Educational Technology and Personalized Learning Experiences among College Students

Abstract

Educational technology is increasingly shifting higher education from uniform digital instruction toward data-informed and individually adaptive learning environments.
This study examines how AI-enabled educational technologies influence personalized learning experiences, academic engagement, and self-regulated learning among college students with differing levels of digital readiness.
A specific research gap is identified in the limited integration of learner heterogeneity, behavioral interaction data, and perceived personalization within a single analytical framework.
The proposed research conceptualizes personalization as a multidimensional process involving adaptive content sequencing, individualized feedback, learning-resource recommendations, and learner-controlled pacing rather than simple technology exposure.
Student interaction traces and questionnaire-based indicators are proposed for constructing learner profiles representing academic behavior, technological readiness, engagement, and self-regulation.
A hybrid machine-learning framework is designed to identify learner profiles and predict the degree to which students benefit from personalized educational interventions.
The study further investigates whether perceived personalization mediates the relationship between educational technology use and meaningful learning engagement.
The anticipated contribution is a student-centered framework for evaluating when, for whom, and under what technological conditions personalized learning produces beneficial higher-education experiences.