Listen and Learn! Enhancing Construction and Engineering Education with AI-Assisted Podcasts

Abstract

Emerging technologies provide promising opportunities to improve learning in construction and engineering education. In the current study, the interaction between students and AI-enhanced educational podcasts in undergraduate and postgraduate modules offered by two South African universities is analysed. Using a quantitative research design, the study noted a considerable increase in class average scores at the postgraduate level. However, the undergraduate class average scores remained at baseline, indicating no apparent improvement; therefore, no causation can be established based on these results. Furthermore, guided by the Cognitive Theory of Multimedia Learning, the Technology Acceptance Model (TAM), and Self-Determination Theory (SDT), student perceptions were assessed across learning effectiveness and engagement, accessibility and satisfaction, and motivation and intent to continue using podcasts. Rankings showed high satisfaction across all dimensions. Although access challenges were infrequent, PLS-SEM revealed a significant negative relationship between technical barriers and motivation and willingness to expand podcast use, highlighting human–technology interaction constraints. Findings underscore the importance of seamless access and curriculum alignment and conclude with strategies to strengthen adoption, engagement, and learning outcomes in AI-assisted podcast-based education.