Key points are not available for this paper at this time.
The proliferation of generative AI (genAI) has sparked debate on its integration into university curricula, particularly in engineering education. This paper examines the adoption of genAI-based teaching tools in an undergraduate chemical engineering course, within the theoretical scaffold of the Unified Theory of Acceptance and Use of Technology (UTAUT). We investigate the voluntary use of custom GPT-powered chatbots, designed to mirror simulated industry consultants and viva/defence interviews within engineering safety case study activities. Through an exploratory mixed-method approach, utilising student surveys and tracking direct software usage, we have identified high student uptake of the AI-tools, implying effective integration into the student learning practices. We identify Performance Expectancy as the most significant factor influencing usage, with concerns around the AI-tools’ accuracy and scope functioning as potential barriers to wider uptake. Interestingly, a subset of students expressed a preference for AI teaching tools over traditional in-person interactions, indicating an overlooked potential for AI in addressing social anxiety as a barrier to learning, for some students. This offers an exciting new avenue for adaptive and personalised learning experiences, to enhance student engagement, access and success.
Honig et al. (Thu,) studied this question.