Reframing artificial intelligence in higher education : Leveraging generative AI for student assessment and enhanced feedback
Abstract
The rapid rise of generative artificial intelligence (GenAI) in higher education has been framed largely by concerns about academic integrity, surveillance, and misuse, particularly in online learning environments. This paper challenges deficit-oriented narratives by reframing GenAI as a pedagogical partner that can enhance student assessment and feedback when intentionally designed and faculty-guided. Drawing on principles of assessment for learning, self-regulated learning, and transparency in online assessment, the paper proposes the Faculty-Guided AI Feedback Loop (F-GAIFL). This conceptual framework integrates GenAI into formative feedback processes while maintaining human judgment and instructional authority. Using an exploratory, pilot-focused perspective, the paper presents a repeatable method for incorporating AI-assisted feedback into existing grading workflows to enhance feedback quality, speed, and student engagement. Instead of providing causal insights, the study offers theoretically grounded guidance for responsible implementation, emphasising ethical considerations, faculty development requirements, and impacts on equity in online education. By framing GenAI as a scaffold rather than a substitute, this work provides a practical and scholarly foundation for future empirical research and pedagogical innovation in higher education assessment. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Patricia Y. Talbert, PhD, MPH, MS, CPHA, CHES®, cGPHN, earned her PhD in public health, specialising in community health promotion and education, from Walden University. She holds numerous nationally accredited degrees and professional certifications. Her expertise spans higher education, leadership, and public health, with a primary focus on women’s health and maternal care, behavioural change, and health promotion. Patricia’s research interests include primary prevention, the social and physical determinants of health, and maternal health. She has worked for over 35 years as an administrator and educator, holding multiple leadership positions. Patricia is a performance-driven public health leader, scholar, and educator with an extensive portfolio of publications, grants, and presentations focused on leadership and population health. Her recent work applies artificial intelligence and machine learning to predict behavioural change and improve diabetes education. She is collaborating with the National Institutes of Health and national leaders on a grant to advance Black maternal health outcomes. Patricia is a member of the American Public Health Association (APHA), the National Association of Health Services Executives (NAHSE), the Association of University Programs in Health Administration (AUPHA), and serves on the board of directors of the American College of Healthcare Sciences (ACHS). She is the author of ‘STRIVE TO FINISH The Dissertation Recipe: A Practical Guide for Graduate Students and Emerging Scholars’.
Citation
Talbert, Patricia Y. (2026, June 1). Reframing artificial intelligence in higher education : Leveraging generative AI for student assessment and enhanced feedback. In the Advances in Online Education: A Peer-Reviewed Journal, Volume 4, Issue 4. https://doi.org/10.69554/XMUS1212.
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