Artificial Intelligence As a Tutor: Enhancing Self-Regulated Learning In Transportation Engineering Through AI-Driven Feedback

Abstract

Background: The contemporary civil engineering landscape, particularly in transportation, necessitates updated knowledge, skills, and practical learning approaches. This study focuses on assessing self-regulated learning strategies through Active Learning in the context of the "Fundamentals of Logistics Services" course within the civil engineering postgraduate program at Unicamp. Methods: Thirty participants, comprising master's, doctorate, and undergraduate students with an average age of 26, engaged in the study. Sixty percent attended classes in person, while the remainder attended virtually. The study employed a three-hour lesson format, integrating four instructional methodologies: Kahoot, "Design Thinking," Opposition Group, and Verbalization and Observation Group. The self-regulated learning cyclical model framed the research approach. Students received homework assignments four days before each class, with presentations by students at the lesson's onset and practical implementation of one of the approaches by researchers at the conclusion. Each lesson concluded with a spoken assessment, except for the Kahoot strategy, which underwent its evaluation. Results: The implemented strategies demonstrated positive outcomes regarding student engagement and commitment to coursework. However, challenges related to internet connectivity, particularly affecting the proper execution of Kahoot activities, were noted and criticized. Conclusion: The study underscores the efficacy of integrating self-regulated learning methodologies into transportation civil engineering education. Despite issues like internet connectivity, the positive engagement and commitment outcomes warrant continued implementation. Future iterations of the study propose involving teachers from diverse disciplines to enhance the effectiveness of self-regulated learning approaches.

Keywords: Active Learning, Teaching Method, Self-Regulated Learning, Learning Strategy.