AI as a differentiated cognitive partner in medical education: a comparative study of popular science versus research-based curriculum extensions in histology and embryology
Year: 2026
Type: article
Study design: Clinical Trial
Abstract: OBJECTIVE: This study compared the efficacy of Popular Science (PS) and Research-Based (RS) curriculum extensions in a Histology and Embryology course, and explored how artificial intelligence (AI) tools can be designed as differentiated cognitive partners to support distinct pedagogical goals. METHODS: Two parallel first-year clinical medicine classes (N = 118) were non-randomly assigned to PS (n = 60) and RS (n = 58) arms. The PS group completed narrative, clinically connected tasks supported by the Intelligent Quiz Platform (IQP). The RS group undertook inquiry-driven literature analysis supported by the Literature Analysis Assistant (LAA). Outcomes included examination scores, validated questionnaire ratings, AI usage logs, and semi-structured interview themes. RESULTS: The RS group was associated with higher comprehensive application (16.8 ± 2.6 vs. 14.2 ± 2.9, p < 0.01) and innovative thinking scores (9.0 ± 1.6 vs. 7.6 ± 1.8, p < 0.01). The PS group reported significantly higher learning interest and satisfaction (p < 0.01). The PS group used IQP more frequently (8.5 ± 2.1 vs. 5.2 ± 1.8, p < 0.001), while the RS group engaged more deeply with LAA (4.8 ± 1.5 vs. 1.2 ± 0.9, p < 0.001). IQP use correlated with basic knowledge (r = 0.42, p < 0.01); LAA use correlated with comprehensive application (r = 0.51, p < 0.001) and innovative thinking (r = 0.46, p < 0.01). Interviews confirmed AI acted as a "tutor" for PS and a "research assistant" for RS. CONCLUSION: PS and RS extensions are associated with complementary competencies. A tiered, AI-differentiated model may support personalized learning to cultivate clinically engaged communicators and critically thinking physician-scientists.
Source: BMC Medical Education
Authors: Dejian Chen, Lin Ma, Shengbing Liu, Yaling Zhang
Institution: Jiaxing University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7160641812