selective courses allow 15-20% to account for grammar tools and legitimate editing assistance, augmentative courses permit up to 40% with required citation (shifting focus from detection to documentation), and integrative courses treat percentages as informational rather than punitive (grading strategic use quality instead). This threshold approach addresses Beale’s (2025) concern that detection tools’ unreliability (26% false positive rates) makes enforcement- focused approaches untenable. Instead, detection becomes a pedagogical feedback tool rather than a gotcha mechanism. Conclusion This study documents faculty AI policy positioning across an entire community college art department, re vealing patterns that extend beyond individual practice to institutional systems. Silence dominates (72%), prohibition prevails among those who speak (56%), critical engagement is absent (0% Augmentative), and clarity remains elusive (59% vague). These findings challenge higher education to move beyond debates about AI detection toward institutional frameworks supporting thoughtful policy development. The AI Integration Continuum framework, refined through inter-rater reliability testing, provides both descriptive documentation and a normative roadmap. Empty categories, particularly Augmentative and Immersive levels, are not methodological flaws but aspirational benchmarks revealing what faculty aren’t yet imagining. Future research should examine student perspectives on AI policy variation, track longitudinal changes as institutional support develops, and assess learning outcomes across different integration levels. Ultimately, silence is a position and one that institutions can no longer afford to maintain. By making AI integration explicit through toolkits and designation systems informed by UNESCO’s (2021, 2023, & 2024) frameworks and comparative institutional research, higher education can transform reactive prohibition into proactive pedagogical design, respecting both traditional skill development and emerging technological literacies as legitimate educational pathways. References Beale, R. (2025). Adapting university policies for generative AI: Opportunities, challenges, and policy solutions in higher education. arXiv preprint arXiv:2506.22231. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284. Fink, L. D. (2013). Creating significant learning experiences: An integrated approach to designing college courses (2nd ed.). Jossey-Bass. Harrington, C., & Thomas, M. (2018). Designing a motivational syllabus: Creating a learning path for student engagement. Stylus Publishing. Henrickson, L. (2023). The end of creative work? Generative AI, proceduralism, and art education. Studies in Art Education, 64(4), 312-329. McCabe, D. L., Butterfield, K. D., & Trevino, L. K. (2012). Cheating in college: Why students do it and what educators can do about it. Johns Hopkins University Press. Parkes, J., & Harris, M. B. (2002). The purposes of a syllabus. College Teaching, 50(2), 55-61. UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO. https://www.unesco.org/en/articles/ai- and-education-guidance-policy-makers UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/ articles/guidance-generative-ai-education-and-research UNESCO. (2024). AI competency framework for teachers. UNESCO. https://www.unesco.org/en/articles/ai- competency-framework-teachers Wang, H., Dang, A., Wu, Z., & Mac, S. (2023). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. arXiv preprint arXiv:2312.05235. 57 TRENDS 2026 PAGE
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