Typology-differentiated AI adoption policy interventions for education systems¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends four priority levels of policy intervention differentiated by manager typology: regulatory framework development (high priority, requiring ministerial action), differentiated training programs (high priority), institutional capacity building (medium priority), and peer learning networks leveraging Personalization champions as mentors (low priority but high impact). Training is differentiated because "one-size-fits-all training is suboptimal": Competency-constrained managers need intensive digital skills training, Regulation-blocked managers need policy advocacy and compliance guidance, and Barrier-free skeptics need evidence-based trust-building.

Design Implications¶

Context¶

Requirements¶

• Identification of manager typologies to differentiate interventions
• Ministerial action for regulatory framework development

Constraints¶

• The latent classes are exploratory and only modestly stable, so the typology-differentiated recommendations should be read as provisional hypotheses for intervention design rather than fixed policy segments, and should be piloted and validated before large-scale rollout

Target Learners¶

• Education managers across six readiness typologies

Target Learning Goals¶

• AI readiness, digital competency, governance understanding, trust in AI

Affordances¶

• Integrative Ai Adoption Readiness Framework Managers

Claims¶

• Latent Class Analysis identifies six provisional manager typologies with heterogeneous readiness and barrier profiles [+W]

Related Strategies¶

• Differentiated AI literacy training matched to acceptance profiles, with interventions beyond operational skills for Resistant Skeptics
• Tailor GenAI chatbot onboarding, training, and support to identified adoption personas

Examples¶

-

Key Sources¶

• V.G. Kremen, O.M. Spirin, O.I. Liashenko, S.H. Lytvynova, Y.I. Malovanyi, O.P. Pinchuk, O.M. Sokolyuk, S.O. Semerikov. (2026). AI adoption readiness among Ukrainian education managers: Barriers, typologies, and policy implications. Computers and Education: Artificial Intelligence 11 (2026) 100648. https://doi.org/10.1016/j.caeai.2026.100648