Generative AI in distance higher education: pedagogical opportunities, controversies, and research priorities

Abstract

Generative artificial intelligence (GenAI) is changing how academic support is delivered in distance higher education through on-demand explanation, feedback, content generation, and dialogue. The educational value of these capabilities depends on task design, students’ capacity for self-regulation, instructor involvement, and institutional safeguards. Drawing on recent scholarship on GenAI and related applications of artificial intelligence in online and hybrid university education, this Mini Review develops a position through critical discussion of learning opportunities, dependence, academic integrity, and pedagogical governance, and reports no original empirical findings. The literature identifies opportunities for personalized support, timely feedback, accessibility, and engagement, alongside concerns about inaccurate output, cognitive offloading, privacy, bias, unequal access, and assessment validity. Debate centers on whether expanded support may, when used uncritically, erode independent reasoning or obscure authorship. Available evidence favors carefully designed, transparent integration over either unrestricted use or blanket prohibition. Priorities for practice include authentic assessment, explicit AI literacy, coherent institutional policy, and human oversight. Research now needs to move beyond short-term perceptions and performance measures by examining retention, transfer, equity, and student agency longitudinally, particularly in fully online programs.