ORIGINAL RESEARCH article
Front. Educ.
Sec. STEM Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1674320
This article is part of the Research TopicBridging Barriers: Technology Integration in Mathematics EducationView all 4 articles
Artificial Intelligence Meets PBL: Transforming Computer-Robotics Programming Motivation and Engagement
Provisionally accepted
- 1University of Nigeria, Nsukka, Nsukka, Nigeria
- 2University of Nigeria, Nsukka, Nigeria
- 3Lesotho College of Education, Maseru, Lesotho
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In response to the growing demand for innovative instructional strategies in STEM education, we examine the effectiveness of AI-supported Problem-Based Learning (PBL) in improving students' engagement, intrinsic motivation, and academic achievement. Traditional pedagogies often fail to sustain learner interest and problem-solving skills, particularly in computing disciplines, which informed our focus on integrating artificial intelligence into PBL to address these gaps. We adopted a quasi-experimental design with a nonequivalent pretest–posttest control group structure, involving 87 second-year undergraduates enrolled in Computer Robotics Programming courses in Nigeria Universities. Participants were divided into two groups: the experimental group (n = 45, University of Nigeria) received AI-supported PBL instruction, while the control group (n = 42, Nnmadi Azikwe University) engaged in traditional PBL. We ensured the reliability and validity of our instruments, with Cronbach's alpha values exceeding .70, composite reliability > .70, and AVE > .50. Data were analysed using one-way multivariate analysis of covariance (MANCOVA) to assess the combined and individual effects of instructional method, controlling for prior programming experience. Results revealed a significant multivariate effect of instructional method on the combined outcomes, Wilks' Λ = .134, F(3, 82) = 176.93, p < .001, η² = .866. Univariate analyses showed that AI-supported PBL significantly improved engagement (η² = .694), motivation (η² = .690), and achievement (η² = .519) compared to traditional PBL. We conclude that integrating AI into active learning environments transforms cognitive and skills learning outcomes. We recommend that curriculum designers, educators and policymakers prioritise AI-enhanced pedagogies and invest in faculty training for sustainable STEM education. This approach promises to advance learner-centred instruction and equip graduates for the challenges of a technology-driven future.
Keywords: AI-supported learning, Problem-Based Learning, Robotics programming, students' motivation, students' engagement, stem education
Received: 27 Jul 2025; Accepted: 18 Sep 2025.
Copyright: © 2025 Omeh and Ayanwale. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Christian Basil Omeh, christian.omeh@unn.edu.ng
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