When knowledge is not enough: Faculty competencies, training challenges, and AI integration in higher ed-ucation aligned with SDG 4
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Abstract
Faculty AI integration in higher education depends critically on educator competencies, yet studies examining multiple predictors simultaneously remain scarce. This cross-sectional quantitative study surveyed 329 higher education faculty members to model AI integration as a function of five predictors: AI literacy, pedagogical readiness, technical readiness, ethical awareness, and AI-related challenges, while also examining variation by academic rank and formal AI training experience using OLS regression, moderation analysis, ANOVA, and independent-samples t-tests implemented in a reproducible Python-based pipeline. Technical Readiness emerged as the strongest independent predictor of AI integration (β = 0.43, p < .001), followed by Pedagogical Readiness (β = 0.35, p < .001), together explaining 35% of outcome variance, while AI Literacy and Ethical Awareness showed no significant direct effects. Counterintuitively, formally trained faculty reported lower AI integration than untrained peers alongside heightened perceptions of AI-related challenges, suggesting a knowing–doing gap whereby professional development raises critical awareness without proportionally enabling classroom experimentation. Academic rank significantly influenced integration levels, with Lecturers and Professors reporting the highest scores. Sustainable AI adoption in higher education requires institutional strategies that build technical confidence and pedagogical adaptability concurrently, supplemented by structured experimentation opportunities and differentiated career-stage support rather than awareness-focused training alone.
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