Leveraging Generative AI To Improve Motivation and Retrieval in Higher Education Learners (Preprint)

UNSTRUCTURED 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 key focus for this emerging technology is academic misconduct, we sought to leverage GAI in curriculum structure to facilitate educational outcomes. For instructors, GAI offers innovative tools within both core course design elements and the broader hidden curriculum while reducing backend time requirements to evaluate outcomes and providing individualized 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 voice, and socio-economic disparities. Our experience demonstrates that 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.