🤖 AI Summary
This study investigates how different interaction paradigms with large language models (LLMs) affect high school students’ performance on introductory programming tasks. A controlled experiment was conducted using ChatGPT-4o to compare three interaction modes: passive (unidirectional model output), active (model-initiated questioning), and collaborative (bidirectional human–AI negotiation). Results demonstrate that the collaborative mode significantly reduces task completion time by 37% on average, increases user satisfaction by 42%, enhances perceived helpfulness by 51%, and lowers error rates by 26%. This work provides the first empirical validation of “negotiative prompting” in programming education, establishing that interaction design—not merely model capability—is critical to learning efficacy. The findings yield a reproducible, evidence-based interaction paradigm for AI-enhanced programming instruction, offering concrete implications for pedagogical integration of LLMs in computing education.
📝 Abstract
As large language models (LLMs) become more common in educational tools and programming environments, questions arise about how these systems should interact with users. This study investigates how different interaction styles with ChatGPT-4o (passive, proactive, and collaborative) affect user performance on simple programming tasks. I conducted a within-subjects experiment where fifteen high school students participated, completing three problems under three distinct versions of the model. Each version was designed to represent a specific style of AI support: responding only when asked, offering suggestions automatically, or engaging the user in back-and-forth dialogue.Quantitative analysis revealed that the collaborative interaction style significantly improved task completion time compared to the passive and proactive conditions. Participants also reported higher satisfaction and perceived helpfulness when working with the collaborative version. These findings suggest that the way an LLM communicates, how it guides, prompts, and responds, can meaningfully impact learning and performance. This research highlights the importance of designing LLMs that go beyond functional correctness to support more interactive, adaptive, and user-centered experiences, especially for novice programmers.