Enhancing Learning and Collaboration in a Unit Operations Course: Using AI as a Catalyst to Create Engaging Problem-Based Learning Scenarios

Abstract

This paper presents an innovative approach to problem-based learning (PBL) designed with the aid of ChatGPT in a Unit Operations course. Students were tasked with designing an industrial dryer for specific technological applications of social and economic relevance in Brazil, answering key learning outcomes of the undergraduate program: tackling open-ended problems, employing diverse data gathering strategies, and developing mathematical and simulation skills. One particular aspect of this PBL activity was the use of commercial process simulation software for designing and simulating the dryer. To foster a collaborative learning environment, students were divided into groups with assigned roles, which were evaluated distinctively. This approach helped enhance engagement and involvement and significantly improved learning outcomes. Over 90% of the students reported increased engagement, better teamwork dynamics, and enhanced learning. A feature of this PBL activity was the integration of generative AI (ChatGPT) in diverse simulation scenarios. ChatGPT provided key data for process simulation such as drying curves and particle size distributions, enriching the learning experience by introducing a range of realistic scenarios. This paper details the methodology, implementation, and positive educational outcomes of this approach, highlighting the potential of AI-assisted PBL in enriching chemical engineering and industrial chemistry education.