Teaching and Learning in the Age of Generative AI: Rethinking the Educational Cycle

This article explores the integration of generative artificial intelligence (AI) into the core processes of education—learning, practice, and testing—conceptualized as an interconnected educational cycle. It situates this development within a broader historical context of media revolutions, arguing that generative AI introduces a novel shift by enabling machines not just to store or transmit, but to produce information. In the learning phase, the article examines how generative AI can enhance comprehension and retention by supporting three interrelated aspects: accounting for students’ prior knowledge, designing effective instructional materials, and implementing appropriate learning and teaching strategies. In the practice phase, five instructional functions of generative AI are identified: diagnosing prior knowledge, generating and storing varied tasks, selecting appropriate exercises, managing their timing and order, and providing personalized feedback. In the testing phase, AI is examined as a tool for generating valid and diverse test items, and for grading student work—including diverse testing formats. Together, these developments call for hybrid models that integrate human expertise with AI capabilities to ensure that educational quality, equity, and integrity are not only preserved, but meaningfully enhanced. Keywords: Generative Artificial Intelligence ; Learning ; Prior Knowledge; Instructional Design ; Learning and Teaching Strategies; Deliberate Practice ; Assessment ; Hybrid Intelligence