AI-Assisted Metacognitive Strategies for Improving Self-Regulated Learning among High School Students

Abstract

The study was set up to determine whether using AI-led metacognition improved high school students’ ability to self-regulate their learning. With the use of a pre-test post-test control group design, 200 students were sampled randomly from public and private schools and then distributed equally into the experimental and control groups. The experimental group benefited from AI-enabled tools to strengthen their metacognitive skills, while the control group stuck to traditional ways of teaching. Data were collected by giving a standardized self-regulated learning questionnaire both before and after the intervention. The differences in post-test results between the two groups were significant enough to prove that AI-assisted strategies positively improved SRL in students. Paired samples t-test revealed that the experimental group experienced considerable improvement between the pre-test and the post-testThere is a significant influence of AI-based instruction and gender on self-regulated learning, but they do not influence each other. Demographic analysis made sure that the study included students from both genders, a variety of age groups, various schools and different grade levels, confirming AI-driven education can help many different types of students. The results support today’s views on education that highlight how AI is important in making learning more individualized and building teacher independence. Researchers say this evidence can be useful for those who are working to use AI in educational systems that are not commonly used. It demonstrates the need for ensuring different learners enjoy fair benefits from AI, stressing the need for digital education that is inclusive and accessible to all.