Guiding Novice Programmers With CPS-GPT: A Prompt-Driven AI Assistant for Creative Problem Solving

Abstract

Non-computer-science novices tend to rely on memorization and imitation when learning programming-related courses. Applying structured learning strategies such as Creative Problem Solving (CPS) can scaffold learners through complex tasks, while timely and adaptive support from ChatGPT can foster active knowledge construction and the development of higher-order thinking (HOT). However, conventional ChatGPT interactions are primarily based on one-way question–answering, which is not conducive to beginners who lack well-established knowledge structures. To address this limitation, this study developed a ChatGPT-based instructional assistant integrated with CPS, namely CPS-GPT. The results indicate that CPS-GPT significantly outperformed the comparison condition in creativity ( p = .013, Cohen’s d = .557), critical thinking ( p = .003, Cohen’s d = .674), problem solving ( p < .001, Cohen’s d = .780), knowledge construction ( p = .015, Cohen’s d = .548), as well as cognitive ( p = .039, Cohen’s d = .459), behavioral ( p = .006, Cohen’s d = .618), and social engagement ( p = .004, Cohen’s d = .656). Nevertheless, its task-oriented and highly structured design also emerged as a key factor underlying the non-significant effect on emotional engagement ( p = .373, Cohen’s d = .201).

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