Impact of AI-Driven Learning on Students’ Metacognitive Self-Regulation Abilities

Framework

The theoretical framework of this research is grounded in *Transformative Learning Theory* and *Metacognitive Self-Regulation Theory*. This research aligns with this theory by exploring how AI-driven learning environments can transform students' self-regulation abilities, and effective knowledge retention.
Additionally, the study draws on Metacognitive Self-Regulation Theory, which focuses on learners' ability to plan, monitor, and evaluate their cognitive strategies. The research examines how AI tools can enhance these metacognitive skills, allowing students to better manage their learning processes and improve academic outcomes.

Methods

Fifty-two (52) science students studying in grade 10 at an Urban English medium secondary school were randomly separated into two groups. AI-driven learning was employed on the treatment group whereas the control group was taught by traditional teaching following the experimental research design. Data were collected quantitatively through the post-test control group design by using the Metacognitive Self-Regulation Questionnaire (MSQ); managed and analyzed by using SPSS software version 27 by using an independent sampled t-test to gauge the impact.

Results

. It resulted that AI-driven learning remained significant with t-value = 4.61 and df = 50 while p = .000 < α = .001 as compared to the traditional method. . The findings suggest that AI-driven learning significantly enhances Secondary School Science Students’ Metacognitive Self-Regulation Abilities. This research contributes to the growing body of evidence supporting the integration of AI in education, offering insights for educators and policymakers on optimizing instructional strategies to foster cognitive development across all educational levels.

Importance

The educational and scientific importance of this study lies in its empirical investigation of AI-driven learning as a tool to enhance metacognitive self-regulation among secondary school students. By demonstrating that AI can significantly improve students' cognitive skills, the study addresses critical gaps in current educational practices where traditional methods often fall short. This research is valuable to conference audiences because it offers concrete data and insights on the integration of AI in education, highlighting its potential to transform learning environments, foster personalized learning, and optimize student outcomes.

References

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