Generative AI as a Tool or Leader? Exploring AI-Augmented Thinking in Student Programming Tasks

TL;DR

This study investigates how university students interact with Generative AI during programming tasks, revealing differences in thinking processes and strategies between human-led and AI-led approaches, and how attitudes influence these interactions.

Contribution

It introduces a novel AI-augmented thinking coding scheme and categorizes student interactions, providing insights into the influence of GAI on programming cognition.

Findings

Human-led students spend more time on solution development and refinement.

Distinct interaction patterns are observed between human-led and AI-led groups.

Positive attitudes and critical thinking correlate with more active question formulation.

Abstract

The increasing use of Generative Artificial Intelligence (GAI) tools in education highlights the need to understand their influence on individuals' thinking processes and agency. This research explored 20 university students' interaction with GAI during programming. Participants completed surveys, recorded their screens during an hour-long programming session, and reflected on their GAI use. To analyse the data, we developed an AI-augmented thinking coding scheme with four dimensions: Question Formulation, Solution Development, Solution Analysis and Evaluation, and Solution Refinement. Participants were categorised into human-led and AI-led groups based on the time ratio of human-generating source code versus copying source code from GAI. T-tests indicated that the human-led group spent significantly more time on Solution Development and Solution Refinement than the AI-led group. Sequential pattern mining revealed distinct patterns of the two groups: the human-led group often refined GAI outputs, while the AI-led group frequently relied on direct answers from GAI. Correlation analyses found that positive attitudes towards AI, critical thinking, and programming self-efficacy positively correlated with Question Formulation; critical thinking was positively related to Solution Refinement; and programming self-efficacy was negatively associated with Solution Analysis and Evaluation. This study enhances understanding of the thinking process in GAI-supported programming.