syllabi to calibrate the AI Integration Continuum framework, (b) operational definition 
refinement through iterative discussion of coding criteria, (c) framework documentation 
and visualization development, and (d) literature search and synthesis support. All syllabus 
coding decisions, thematic analysis, interpretation of findings, and theoretical framing 
represent the independent judgment of the researcher.
Data Collection
All publicly accessible Spring 2026 art syllabi (N=330) from a large multi-campus community 
college were collected through the institutional database. The corpus includes studio 
courses (Ceramics, Design, Drawing, Painting, Photography, etc.) and lecture courses (Art 
Appreciation, Art History I, Art History II). Syllabi represent diverse modalities: in-person, 
online, and hybrid delivery.
Operational Definitions
AI was operationally defined as large language model-based generative systems (ChatGPT, 
Claude, Gemini) and generative image systems (Midjourney, DALL-E, Stable Diffusion). 
This excludes non-generative tools (spell-checkers, grammar tools, calculators) but includes 
any system producing original text, images, or code through machine learning.
Framework Development and Coding
A hybrid qualitative content analysis identified AI policy language patterns across syllabi. 
Initial open coding of 19 syllabi revealed six recurring integration levels, leading to the 
AI Integration Continuum framework: Absent (no AI mention), Prohibitive (AI forbidden), 
Selective (AI permitted in bounded contexts), Augmentative (AI as critical engagement 
tool), Integrative (systematic AI incorporation), and Immersive (AI as central medium).
Inter-rater reliability was established through independent coding of 19 syllabi by the 
researcher and an AI assistant using operational definitions. Initial agreement was 63% 
(12/19); after calibration discussion resolving boundary cases, agreement reached 84% 
(16/19). Refinements included: (1) distinguishing curricular AI mentions from usage policies, 
(2) clarifying that Selective requires affirmative permission for bounded uses rather than 
partial prohibition, and (3) separating Integration Level from Clarity Index as independent 
dimensions.
All 330 syllabi were then coded for Integration Level (Absent, 1-5), Framing Tone (F1-Punitive, 
F2-Neutral, F3-Instructional, F4-Affirming), Tool Designation (Restricted/Unrestricted), and 
Clarity Index (0-No Policy, 1-Vague, 2-Partial, 3-Clear). For Prohibitive policies, strength was 
coded (Weak/Moderate/Strong). Actual AI policy language was extracted and documented 
for qualitative analysis.
“Ultimately, silence is 
a position and one that 
institutions can no longer 
afford to maintain.”
53
TRENDS 2026 	
	
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