Artificial Intelligence and Instructional Designer Competencies: A Repeated Cross-Sectional Job Advertisements Study

This study analyzes temporal variation in Artificial Intelligence (AI)-related competency references in Instructional Design job advertisements using a repeated cross-sectional design. The data derive from a broader mixed-methods research project examining Instructional Designer competencies; the present paper focuses exclusively on the labor market component. Job postings were collected from LinkedIn in two independent waves—Wave 1 (November 2024–January 2025) and Wave 2 (November 2025–January 2026)—yielding a population of 5,337 advertisements. Independent random validation subsamples were drawn from each wave (Wave 1: n = 332; Wave 2: n = 340) for manual coding verification and statistical analysis. AI-related references were identified using dictionary-based automated coding in MAXQDA and subsequently validated to remove context-dependent false positives. A binary indicator of AI reference presence was used for inferential analysis. Results show a statistically significant increase in AI-related competency references between waves, χ²(1, N = 672) = 33.93, p < .001. Logistic regres-sion indicates that Wave 2 advertisements were significantly more likely to include AI-related references than Wave 1 (OR = 3.295, 95% CI [2.116, 5.131], p < .001), controlling for experience level and geographic region. The findings provide empirical evidence of a marked temporal increase in employer references to AI competencies in Instructional Design, suggesting growing visibility of AI within labor market expectations.