The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches

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Veröffentlicht in:International Journal of STEM Education vol. 12, no. 1 (Dec 2025), p. 16
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Springer Nature B.V.
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024 7 |a 10.1186/s40594-025-00537-3  |2 doi 
035 |a 3173679773 
045 2 |b d20251201  |b d20251231 
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245 1 |a The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a PurposeThis study investigates the impact of AI-assisted pair programming on undergraduate students’ intrinsic motivation, programming anxiety, and performance, relative to both human–human pair programming and individual programming approaches.MethodsA quasi-experimental design was conducted over two academic years (2023–2024) with 234 undergraduate students in a Java web application development course. Intact class sections were randomly assigned to AI-assisted pair programming (using GPT-3.5 Turbo in 2023 and Claude 3 Opus in 2024), human–human pair programming, or individual programming conditions. Data on intrinsic motivation, programming anxiety, collaborative perceptions, and programming performance were collected at three time points using validated instruments.ResultsCompared to individual programming, AI-assisted pair programming significantly increased intrinsic motivation (p < .001, d = 0.35) and reduced programming anxiety (p < .001), producing outcomes comparable to human–human pair programming. AI-assisted groups also outperformed both individual and human–human groups in programming tasks (p < .001). However, human–human pair programming fostered the highest perceptions of collaboration and social presence, surpassing both AI-assisted and individual conditions (p < .001). Mediation analysis revealed that perceived usefulness of the AI assistant significantly mediated the relationship between the programming approach and student outcomes, highlighting the importance of positive perceptions in leveraging AI tools for educational benefits. No significant differences emerged between the two AI models employed, indicating that both GPT-3.5 Turbo and Claude 3 Opus provided similar benefits.ConclusionWhile AI-assisted pair programming enhances motivation, reduces anxiety, and improves performance, it does not fully match the collaborative depth and social presence achieved through human–human pairing. These findings highlight the complementary strengths of AI and human interaction: AI support can bolster learning outcomes, yet human partners offer richer social engagement. As AI capabilities advance, educators should integrate such tools thoughtfully, ensuring that technology complements rather than replaces the interpersonal dynamics and skill development central to effective programming education. 
610 4 |a International Journal of STEM Education 
653 |a Motivation 
653 |a Students 
653 |a Applications programs 
653 |a Experimental design 
653 |a Human relations 
653 |a Anxiety 
653 |a Comparative studies 
653 |a Performance enhancement 
653 |a Learning 
653 |a Artificial intelligence 
653 |a Undergraduate study 
653 |a Programming 
653 |a Design of experiments 
653 |a Computer science 
653 |a Student participation 
653 |a Science programs 
653 |a Science education 
653 |a Student retention 
653 |a Skill development 
653 |a Collaborative learning 
653 |a Pedagogy 
653 |a Mathematics education 
653 |a STEM education 
653 |a Skills 
653 |a Technology education 
653 |a Perceptions 
653 |a Educational objectives 
653 |a Qualitative research 
653 |a Quasi-experimental methods 
653 |a College students 
653 |a Comparative analysis 
653 |a Social 
653 |a Educational Benefits 
653 |a Undergraduate Students 
653 |a Learning Motivation 
653 |a Cooperative Learning 
653 |a Computer Oriented Programs 
653 |a Student Motivation 
653 |a Comparative Education 
653 |a Quasiexperimental Design 
653 |a Outcomes of Education 
653 |a Self Motivation 
773 0 |t International Journal of STEM Education  |g vol. 12, no. 1 (Dec 2025), p. 16 
786 0 |d ProQuest  |t Agriculture Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3173679773/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3173679773/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch