  The new capabilities of generative artificial intelligence tools Generative
AI, such as ChatGPT, allow users to interact with the system in intuitive ways,
such as simple conversations, and receive (mostly) good-quality answers. These
systems can support students' learning objectives by providing accessible
explanations and examples even with vague queries. At the same time, they can
encourage undesired help-seeking behaviors by providing solutions to the
students' homework. Therefore, it is important to better understand how
students approach such tools and the potential issues such approaches might
present for the learners. In this paper, we present a case study for
understanding student-AI collaboration to solve programming tasks in the CS1
introductory programming course. To this end, we recruited a gender-balanced
majority non-white set of 15 CS1 students at a large public university in the
US. We observed them solving programming tasks. We used a mixed-method approach
to study their interactions as they tackled Python programming tasks, focusing
on when and why they used ChatGPT for problem-solving. We analyze and classify
the questions submitted by the 15 participants to ChatGPT. Additionally, we
analyzed user interaction patterns, their reactions to ChatGPT's responses, and
the potential impacts of Generative AI on their perception of self-efficacy.
Our results suggest that in about a third of the cases, the student attempted
to complete the task by submitting the full description of the tasks to ChatGPT
without making any effort on their own. We also observed that few students
verified their solutions. We discuss the results and their potential
implications.
