Educating Humans, Training Machines: From Doing to Becoming in the Age of Artificial Intelligence

Artificial intelligence (AI) is rapidly reshaping higher education. Generative AI systems can summarize texts, generate written work, provide feedback, assist in problem solving, and support increasingly complex cognitive activities across disciplines. While these technologies create significant opportunities for teaching and learning, they also challenge long-standing assumptions about the relationship between academic task performance and evidence of learning. This paper argues that the central challenge for higher education is not simply integrating artificial intelligence into the classroom but reconsidering the purpose of education through a shift from task-oriented learning focused on “doing” toward developmental learning centered on “becoming.” Drawing upon Bloom’s Taxonomy, experiential learning, transformative learning, situated learning and research on human–AI collaboration, this paper develops a conceptual framework for examining the changing relationship between cognitive task performance and learner development in AI-augmented educational environments. Rather than assigning lower-order cognition to artificial intelligence and higher-order cognition exclusively to humans, the framework recognizes that AI can support activity across cognitive levels while distinguishing successful task performance from the intellectual development of the learner. The paper argues that the educational value of AI ultimately depends on whether its use contributes to the development of judgment, agency, reflection, ethical responsibility, creativity, and intellectual identity. Implications for curriculum design, assessment, faculty development, and institutional policy are discussed.