부산대학교 도서관

Background: Artificial intelligence (AI) applications have been implemented across all levels of education, with the rapid developments of chatbots and AI language models, like ChatGPT, demonstrating the urgent need to conceptualize the key debates and their implications for a new era of learning and assessment. This adoption occurs in a context where AI is dramatically remapping "the human," the purposes of schooling, and pedagogy. Focus of Study: The paper examines how different formulations of "human" became interwoven with the sliding signifier of "intelligence" through a series of violent exclusions, and how the shifting contour of "intelligence" produces uneven and unjust ontological scales undergirding both education and AI fields. Its purpose is to engage the education research community in dialogue about biases, the nature of ethics, and decision-making concerning AI in education. Research Design: This paper adapts a historical-philosophical method. It traces the effects of colonialism and racialization within humanism's emergence through Sylvia Wynter's historiography of "figure of Man," especially via the invention of "intelligence," which has linked education and computer science. It also investigates themes central to modern education such as justice, equity, and in/exclusion through a philosophical examination of the ontological scales of "human." Conclusions: After outlining how "intelligence" has shifted from reason-as-morality to concepts of natural intelligence, we argue that current examples of AI in Education (AIEd), like classroom chatbots and social agents, constitute an intermediary point in the arc toward a new computational superintelligence--the emergence of man3--illustrating the opportunities, risks, and ethical issues in pedagogical applications based on emotion. We outline three differing visions of AIEd's future, concluding with a series of provocations (onto-epistemological, practice-based, and purposes of schooling) that exceed such models and that, given rapid innovations in machine learning, require urgent consideration from multiple stakeholders.