Traditional Lecture versus AI in Teaching Health Terminology: Gender, Technology Acceptance and Learning Autonomy

Artificial intelligence (AI) tools are rapidly shifting the way healthcare education is taught, moving from human-led teaching to adaptive, automated assistance. However, acceptance and adoption of digital learning systems are not uniform across demographic groups. This study analyzes quantitative responses from non-clinical Health Administration students at the Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam Campus (N = 114; Female n = 97, Male n = 17), evaluating the transition from tradition lecture-based to an AI-assisted learning framework. Grounded in the Technology Acceptance Model (TAM) and Self-Regulated Learning (SLR) theory, this paper evaluated gender-based differences in technology acceptance and learning autonomy in the Health Terminology course. Empirical findings show clear differences in perceived understanding between both genders. Result indicates both male and female value AI as useful for basics, however it differs in advanced application. Students reported traditional lectures (M = 4.34, SD = 0.59), while AI-assisted learning scoring at (M = 4.35, SD = 0.60), with equally high satisfaction. This confirms that both methods fulfill distinct but complementary functions in addressing the complex vocabulary and memorization demands of health terminology. Lectures provided professional context and live guidance, while AI tools offered automated accuracy, immediate feedback, and personalized reinforcement. Significantly, despite the female majority in the sample (85.1%), independent samples t tests showed no significant gender differences in perceptions of lectures (p = .196) or AI learning (p = .083). These findings suggest that the historical digital divide has effectively closed among modern undergraduate health sciences students, with both genders demonstrating similar technological readiness and confidence. The implications strongly support a blended instructional model in which AI handles repetitive vocabulary drills outside class, freeing lecture time for higher order applications such as case studies, and healthcare documentation.

Artificial intelligence, Tradition Learning, Gender, Active Learning, Health Terminology

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