Harnessing Ethical and Inclusive AI in Early Years Education: Teachers’ PsyCap and Neurodiverse Children’ Social-Emotional Development

Background: As Artificial Intelligence (AI) increasingly transforms Early Childhood Education (ECE), teachers’ psychological readiness has emerged as a critical yet underexplored factor in fostering inclusive learning environments for neurodiverse learners, particularly in the Global South. Purpose: This study examines the effect of AI-assisted differentiated instruction on the social-emotional development of neurodiverse learners and investigates the moderating role of teachers’ Psychological Capital (PsyCap). Methodology: Employing a quantitative cross-sectional design, data were collected from ECE teachers (N = 350) in Lahore and Islamabad. Moderation analysis was conducted using Hayes’ PROCESS Macro (Model 1) to examine the interaction effect of AI-assisted instruction × PsyCap, including its dimensions hope, efficacy, resilience, and optimism. Findings: Results indicate that AI-assisted differentiated instruction significantly predicts social-emotional development (β > 0, p < .001). Moreover, teachers’ PsyCap significantly moderates this relationship (AI × PsyCap: β = 0.18, p < .001), such that higher levels of PsyCap amplify the positive effects of AI-driven instructional personalization. Conclusion: These findings highlight the need for a strategic policy shift from predominantly hardware-centric approaches toward psychologically informed readiness models. Strengthening teachers’ PsyCap is essential for sustainable, ethical, and inclusive AI integration within Pakistan’s ECE classrooms.