Actionable companion · Cognitive load & attention

Learning with agency: Data-Driven Insights into E-Learning: A Comprehensive Review of Eye-Tracking Applications in Learning Systems

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education, K–12 · mixed / unspecified. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract In the last few years, universities have increasingly implemented online learning environments, allowing students to study at their own pace. These environments utilize technological tools and implement methods to support training, deliver content, and promote the acquisition of new knowledge and skills. As an example of these technologies, eye tracking has emerged as a powerful tool for studying visual attention, cognitive processes, and learning behaviors. The main aim of this study is to provide a scoping review of recent eye-tracking research across diverse learner populations, ranging from K-12 students to university-level learners and educators. The present study examined recent advances in eye-tracking technologies, focusing on their potential, especially when combined with artificial intelligence (AI) techniques such as machine learning. It analyzed 54 empirical studies in the last few years, highlighting their applicability, strengths, and limitations. The research findings highlight the promise of eye-tracking technology to transform educational practices by providing data-driven insights regarding student behavior and cognitive processes. Future research must address implementation and data-analysis challenges to maximize the educational benefits of eye tracking. Keywords: eye tracking, e-learning, cognitive processes, visual attention, learning styles, cognitive load, engagement

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Higher education; K–12 · mixed / unspecified. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

In another context, Wyss et al. [33] analyzed eye-tracking data alongside post-hoc think-aloud verbalizations to examine teachers’ professional vision for noticing and interpreting significant classroom interactions. In their study, participants viewed a video of a lesson containing a critical incident (a pivotal event impacting the teaching-learning flow). The findings revealed that experienced teachers exhibited stronger professional vision, and they detected the critical incident more quickly (shorter time to first fixation). They made more frequent fixations on the critical incident than novices. Chen et al. [34] employed a commercial eye-tracking tool to collect and analyze students’ eye-movement data during Facebook Live streaming, identifying participants’ areas of interest and behavior, providing insights into visual attention and cognitive engagement. This work can guide the design of more effective live-streamed educational content to maximize attention, behavior, and interaction.

Limitations and uncertainty · source passage

Through this scoping review, we can deduce that eye-tracking studies on e-learning address a wide range of objectives, ultimately aiming to enhance the effectiveness and personalization of e-learning environments. Furthermore, eye-tracking studies on e-learning are developed for different aims, which often include:

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Safia Bendjebar; Yacine Lafifi; Rochdi Boudjehem; Aissa Laouissi. Data-Driven Insights into E-Learning: A Comprehensive Review of Eye-Tracking Applications in Learning Systems. Journal of Eye Movement Research (2026-04-17). https://doi.org/10.3390/jemr19020041

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.