arXiv subjects

Naser Al Madi

Publications and source records attributed to Naser Al Madi.

3 recordsLinked to original sources

arXiv2026

Desirable Unfamiliarity: Insights from Eye Movements on Engagement and Readability of Dictation Interfaces

Transcripts displayed on dictation interfaces can be hard to read due to recognition errors and disfluencies. LLM-based text auto-correction could help, but changing the text during production could lead to distraction and unintended phrasing. To understand how to balance readability, attention, and accuracy, we conducted an eye-tracking experiment with 20 participants to compare five dictation interfaces: PLAIN (real-time transcription), AOC (periodic corrections), RAKE (keyword highlights), GP-TSM (grammar-preserving highlights), and SUMMARY (LLM-generated abstractive summary). By analyzing participants' gaze patterns during speech composition and reviewing processes, we found that during composition, participants spent only 7-11% of their time in active reading regardless of the interface. Although SUMMARY introduced unfamiliar words and phrasing during composition, it was easier to read and more preferred by participants. Our findings suggest a high user tolerance for altering spoken words in LLM-enabled diction interfaces.

cs.HC↗

arXiv2025

Combining Automation and Expertise: A Semi-automated Approach to Correcting Eye Tracking Data in Reading Tasks

In reading tasks drift can move fixations from one word to another or even another line, invalidating the eye tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast yet limited in accuracy. In this paper we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N=14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that on average the proposed technique was 44% faster than manual correction without any sacrifice in accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye tracking data, visualization, filters, data converters, and eye movement analysis in addition to the main contribution in data correction.

cs.HC↗

arXiv2022

How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot

Background: Recent advancements in large language models have motivated the practical use of such models in code generation and program synthesis. However, little is known about the effects of such tools on code readability and visual attention in practice. Objective: In this paper, we focus on GitHub Copilot to address the issues of readability and visual inspection of model generated code. Readability and low complexity are vital aspects of good source code, and visual inspection of generated code is important in light of automation bias. Method: Through a human experiment (n=21) we compare model generated code to code written completely by human programmers. We use a combination of static code analysis and human annotators to assess code readability, and we use eye tracking to assess the visual inspection of code. Results: Our results suggest that model generated code is comparable in complexity and readability to code written by human pair programmers. At the same time, eye tracking data suggests, to a statistically significant level, that programmers direct less visual attention to model generated code. Conclusion: Our findings highlight that reading code is more important than ever, and programmers should beware of complacency and automation bias with model generated code.

cs.SE↗