificial intelligence (GenAI) opens new possibilities for supporting students in acquiring these competencies, yet its effectiveness as a reasoning support tool in case-based learning (CBL) remains unclear.

Objective:

This study examined whether a commercially available GenAI chatbot could support veterinary students in CBL and evaluate its potential for clinical reasoning training.

Methods:

Following systematic evaluation, Microsoft Copilot was selected for its accessibility, functionality, and data protection compliance, and students were provided with a user-oriented manual including prompt instructions. In an interventional crossover study involving 60 fourth-year veterinary students at a Swiss university, participants alternated between AI-supported and traditional case-based learning (CBL) across four clinical cases. Clinical reasoning outcomes were assessed by a dedicated lecturer per case using 34 scored items, complemented by student surveys and lecturer reflections.

Results:

Clinical reasoning outcomes showed no meaningful evidence of a difference between AI-supported and traditional CBL groups (W = 6719, p = 0.464), with results varying across cases. Post-class surveys (n = 38) indicated that most students viewed GenAI support positively: 68% agreed the AI provided relevant inputs they had not previously considered, 58% perceived reduced task difficulty, and 61% found the AI-generated starting point effective. However, 45% also reported negative effects on case understanding and dissatisfaction with the overall learning experience. Qualitative feedback highlighted benefits such as information retrieval and stimulation of reflection, alongside limitations related to superficial or inaccurate AI outputs.

Conclusions:

These findings indicate that AI integration alone is insufficient to enhance clinical reasoning in case-based learning. Without sufficient AI literacy on top of developing clinical competencies, the cognitive demands of verifying AI-generated outputs may offset potential benefits in complex reasoning tasks. Tailoring AI integration to learner experience, scaffolding, and prior AI exposure appear relevant to realizing GenAI's potential for clinical reasoning development in veterinary education.