arXiv subjects

Rahad Arman Nabid

Publications and source records attributed to Rahad Arman Nabid.

2 recordsLinked to original sources

arXiv2026

To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks

AI code completion tools, such as Github Copilot, provide students with code suggestions to help them write programs. However, recent qualitative studies suggest that students fail to critically evaluate these suggestions. We present Clover, a code completion tool that logs students' interactions with code suggestions and additionally offers attention checks to probe reflective engagement during programming tasks. We also develop a taxonomy of behavioral interaction metrics for AI-assisted programming, informed by literature. We analyzed relationships between interaction patterns, engagement with attention checks, and task performance. We observed that higher rates of tab accept were associated with lower attention check performance, while increased dwell time was associated with higher attention check performance. We conclude by discussing how programming process data and attention checks might support reflective engagement in AI-assisted programming.

cs.HC↗

arXiv2026

Changing the Optics: Comparing Traditional and Retrieval-Augmented GenAI E-Tutorials in Interdisciplinary Learning

Understanding information-seeking behaviors in e-learning is critical, as learners must often make sense of complex and fragmented information, a challenge compounded in interdisciplinary fields with diverse prior knowledge. Compared to traditional e-tutorials, GenAI e-tutorials offer new ways to navigate information spaces, yet how they shape learners information-seeking behaviors remains unclear. To address this gap, we characterized behavioral differences between traditional and GenAI-mediated e-tutorial learning using the three search modes of orienteering. We conducted a between-subject study in which learners engaged with either a traditional e-tutorial or a GenAI e-tutorial accessing the same underlying information content. We found that the traditional users maintained greater awareness and focus of the information space, whereas GenAI users exhibited more proactive and exploratory behaviors with lower cognitive load due to the querying-driven interaction. These findings offer guidance for designing tutorials in e-learning.

cs.HC↗