Enhancing Problem-Solving Skills and Level of Engagement in Mathematics Through AI-Supported Task-Based Learning

Abstract

This study examined the effectiveness of AI-supported task-based learning in enhancing students’ problem-solving skills and level of engagement in mathematics. Through the quasi-experimental research design, the researcher assessed the performances of the students before, during, and after exposure to the intervention across topics such as permutation, combination and probability.

Findings revealed that prior to the intervention, the students demonstrated a low level of problem-solving skills with a mean of 73.29 categorized as “Did Not Meet Expectation”, particularly on the areas of making the problem-solving plan, carrying out the plan, and looking back at the completed solution which could be associated with planning, execution and reflection which requires higher order thinking skills. However, a substantial improvement was made during implementation and was sustained after in the posttest results, indicating mastery of both cognitive and metacognitive skills.

In terms of the level of student’s engagement, the students achieved a Very High level of engagement with an overall mean of 3.88 interpreted as “Very Highly Engaged” across all three components: behavioral, cognitive and affective engagement. This indicates that the intervention not only improved academic performance but also fostered active participation.  However, the relationship between engagement and problem-solving skills varied across AI tools, emphasizing the need for structured guidance and feedback.

The study concludes that AI-supported task-based learning is an effective and transformative tool for instruction that can significantly enhance the skills and engagement especially in the field of Mathematics.