AI-supported problem-based learning for enhancing computational thinking skills in STEM education

By 2030, over 80% of skilled jobs will require higher order thinking such as computational thinking skills and problem-solving capabilities, yet most university students, especially in developing countries remain underprepared for such demands. This skills gap is particularly evident in computer robotics programming education, where traditional pedagogies fall short in fostering computational thinking (CT) and academic achievement. This study investigates the effectiveness of Artificial Intelligence-supported Problem-Based Learning (AI-PBL) in enhancing students’ CT and performance in computer robotics programming within a Nigerian university context. Grounded in Vygotsky’s Social Constructivist Theory, the study positions AI tools as “more capable peers,” offering adaptive scaffolding through intelligent systems embedded in inquiry-driven instruction. A quasi-experimental design with pre-test–post-test non-equivalent groups was employed to randomly assign the students. A total of 103 students were randomly assigned to experimental (AI-PBL) and control (conventional PBL) groups. Data was analyzed using ANCOVA and simple main effects tests. Results showed that students in the AI-PBL group significantly outperformed their peers in both posttest CT and academic achievement, controlling baseline scores. While gender did not significantly moderate the overall effects, both male and female students benefited from the AI-PBL approach. These results affirm the pedagogical potential of AI-enhanced PBL in STEM education, particularly in under-resourced contexts. The integration of intelligent systems not only improves student learning outcomes but also aligns with future workforce needs. The study calls for institutional and policy-level adoption of AI-PBL frameworks, investments in teacher training, and further research to ensure scalability. AI-supported pedagogy is not just innovative; it is essential for equitable skill acquisition and making student future-ready for world of work.

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Publication Details

Journal
Computers in Human Behavior Artificial Humans
Published
2026-02-09
DOI
https://doi.org/10.1016/j.chbah.2026.100263
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-supported problem-based learning for enhancing computational thinking skills in STEM education

Musa Adekunle Ayanwale, Christian Basil Omeh
Computers in Human Behavior Artificial Humans
Teaching and Learning Programming
article

AI-supported problem-based learning for enhancing computational thinking skills in STEM education

Musa Adekunle Ayanwale, Christian Basil Omeh
article en

Abstract

By 2030, over 80% of skilled jobs will require higher order thinking such as computational thinking skills and problem-solving capabilities, yet most university students, especially in developing countries remain underprepared for such demands. This skills gap is particularly evident in computer robotics programming education, where traditional pedagogies fall short in fostering computational thinking (CT) and academic achievement. This study investigates the effectiveness of Artificial Intelligence-supported Problem-Based Learning (AI-PBL) in enhancing students’ CT and performance in computer robotics programming within a Nigerian university context. Grounded in Vygotsky’s Social Constructivist Theory, the study positions AI tools as “more capable peers,” offering adaptive scaffolding through intelligent systems embedded in inquiry-driven instruction. A quasi-experimental design with pre-test–post-test non-equivalent groups was employed to randomly assign the students. A total of 103 students were randomly assigned to experimental (AI-PBL) and control (conventional PBL) groups. Data was analyzed using ANCOVA and simple main effects tests. Results showed that students in the AI-PBL group significantly outperformed their peers in both posttest CT and academic achievement, controlling baseline scores. While gender did not significantly moderate the overall effects, both male and female students benefited from the AI-PBL approach. These results affirm the pedagogical potential of AI-enhanced PBL in STEM education, particularly in under-resourced contexts. The integration of intelligent systems not only improves student learning outcomes but also aligns with future workforce needs. The study calls for institutional and policy-level adoption of AI-PBL frameworks, investments in teacher training, and further research to ensure scalability. AI-supported pedagogy is not just innovative; it is essential for equitable skill acquisition and making student future-ready for world of work.

Computers in Human Behavior Artificial HumansVol. 7
University of Nigeria (NG), University of Johannesburg (ZA)
Openalex Percentile: Top 70%
Teaching and Learning Programming
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