Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions - Alghamdi, Latifah Hamdan; Alghizzi, Talal Musaed (Frontiers in Education, 2025)
The rapid incorporation of artificial intelligence (AI) into higher education has established automated feedback systems as both a potential benefit and a challenge. Accordingly, this systematic study synthesizes the findings of 37 empirical investigations (2014–2024) to underscore the significance of teachers’ perspectives, which are sometimes overlooked in the use of AI-mediated feedback. Research indicates that AI can enhance customization, deliver immediate feedback, optimize repetitive processes, and increase student engagement. Nonetheless, these advantages are persistently compromised by concerns regarding algorithmic bias, data privacy, the deterioration of teacher-student relationships, and inadequate professional growth. The current evidence base is methodologically deficient, predominantly including short-term research or subjective evaluations, with just a limited number providing longitudinal data or controlled comparisons. This research distinguishes itself from previous evaluations that emphasize technology attributes or student results by integrating the FATE framework (Fairness, Accountability, Transparency, Ethics) with adoption models (TAM/UTAUT). It redefines educators as proactive mediators whose ethical choices and professional identities influence the optimal integration of AI. Thus, it contends that AI feedback should enhance, rather than replace, human teaching, and that its ongoing application depends on professional growth and strong governance frameworks. Future research must focus on longitudinal, cross-cultural, and outcome-validated approaches to shift the profession from experimental excitement to evidence-based educational change.