Abstract
Digital pedagogy has moved from the periphery of educational innovation to its
very center as artificial intelligence and automation reconfigure how knowledge is
produced, accessed, assessed, and accredited. This paper argues that digital
pedagogy in the era of AI is not merely a translation of traditional instruction into
online spaces but a paradigmatic reimagining of teaching, learning, and academic
work. It locates the shift at the intersection of four concurrent forces: the
algorithmic mediation of content and interaction; the datafication of learning
processes; the platformization of curricula, assessment, and credentials; and the
ethical imperatives embedded in human–machine collaboration. Rather than
treating AI tools as neutral instruments, the paper frames them as sociotechnical
actors that shape epistemic practices, redistribute authority, and foreground
questions of equity, inclusion, and academic integrity. It examines how generative
AI, predictive analytics, and automation are altering instructional design,
formative and summative assessment, feedback cycles, and student agency. The
argument advances a model of human–AI complementarity in which teachers
leverage machine capabilities for personalization, analytics, and routine task
automation while intentionally cultivating higher-order thinking, creativity,
dialogue, and care—the uniquely human dimensions of education that resist
mechanization. The analysis elaborates how classrooms can be restructured
around inquiry-based learning, authentic assessment, and scaffolded
metacognition to mitigate shortcut learning and surface plagiarism or overreliance
on machine-generated responses.
very center as artificial intelligence and automation reconfigure how knowledge is
produced, accessed, assessed, and accredited. This paper argues that digital
pedagogy in the era of AI is not merely a translation of traditional instruction into
online spaces but a paradigmatic reimagining of teaching, learning, and academic
work. It locates the shift at the intersection of four concurrent forces: the
algorithmic mediation of content and interaction; the datafication of learning
processes; the platformization of curricula, assessment, and credentials; and the
ethical imperatives embedded in human–machine collaboration. Rather than
treating AI tools as neutral instruments, the paper frames them as sociotechnical
actors that shape epistemic practices, redistribute authority, and foreground
questions of equity, inclusion, and academic integrity. It examines how generative
AI, predictive analytics, and automation are altering instructional design,
formative and summative assessment, feedback cycles, and student agency. The
argument advances a model of human–AI complementarity in which teachers
leverage machine capabilities for personalization, analytics, and routine task
automation while intentionally cultivating higher-order thinking, creativity,
dialogue, and care—the uniquely human dimensions of education that resist
mechanization. The analysis elaborates how classrooms can be restructured
around inquiry-based learning, authentic assessment, and scaffolded
metacognition to mitigate shortcut learning and surface plagiarism or overreliance
on machine-generated responses.