Enhancing Student Engagement through AI-Enabled Simulated Case Activity: An Empirical Study

Description

This study utilises interactive, decision-based learning experiences in tertiary education to investigate the role of generative AI in enhancing student engagement. OpenAI was employed to simulate an international business case that was embedded within an H5P branching scenario. Students (N = 164) were tasked with making iterative market entry strategy decisions and were provided with immediate feedback at each decision point to guide them to the optimal outcome. To address learners’ limited avidity for long-form cases, the design is grounded in experiential theory, which further promotes affective involvement, cognitive immersion, and behavioural participation. The influence of AI-enabled activity design on affective, cognitive, and behavioural engagement, as well as its collective outcome on student satisfaction, was tested through a structural equation model. Results highlighted a full mediation effect through engagement, where all hypotheses were supported except the direct relationship between activity design and satisfaction. This advances the use of AI technology in digital pedagogy, sparking engagement as a driver of satisfaction. The study reframes AI as a co-creator of pedagogical resources, thus theoretically contributing to current literature. Practically, it also proposes AI as a facilitating tool for developing immersive, feedback-driven learning activities. The study further necessitates professional development and targeted training for educators to garner skills in applying AI programs for content creation. Future research should investigate various contexts and the long-term impact to address the limitation of relying on a single cohort and platform. This study advocates for active, experiential learning through the reconceptualisation of AI use in curriculum design.

Period	2025
Held at	Bryant University, United States, Rhode Island
Degree of Recognition	International