Abstract

Agentic artificial intelligence (AAI) systems—capable of autonomously executing multi-step instructional tasks with minimal human oversight—are reshaping higher education while exposing the limitations of adoption- and continuance-centred models that treat value and risk as independent predictors. In an environment of widespread AI availability, the more meaningful question concerns how deeply educators integrate delegation-capable systems into teaching, assessment, and instructional workflows. Drawing on affordance theory, institutional theory, and automation risk perspectives, this study develops and tests a dual-pathway framework that conceptualises pedagogical integration as a dynamic value–risk trade-off embedded within institutional and contextual conditions. Survey data from 338 higher-education educators across Global North and South contexts, all of whom underwent a structured screening process to verify exposure to agentic rather than conventional generative AI systems, were analysed using PLS-SEM with robustness and endogeneity checks. The model explains substantial variance in pedagogical integration (R2 = 0.619), pedagogical value (R2 = 0.621), and autonomy risk (R2 = 0.615). Institutional AI capability is positively associated with pedagogical value and negatively associated with autonomy risk, whereas perceived AI complexity shows the opposite pattern. Higher pedagogical value is also associated with lower perceived autonomy risk (β = −0.445, p < 0.001), suggesting that educators may interpret system autonomy as a manageable pedagogical feature rather than solely as a source of concern. Regulatory clarity weakens the complexity–risk relationship, while AI legitimacy strengthens the value–integration relationship. Given the cross-sectional design, findings should be interpreted as associative rather than causal.

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