Date
Publisher
arXiv
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.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
