Integrating Generative Artificial Intelligence and Augmented Reality Within a Structured Project-Based Learning Framework for Unit Operations Education

This study examines the impact of integrating Generative Artificial Intelligence (AIgen) and Augmented Reality (AR) within a Project-Based Learning (PBL) framework to enhance student motivation and active learning in Chemical Engineering Unit Operations (OU) courses. The research employed a quasi-experimental post-test design without a control group and followed a mixed-methods explanatory approach. The intervention was implemented over two academic semesters at the Universidad Técnica Particular de Loja. The instructional design was structured using the 4PADAFE model, based on the principles of constructive alignment and competency-based education. The intervention was organized into seven sequential phases that integrated pedagogical planning, technological implementation, and evaluation. Students collaboratively developed educational resources, including interactive AR visualizations and AI-generated teaching materials addressing key topics such as fluid flow, heat transfer, and adsorption kinetics. The quantitative component constituted the main analytical approach and assessed changes in motivation using the Instructional Materials Motivation Survey (IMMS), based on Keller’s ARCS model. The instrument’s reliability was supported by high internal consistency (Cronbach’s α > 0.95) and satisfactory factor adequacy indices. The results revealed increases in all motivational dimensions, with mean scores rising from 3.26–3.33 in Phase I to 3.48–3.60 in Phase II. Overall, the findings indicate that the structured integration of AIgen and AR within an aligned problem-based learning (PBL) framework was associated with improvements in student engagement, confidence, and satisfaction. The study provides evaluative evidence supporting the implementation of technology-enhanced teaching strategies in engineering education under real-world classroom conditions.