Artificial Intelligence and Problem-Based Learning: Structural Equation Modeling Evaluation of Undergraduate Critical Thinking and Academic Performance
Keywords:
Problem-Based Learning (PBL), Artificial Intelligence in Education, Structural Equation Modeling (SEM), Critical Thinking, Higher Education EvaluationAbstract
The integration of Artificial Intelligence (AI) into pedagogical frameworks represents a paradigm shift in higher education. This study evaluates the effectiveness of an AI-integrated Problem-Based Learning (AI-PBL) model among undergraduate students. Specifically, it aims to determine how AI tools, acting as scaffolding agents, influence students' critical thinking, self-regulated learning, and overall academic performance. A quantitative research design utilizing Structural Equation Modeling (SEM) was employed. Data were collected from 450 undergraduate students across three major universities who participated in a semester-long AI-PBL course. The instrument consisted of a validated questionnaire measuring AI literacy, PBL engagement, critical thinking disposition, and learning outcomes. The measurement model demonstrated high validity and reliability. The structural model revealed that AI integration significantly mediates the relationship between PBL engagement and critical thinking skills ($\beta = 0.42, p < .001$). Furthermore, the model showed that AI-PBL positively impacts academic performance directly and indirectly through self-regulated learning mechanisms. The study confirms that AI does not diminish cognitive effort but, when integrated into PBL, enhances critical analysis and learning efficiency. These findings offer a robust framework for curriculum designers to embed AI explicitly as a collaborative intelligence tool in problem-solving tasks.
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