<![CDATA[Artificial Intelligence (AI) has rapidly transformed teaching, learning, research, and assessment practices in higher education, prompting universities worldwide to establish institutional guidelines governing its responsible use. While previous studies have primarily examined AI adoption, ethics, and educational implementation, limited attention has been paid to how university AI guidelines construct academic integrity through language. This study investigates how academic integrity is discursively negotiated in official AI guidelines published by nine internationally recognized universities using Corpus-Assisted Critical Discourse Analysis (CACDA). Employing Fairclough's three-dimensional Critical Discourse Analysis framework, the study integrates corpus linguistic techniques through AntConc, including word frequency analysis, keyword-in-context (KWIC), and collocation analysis, to identify recurring linguistic patterns before conducting qualitative critical interpretation. The findings reveal four dominant discursive patterns. First, institutional authority is constructed through obligatory modality, particularly the recurrent use of must, which establishes mandatory academic practices concerning AI use, assessment, and disclosure. Second, AI governance is negotiated through conditional modality (should and may), allowing flexible yet regulated AI integration into academic activities. Third, students are positioned as responsible academic agents expected to exercise transparency, accountability, and ethical judgment when using AI-assisted technologies. Finally, academic integrity is reconstructed by redefining plagiarism through new institutional boundaries that distinguish legitimate AI assistance from academic misconduct. The study argues that university AI guidelines function not merely as regulatory documents but as institutional discourse through which universities negotiate technological innovation, construct responsible academic identities, and legitimize emerging standards of ethical AI use. The findings contribute to Critical Discourse Analysis and AI governance research by demonstrating that language plays a central role in shaping institutional responses to generative artificial intelligence in higher education.]]>
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