as an implicit policy statement: what faculty do not mention signals either irrelevance or discomfort. UNESCO’s (2023) frameworks for AI in education provide normative guidance for institutional responses. The Guidance for Generative AI in Education and Research identifies three core principles: (1) human-centered design ensuring AI enhances rather than replaces human agency, (2) ethical validation requiring privacy protection and pedagogical appropriateness, and (3) transparency in AI use including disclosure requirements and critical evaluation. The AI Competency Framework for Teachers (2024) outlines progressive skill development from basic AI literacy through pedagogical integration and ethical reasoning. These frameworks are positioned as thoughtful AI engagement rather than prohibition or uncritical adoption. However, translating UNESCO’s (2023) normative frameworks into classroom practice requires institutional infrastructure. Wang et al.’s (2023) analysis of the top 100 U.S. universities’ AI policies revealed that institutions adopting “open but cautious” approaches primarily offered resources (syllabus templates, workshops, consultation) rather than mandates. Primary faculty concerns centered on ethical usage, accuracy, and data privacy suggesting that prohibition stems from uncertainty rather than pedagogical conviction. Most universities provided detection tool access (Turnitin, GPTZero), but fewer than half offered guidance on integrating AI pedagogically. This gap between policy aspiration and implementation support helps explain why faculty default to silence or prohibition. Academic integrity frameworks traditionally emphasize detection and punishment (McCabe et al., 2012), but generative AI challenges these approaches by producing work indistinguishable from student writing. Beale (2025) argues that universities must shift from policing-based models to principle-based frameworks emphasizing transparency, attribution, and authentic assessment redesign. Detection tools remain unreliable, with false positive rates as high as 26% and systematic biases against non-native English speakers. This technical limitation necessitates pedagogical rather than punitive responses. In art education specifically, discussions have focused on ethical implications and creative authenticity (Henrickson, 2023) rather than practical syllabus policy development. Henrickson argues that generative AI represents proceduralism in creative work and algorithmic mediation replacing direct material engagement. This positions AI as fundamentally opposed to studio art pedagogy’s emphasis on hand skills, material knowledge, and embodied making. Yet such philosophical critiques rarely translate to concrete syllabus language, leaving faculty without models for articulating disciplinary values in policy form. Faculty technology adoption literature reveals that institutional support structures significantly impact implementation success (Ertmer & Ottenbreit-Leftwich, 2010). Without clear models, policies, or professional development, faculty default to prohibition or silence rather than thoughtful integration of AI. This study contributes an empirically- grounded framework for categorizing AI integration levels and documenting current faculty positioning. The AI Integration Continuum extends beyond binary permission/prohibition to map developmental trajectories from absence through immersive implementation, which provides both descriptive documentation and a normative roadmap for institutional support. Methods This research employed Claude (Anthropic, 2026), a generative AI assistant, for specific methodological and analytical tasks. AI use included: (a) inter-rater reliability coding of 19 52 TRENDS 2026 PAGE
View this content as a flipbook by clicking here.