GENERATIVE AI IN ACADEMIA: THEMATIC ANALYSIS OF REDDIT ATTITUDES
GENERATIVE AI IN ACADEMIA: THEMATIC ANALYSIS OF REDDIT ATTITUDES

Summary/Abstract: This study examines academic community attitudes toward generative artificial intelligence through qualitative analysis of 403 comments from eight Reddit discussions in academic subreddits. Employing reflexive thematic analysis, the research identifies three dominant attitude patterns: negative (42%), positive (32%), and neutral (26%). Negative attitudes focus on the degradation of academic integrity, uncritical institutional acceptance, AI hallucinations, unreliable detection tools, and totalitarian technology imposition. Positive attitudes emphasise productivity gains in technical tasks, writing assistance, accessibility, and practical teaching applications. Neutral attitudes highlight AI as a context-dependent tool and acknowledge systemic pressures driving adoption. Analysis of community reception reveals that nuanced comments receive the highest engagement while uncritical optimism is actively rejected. Significant disciplinary differences emerge, with STEM fields showing predominantly positive attitudes and humanities expressing concern about undermining core educational values. The study identifies key ethical issues, including false authorship, citation fabrication, detection unreliability, copyright concerns, deskilling, and environmental impact. Results indicate no consensus exists regarding AI's role in higher education, suggesting the need for discipline-specific institutional guidelines that balance benefits and risks while addressing systemic pressures driving uncritical adoption.

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