Adaptive Cognitive Intervention Architecture: An Exploratory Computational Framework for Precision Reading Comprehension in Higher Education.

Reading comprehension is a critical cognitive competency in higher education, although learners demonstrate substantial variability in responsiveness to metacognitive instructional interventions. The study focused on individual cognitive-response processes within the framework of the adaptive metacognitive reading system, which was realized through precision-learning architecture, which integrates latent learner-response phenotyping, explainable machine learning, Markov transition analysis, Bayesian adaptive inference, and reinforcement-learning optimization. The study employed a quasi-experimental longitudinal design involving an eight-week structured metacognitive reading intervention delivered through planning, monitoring, evaluation, strategic flexibility, and reading self-regulation activities. The psychometric analyses demonstrated satisfactory reliability of the adapted Metacognitive Awareness Inventory (MAI), with Cronbach's α ranging from 0.83 to 0.89. A latent-profile model revealed significant heterogeneity of learner-response patterns among learners, with four learner-response phenotypes: High Responders, Strategic Improvers, Monitoring-Dependent Learners, and Low Responders. Explainable machine-learning models performed well in predicting individualized comprehension gains, with the model with the highest predictive accuracy being XGBoost (R2 = 0.61). Markov transition modeling identified exploratory learner-state redistribution patterns following the intervention. Bayesian adaptive inference and reinforcement-learning optimization were subsequently conducted as post hoc simulation procedures to estimate hypothetical adaptive instructional calibration scenarios rather than as real-time instructional decision systems. Overall, the proposed Adaptive Cognitive Intervention Architecture (ACIA) should be interpreted as an exploratory computational framework for modeling learner heterogeneity, predicting comprehension gains, and simulating post hoc computational optimization in higher-education learning environments.