With the proliferation of Generative AI in higher education, research has focused on college students' anxiety regarding false accusations of academic dishonesty (e.g., Firat, 2023). This study extends prior literature by moving beyond mere perception to explore how learners discursively negotiate and prove their human authenticity under technological surveillance. To this end, the study analyzed 671 comments from 10 most commented threads discussing AI use and detection within student-centered Reddit communities (e.g., r/college) during 2024-2025. The collected data were iteratively read, coded, and categorized to derive discursive themes, which were then interpreted through Fairclough(1992)’s Critical Discourse Analysis. First, at the textual level, linguistic "fluency" is depicted as a machine trait, while intentional "clunky" and "messy" structures are semiotically inverted as markers of authentic humanity. Second, at the discursive level, students form a dual discourse of resistance and conformity, reconstructing their subjectivity to align with algorithmic surveillance. Third, at the sociocultural level, humanity is transformed from a subject of knowledge production into a "provable alibi" to evade suspicion. The structural paradox, where students must intentionally degrade their achieved linguistic proficiency, fundamentally erodes the logic of assessment and the foundation of academic trust. Ultimately, this study exposes a state of "academic self-negation" where learners perform flaws to avoid being misidentified as AI, highlighting an urgent need to restore human-centered assessment practices.

Authors:
Minjoo Chong, University of Illinois Urbana-Champaign, United States

About the Presenter(s)
Minjoo Chong is a doctoral student in Education Policy, Organization and Leadership at the University of Illinois Urbana-Champaign, US.

Connect on Linkedin
https://www.linkedin.com/in/minjoo-chong/

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