A case study: rethinking “Average Intelligence” and the artificiality of AI in academia

Abstract

Abstract This paper presents a critical conceptual and empirical analysis of artificial intelligence (AI) in higher education. It challenges prevailing assumptions about intelligence, learning, and assessment, and redefines AI not as artificial intellect but as a technological reflection of institutional norms, referred to here as "Average Intelligence." As AI tools become increasingly embedded in academic environments, concerns continue to grow regarding academic integrity, diminishing critical thinking, and the erosion of authentic learning. To investigate these issues, the study incorporates a mixed-methods analysis of thirty undergraduate essays, each focused on AI’s influence within academia. The empirical data provide an exploratory snapshot of recurring perspectives among students in the course, including ethical uncertainty, ambivalence about AI’s role in learning, and cautious optimism about its potential to support, rather than replace, traditional education. These findings suggest the broader argument that while AI may enhance personalization and efficiency, it can also reinforce intellectual conformity and reduce deep engagement if adopted without critical oversight. By integrating both theoretical critique and grounded data, this paper proposes pedagogical strategies that promote academic rigor while encouraging responsible and reflective AI use. The aim is to preserve the foundational values of education by positioning AI as a complement to human learning rather than a substitute. Through this approach, academic institutions can cultivate environments that promote intellectual growth, ethical awareness, and lifelong curiosity in the age of machine intelligence.

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