2026-10-14T04:51:30Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33949147 2026-09-21T04:30:20Z category_19 portal_316 item_type_3 month_year_09_2026

Data Sheet 4_Evaluation of a multi-component AI-guided mechanistic reasoning practical in experimental pharmacology: a single-group pre-post study.pdf Maan H. Harbi (20896504) Pharmacology antithrombotic pharmacology artificial intelligence experimental pharmacology mechanistic reasoning pharmacology education pharmacy education <p>Background. Experimental pharmacology requires students to interpret laboratory findings, identify affected physiological pathways, and infer underlying drug mechanisms. While artificial intelligence (AI) is increasingly explored in health professions education, concerns remain regarding passive learning and overreliance on generated answers. This study evaluated the feasibility, acceptability, and short-term outcomes of a structured, multi-component practical designed to support active reasoning in antithrombotic pharmacology. Methods. A single-group pre-post educational intervention was conducted among 44 second-year PharmD students. The intervention included a baseline assessment, an experimental logic mini-brief, an AI-supported mechanistic reasoning practical using a standardized reasoning prompt, an independent post-test, and a perception survey. The primary outcome was total assessment score change. Secondary outcomes included domain-specific scores, performance during the AI-supported practical, confidence, perceived AI helpfulness, student perceptions, and associations between practical performance and learning outcomes. Results. Total assessment scores improved significantly from 76.82% ± 25.06% at baseline to 85.80% ± 21.27% after the intervention, with a mean gain of 8.98 ± 20.53 percentage points (Wilcoxon signed-rank test, Z = −2.597, p = 0.009, r = 0.39). Significant pre-post improvements were observed in coagulation pharmacology (p = 0.001, r = 0.49) and a smaller uncorrected improvement occurred in platelet pharmacology (p = 0.048, r = 0.30), while experimental reasoning scores increased numerically but did not reach statistical significance (p = 0.219, r = 0.19). Among AI-supported practical completers (n = 43), the mean Step 3 reasoning score was 17.88 ± 4.01 out of 21 (85.1% ± 19.1%). Accuracy was highest for experimental interpretation (87.0%), followed by mechanism or pathway identification (85.0%) and drug-class identification (83.4%). Step 3 performance was positively associated with post-test score (Spearman’s rho = 0.606, p < 0.001). The core perception and acceptability items showed excellent internal consistency (Cronbach’s alpha = 0.935), and student perceptions were generally favorable. Conclusion. A multi-component pharmacology practical containing an AI-guided reasoning prompt was feasible, acceptable, and associated with improved short-term assessment scores. Given the single-group pre-post design, we cannot isolate the specific AI contribution from co-occurring instructional elements, such as the mini-brief, case-based practice, and retrieval-practice testing effects. Controlled studies are needed to assess causal efficacy and long-term retention.</p> 2026-09-21T04:30:20Z Dataset Dataset 10.3389/fphar.2026.1937176.s005 https://figshare.com/articles/dataset/Data_Sheet_4_Evaluation_of_a_multi-component_AI-guided_mechanistic_reasoning_practical_in_experimental_pharmacology_a_single-group_pre-post_study_pdf/33949147 CC BY 4.0