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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| Veröffentlicht: |
Springer Nature B.V.
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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 |