Generative AI and Academic Integrity: A Sociological Perspective

Abstract

Generative artificial intelligence (AI) has entered higher education quickly, and it has brought back old sociological questions about how academic misconduct gets defined, spread, and controlled. This paper does not treat AI-assisted writing as just a quicker way to plagiarise. Instead, it argues that generative AI shakes the very norms that academic integrity has always relied on. Using classical sociological theories of deviance — strain theory, neutralization theory, social learning theory, and institutional theory — along with recent research on AI in higher education, the paper looks at how AI blurs the line between help and authorship, how “cheating” is a socially built category applied unevenly, how detection tools carry linguistic and economic bias, and why institutional policies often look better than they work. The paper concludes that academic integrity is not something technology can simply threaten or destroy. It is something built continuously through institutional design, peer relationships, and resources that are not shared equally among students. Keywords: generative artificial intelligence, academic integrity, sociology of deviance, higher education, academic dishonesty