Investigating the impact of generative AI integration on the sustenance of higher-order thinking skills and understanding of programming logic

This study investigates how integrating generative AI (GenAI) with instructional scaffolding and prompt engineering supports higher-order thinking skills (HOTS) and programming logic. A mixed-methods design was used, combining quantitative and qualitative data. The intervention followed a one-group pretest-post-test structure over seven weeks with 25 computer science students with no prior C++ experience. The GenAI-Ped framework guided the design. It combines Bloom's taxonomy, Seelf-Regulated Learning, Universal Design for Learning, and Vygotsky's Zone of Proximal Development. Students received scaffolded support across six instructional phases, including prompt training and guided GenAI use. Quantitative results showed significant gains in problem-solving (applying constructs: t = 2.38, p = 0.013, d = 0.475), critical thinking (conditional reasoning: t = 2.53, p = 0.018, d = 0.506), creativity (applying new ideas: t = 2.28, p = 0.032, d = 0.456), and programming logic (loops: t = 2.78, p = 0.010, d = 0.555). However, smaller gains were observed in code optimization ( t = 1.693, p = 0.103, d = 0.339) and evaluating solutions ( t = 1.732, p = 0.096). Qualitative data, including feedback and GenAI chat logs, showed that prompt specificity and scaffolded feedback improved engagement, HOTS, and programming logic. The novelty of the study lies in its demonstration that the integration of GenAI into programming education using GenAI-Ped framework can sustain HOTS and programming logic while mitigating overreliance. These findings offer a practical model for integrating GenAI into programming education. • Integrated GenAI with scaffolding to sustain programming logic and HOTS. • Scaffolding + prompt engineering boosts programming logic and HOTS. • Novel GenAI-Ped framework prevents overreliance while enhancing programming logic and HOTS. • Mixed-methods reveal students' prompt quality directly impacts learning gains. • Scaffolding prevents overreliance while enhancing creativity in coding tasks.

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Publication Details

Journal
Computers and Education Artificial Intelligence
Published
2025-08-09
DOI
https://doi.org/10.1016/j.caeai.2025.100460
Citations
4
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
6.77
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article

Investigating the impact of generative AI integration on the sustenance of higher-order thinking skills and understanding of programming logic

Solomon Sunday Oyelere, Jarkko Suhonen, Matti Tedre, Jemimah Nathaniel
4 citations
Computers and Education Artificial Intelligence
Teaching and Learning Programming
6.77
article

Investigating the impact of generative AI integration on the sustenance of higher-order thinking skills and understanding of programming logic

Solomon Sunday Oyelere, Jarkko Suhonen, Matti Tedre, Jemimah Nathaniel
article en
4 citations

Abstract

This study investigates how integrating generative AI (GenAI) with instructional scaffolding and prompt engineering supports higher-order thinking skills (HOTS) and programming logic. A mixed-methods design was used, combining quantitative and qualitative data. The intervention followed a one-group pretest-post-test structure over seven weeks with 25 computer science students with no prior C++ experience. The GenAI-Ped framework guided the design. It combines Bloom's taxonomy, Seelf-Regulated Learning, Universal Design for Learning, and Vygotsky's Zone of Proximal Development. Students received scaffolded support across six instructional phases, including prompt training and guided GenAI use. Quantitative results showed significant gains in problem-solving (applying constructs: t = 2.38, p = 0.013, d = 0.475), critical thinking (conditional reasoning: t = 2.53, p = 0.018, d = 0.506), creativity (applying new ideas: t = 2.28, p = 0.032, d = 0.456), and programming logic (loops: t = 2.78, p = 0.010, d = 0.555). However, smaller gains were observed in code optimization ( t = 1.693, p = 0.103, d = 0.339) and evaluating solutions ( t = 1.732, p = 0.096). Qualitative data, including feedback and GenAI chat logs, showed that prompt specificity and scaffolded feedback improved engagement, HOTS, and programming logic. The novelty of the study lies in its demonstration that the integration of GenAI into programming education using GenAI-Ped framework can sustain HOTS and programming logic while mitigating overreliance. These findings offer a practical model for integrating GenAI into programming education. • Integrated GenAI with scaffolding to sustain programming logic and HOTS. • Scaffolding + prompt engineering boosts programming logic and HOTS. • Novel GenAI-Ped framework prevents overreliance while enhancing programming logic and HOTS. • Mixed-methods reveal students' prompt quality directly impacts learning gains. • Scaffolding prevents overreliance while enhancing creativity in coding tasks.

Computers and Education Artificial IntelligenceVol. 9
University of Eastern Finland (FI), Covenant University (NG), Luleå University of Technology (SE), University of Exeter (GB)
Quality Education
Openalex Percentile: Top 3%
Teaching and Learning Programming
6.77
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