AI-Driven Personalization and Its Effects on Self-Regulated Learning and Intrinsic Motivation Among Primary School Students in Kurdistan Province, Iran: A Post-Pandemic Perspective

In the post-pandemic era, primary school students in resource-limited regions like Kurdistan Province, Iran, face persistent challenges in self-regulated learning (SRL) and intrinsic motivation (IM), prompting the need to evaluate AI-driven personalization as a targeted intervention to enhance learner autonomy and engagement. This quasi-experimental study involved ۲۵۰ students aged ۸-۱۲, divided into an intervention group (n=۱۲۵) using an AI-personalized learning platform and a control group (n=۱۲۵) receiving standard instruction over ۱۲ weeks. Pre- and post-assessments utilized the Motivated Strategies for Learning Questionnaire (MSLQ) for SRL and the Intrinsic Motivation Inventory (IMI) for IM, with data analyzed via repeated-measures ANOVA, paired t-tests, and subgroup analyses. Results revealed significant improvements in the intervention group, including a ۲۵.۶% mean increase in combined SRL and IM scores (p < ۰.۰۰۱, Cohen's d = ۱.۱۲), compared to ۴.۲% in controls. SRL gains averaged ۲۹.۴%, with enhancements in goal setting, self-efficacy, and metacognition; IM improved by ۲۵.۷%, particularly in enjoyment and competence. Subgroup effects were amplified among rural (SRL +۳۲.۱%, IM +۲۸.۴%) and low socioeconomic status students. These findings underscore AI personalization's efficacy in addressing post-pandemic educational gaps, promoting equitable outcomes, and informing ethical implementations in primary education.

کلیدواژه ها:

AI-driven personalization ، Self-regulated learning ، Intrinsic motivation ، Primary education ، Post-pandemic education ، Kurdistan Iran