Comparing conventional and generative AI-assisted task performance in physiology education

Area of Science:

• Medical Education
• Educational Technology
• Physiology

Background:

• Generative artificial intelligence (AI) presents new opportunities and challenges in medical education.
• Understanding the impact of AI on student performance is crucial for effective curriculum integration.

Purpose of the Study:

• To evaluate the educational impact of generative AI on medical students' performance in a physiology laboratory course.
• To compare assignment scores from AI-assisted report writing with conventional methods.
• To identify factors associated with effective generative AI use in this context.

Main Methods:

• Medical students completed a physiology assignment using both conventional resources and generative AI tools.
• Reports were scored using a keyword-based system to assess core conceptual elements.
• Student perceptions and prior AI experience were gathered via questionnaires.

Main Results:

• No significant difference was found between scores obtained using conventional methods versus generative AI (p = 0.54).
• Scores from both methods showed a moderate positive correlation (r = 0.465).
• Prior performance with conventional resources was the strongest predictor of AI-assisted outcomes.

Conclusions:

• Generative AI does not fundamentally alter task performance in this educational setting; it reflects existing learner understanding.
• Effective integration of generative AI in physiology education requires prioritizing foundational knowledge.
• Emphasis should remain on core concepts rather than solely relying on AI tools.