Artificial intelligence as a supplementary learning tool in pharmacology tutorials: students' perceptions and descriptive knowledge outcomes

BACKGROUND: Artificial Intelligence (AI) tools are being rapidly integrated into medical education, yet discipline-specific evidence on their use in pharmacology tutorials remains limited. This study evaluated the perceptions of AI-assisted learning in pharmacology tutorials and attempted to descriptively examine the knowledge outcomes before and after the introduction of AI-based learning support.

METHODS: This quasi-experimental quantitative study was conducted among Year 1 MBBS students at Universiti Kuala Lumpur Royal College of Medicine Perak, Malaysia. Freely available AI tools, including ChatGPT accessed through the free web-based interface and other student-selected AI applications such as AI summarizers, quiz makers, flashcard generators, and slide assistants, were introduced as optional adjuncts during one two-hour end-of-module respiratory pharmacology tutorial after completion of the six-week respiratory module. Pre-test and post-test multiple-choice questions were used to summarize knowledge scores descriptively. A content-validated structured questionnaire comprising five thematic domains on a five-point Likert scale was used to assess student perceptions. Descriptive statistics were performed using means, standard deviations, frequencies and percentages. For Likert-scale items, mean scores and standard deviations were calculated for each item and the grouped domain.

RESULTS: Sixty-nine Year 1 MBBS students completed the perception survey. The Moodle view count was not used as the response-rate denominator because it reflected page/link views rather than verified eligible consenting participants. Pre-test and post-test mean scores were 9.61/10 and 9.52/10, respectively, indicating no meaningful descriptive change in knowledge. Overall perceptions of AI-assisted learning were positive (mean 4.15/5). The highest-rated benefits were improved understanding of pharmacology topics (mean 4.43), time-saving during self-study (mean 4.36), more effective doubt clarification (mean 4.31), and willingness to recommend AI tools for other subjects (mean 4.30). Students most frequently used AI to explain difficult concepts and to summarize lecture content. Though ChatGPT was rated the most helpful tool, there was concern about incorrect or outdated information. A total of 63.5% of respondents stated that they would continue using AI because it saves time and improves study efficiency.

CONCLUSIONS: AI tools were well accepted as supplementary learning resources in pharmacology education. Students valued AI for understanding complex concepts, clarifying doubts, and supporting independent study. Measurable knowledge gains were not observed, most likely owing to high baseline performance. AI should be integrated cautiously as a complement to conventional teaching, with faculty oversight to ensure content accuracy and curricular alignment. Future studies should employ matched designs, discriminative assessments, and longitudinal follow-up.