Metacognitive–Algorithmic Readiness for Human–AI Interaction in Educational Supervision: Scale Development, Network Psychometric Validation, and Preliminary Responsiveness to Microlearning

.As artificial intelligence (AI) becomes increasingly embedded in professional decision-making, readiness for effective human–AI interaction requires more than general technology acceptance. This study developed and validated the Metacognitive–Algorithmic AI Learning Readiness Scale for Educational Supervision (MA-AILRS-ES) and examined its preliminary responsiveness. The multi-phase design included expert review (n = 4), cognitive interviews (n = 8), pilot testing (n = 30), independent exploratory and confirmatory samples (n = 194 each), and a one-group pretest–posttest phase (n = 36) with Saudi educational supervisors. Exploratory Graph Analysis, bootstrap EGA, and DWLS confirmatory factor analysis supported an 18-item correlated five-factor structure. Total readiness and three dimensions increased significantly over two weeks, whereas Perceived Capability and Practical Supervisory Applicability did not. Because no comparison group was used, changes indicate score responsiveness rather than intervention effectiveness. Findings support the MA-AILRS-ES as a context-specific developmental measure requiring further controlled and cross-context validation.