Evaluating an AI-Enhanced Elective Program for Clinical Medical Students: Insights from Student Feedback on a Renal Dialysis Module

Abstract

Background: This paper assesses an AI-based enhanced renal dialysis elective course on clinical medical students by examining student feedback to lecture material, teaching format and the delivery of the program. Thematic text analysis using qualitative feedback and descriptive pattern recognition using quantitative data were incorporated into the evaluation process to enable a structured finding of strengths, weaknesses, and areas of curricular improvement using artificial intelligence (AI). Students identified benefits of interdisciplinary learning, technical learning and useful strategies of engagement. Nonetheless, they also reported such issues as lack of visual materials, excessive complexity of the material, and no chance to practice in real life.Purpose: The purpose of the assessment is to evaluate the effectiveness of a renal dialysis program consisting of an elective program with the help of AI-supported feedback analysis to determine the strengths and weaknesses of the educational program and the opportunities to improve the program.Procedure: It was a qualitative descriptive study, which included five fourth-year MBBS students. Open-ended forms, structured logbooks and supervisor evaluations were used to collect the data that were further processed by qualitative coding tools based on AI-driven thematic clustering and visualization.Findings: Lectures on haemodialysis, CKD screening, intradialytic complications, management of long-term complications, and cardiovascular risk factors seemed to be well-received. The analysis of the collected data with the help of AI was confirmed to have recurrent themes, such as the lack of visual aid, outdated references, as well as insufficient use of case-based methods. The suggestions were incorporation of simulations, betterment in multimedia teaching aids as well as fine tuning lecture pace.Conclusion: The results of the evaluation process with the integration of AI were more comprehensive and revealed the necessity to improve teaching aids, improve the design of lectures, and increase the correspondence between theory and practice. Such results indicate that AI can be useful in the assessment of medical curricula, including the creation of more interactive and centred clinical electives to students.

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