While many students with attention-deficit/hyperactivity disorder (ADHD) already use productivity and focus tools to navigate attention challenges in university life, these tools remain limited. Existing approaches typically rely on a static mapping of applications to activities (e.g., labeling Facebook as social media and Microsoft Word as writing), which are often rigid and outdated, failing to distinguish between study-related and nonstudy-related activities within the same application. Additionally, while some tools provide feedback, it is usually retrospective, limiting support for real-time awareness needed in the context of ADHD. These limitations point to the need for tools that (1) detect distractions in real time and (2) provide immediate, point-of-performance feedback. Toward this end, we developed a comprehensive approach that (1) analyzes content from user screenshots to determine task focus and (2) provides immediate feedback to promote awareness of off-task shifts, allowing for real-time adjustments without external supervision. This approach includes an off-task detection model trained on the training partition of 14,533 in-lab screenshots from a manually labeled dataset of 14,995 screenshots, which achieved 96% accuracy on in-lab data and 84.4% on the remaining 462 intentionally challenging real-world data. We evaluated this approach with 11 university students with ADHD that we asked to work on their homework with and without our tool for 2 hours each and conducted interviews afterward. Participants appreciated the real-time feedback and reported feeling more focused, with benefits continuing even after the tool was removed. Our findings suggest that this approach offers a flexible resource for students with ADHD to manage their attention during homework; however, its effectiveness as a supportive aid relies on ensuring that students retain full agency over when, and for how long, they choose to engage with the system.
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Jamalinabijan et al. (2026) studied this question.
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