Bridging Student Expectations and Instructor Capacity: Leveraging General Artificial Intelligence to Enhance Quality Online Learning - Weber, Nicole L.; Gay, Kristen (Innovations in Pedagogy and Technology, 2025)
This study centers student perspectives on quality digital, blended, and online learning through a secondary qualitative analysis of panel data originally collected by the Online Learning Consortium and Every Learner Everywhere. Drawing on transcripts from four student panels featuring twelve learners from diverse academic and institutional contexts, previously identified themes were revisited to explore how Generative Artificial Intelligence (GAI) might support the elements of quality that students defined. Using a conceptual mapping process grounded in current discussions on GAI in education, areas were examined where GAI could realistically enhance the instructional strategies students most valued. Three key themes guided this exploration: structuring learning experiences for success, designing intentionally and deeply, and keeping humans and connection at the center of learning. In these themes, students emphasized the need for clarity, purpose, flexibility, and authentic connection in digital learning environments. In response, a set of student-informed insights and practical recommendations is offered, illustrating how GAI might support instructors in meeting these expectations without overly increasing workload—by streamlining course design, enhancing communication, fostering collaboration, and generating real-world learning experiences that move beyond busywork. At the same time, attention is given to ethical considerations and potential risks associated with GAI to maintain responsible and transparent instructional use. Rather than prescribing specific tools or models, this study invites a thoughtful, values-driven approach to integrating GAI in support of human-centered teaching.