Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners

Abstract

Generative artificial intelligence (GAI) presents novel approaches to enhance motivation, curriculum structure and development, and learning and retrieval processes for both learners and instructors. Though a focus for this emerging technology is academic misconduct, we sought to leverage GAI in curriculum structure to facilitate educational outcomes. For instructors, GAI offers new opportunities in course design and management while reducing time requirements to evaluate outcomes and personalizing learner feedback. These include innovative instructional designs such as flipped classrooms and gamification, enriching teaching methodologies with focused and interactive approaches, and team-based exercise development, among others. For learners, GAI offers unprecedented self-directed learning opportunities, improved cognitive engagement, and effective retrieval practices, leading to enhanced autonomy, motivation, and knowledge retention. Though empowering, this evolving landscape has integration challenges and ethical considerations, including accuracy, technological evolution, loss of learner's voice, and socio-economic disparities. Our experience demonstrates that the responsible application of GAI's in educational settings will revolutionize learning practices, making education more accessible and tailored - producing positive motivational outcomes for both learners and instructors. Thus, we argue that leveraging GAI in educational settings will improve outcomes with implications extending from primary through higher and continuing education paradigms.

Ähnliche Arbeiten