The R5E pattern: can artificial intelligence enhance programming skills development?

Abstract The rapid proliferation of artificial intelligence (AI), particularly within education, presents both opportunities and challenges. While AI offers innovative solutions to pedagogical challenges, unstructured utilization of AI tools like ChatGPT can foster passive learning, with students relying on automated solutions rather than engaging in active learning. This quasi-experimental study addressed this concern by introducing and evaluating the R5E pattern, a novel pedagogical model for integrating ChatGPT into programming education to promote active learning and critical thinking. The study compared the efficacy of the R5E pattern with unstructured ChatGPT use in developing programming skills among undergraduate students. Seventy students were randomly assigned to either an R5E group or an unstructured ChatGPT using group. A mixed-methods approach, employing the Programming Skills Cognitive Test (PS-CT) and the Programming Skills Performance Observation Card (PS-POC), assessed both cognitive and performance aspects of programming skills. Pre- and post-intervention data revealed improved programming skills across both groups; however, statistically significant differences favoured the R5E group on both post-intervention assessments. A significant positive correlation between PS-CT and PS-POC scores indicated that increased theoretical knowledge corresponded with enhanced practical performance. Qualitative observations revealed that R5E students engaged in more peer and instructor interaction, alongside ChatGPT use, and posed more sophisticated questions, unlike the second group, which relied more heavily on direct ChatGPT queries. These findings support the superior efficacy of the structured R5E pattern in fostering programming skill development compared to unstructured ChatGPT use. While limitations, including sample size and a short post-test timeframe, necessitate further research, this study suggests the R5E pattern holds promise as a validated pedagogical model for integrating AI into education. Future research should expand the sample, extend the intervention duration, and utilize a delayed post-test.