The scientific foundation for this work rests on well-established theories of expertise. The information-reduction hypothesis holds that experts become efficient by discarding irrelevant visual information and concentrating on the crucial elements of a task. The theory of long-term working memory suggests that mastery extends processing capacity by building retrieval structures that let experts access vast stores of knowledge quickly. Meanwhile, the holistic model of image perception, developed from mammography research, proposes that experts gather information from broad and peripheral areas of the visual field, effectively widening their useful field of view. Decades of eye-tracking studies have backed these ideas: as professionals gain experience, they visit objects less frequently, spend less time viewing them, avoid distractors more reliably, and show evidence of an expanding visual span.

What makes the new review distinctive is its focus on turning those expert differences into training tools. The authors systematically searched the Scopus database following PRISMA guidelines, screening 79 initially identified articles down to a final sample of eight studies that either examined expertise-dependent differences in eye movements or tested the transfer of skills through gaze patterns. Their conceptual framework divided the field into three elements: the skill-transfer methods and cognitive phenomena involved, the macro application areas where eye movements serve as training tools, and the eye-tracking metrics used to measure both expertise and learning. The framework captures how techniques such as eye movement modeling examples, or EMMEs, record an expert’s gaze as the expert performs a task, then present the playback to learners alongside the expert’s verbal narration.

EMMEs are theoretically rooted in observational learning and in the cognitive theory of multimedia learning, and they draw on a striking discovery in cognitive neuroscience: the brain’s mirror system activates when a person watches another perform an action, simulating that action internally. Research on expert dancers has shown that this mirroring process can help integrate observed actions into the observer’s own behavioral repertoire. In practical studies, the technique has delivered real gains. Novice aircraft inspectors trained with expert gaze displays detected more faults during search tasks, and EMME-trained inspectors of circuit boards showed improved fault detection. Even programmers using expert gaze cues debugged software more quickly, suggesting the approach extends well beyond medicine and industry.

Medicine, and especially surgery, has become the proving ground for gaze-based training. In a randomized controlled trial, novice surgeons trained to follow expert-like gaze strategies on a laparoscopic simulator developed more target-locking fixations, completed tasks faster, and made fewer errors than peers who learned by discovery alone. The advantage became even more pronounced when participants had to multitask, hinting that trained gaze frees cognitive resources for other demands. Related work on quiet eye training, a technique that extends the final fixation before a critical movement, showed that trainees who practiced knot-tying with gaze training maintained their performance under heightened anxiety while traditionally trained peers faltered. Collaborative systems that displayed a supervisor’s live gaze to trainees reduced completion times and errors, and studies of visual guidance during laparoscopic tasks found better trainee performance when experts’ point of gaze was made visible.

The applications reach far beyond the operating room. In a maritime operation simulator, researchers built expert-derived attention maps that told trainees exactly where to focus during heavy lifting operations; the briefed group showed superior visual focus compared with a control group told only about the risks. In construction, eye-tracking studies revealed that workers who scanned the workplace more broadly recognized a higher proportion of hazards, and that personalized feedback based on eye movement data improved both search patterns and hazard recognition. In aviation, researchers comparing helicopter pilots of different experience levels during landing simulation found that veterans relied more heavily on cockpit instruments while novices looked out the window, and that eye-tracking feedback improved the transfer of skills from simulator to real flight.

Not every finding supported the enthusiasm. Studies of EMMEs in school settings produced mixed results. One experiment found that students who watched a model’s gaze replay while reading illustrated texts integrated verbal and graphical information more effectively, and that weaker readers benefited most. But another pair of experiments on procedural problem-solving in geometry found no significant advantage from displaying a model’s eye movements, and in one case the modeled gaze actually slowed transfer problem-solving. The authors of the review conclude that the nature of the task is a critical moderator: gaze modeling appears most effective for non-procedural tasks such as classification and strategy learning, where the expert simply observes the material, and least effective for procedural tasks requiring direct interaction with on-screen objects, which already capture attention naturally.

The review also flags subtler moderators. Prior knowledge matters, consistent with the expertise reversal effect, which holds that extra instructional guidance can burden learners who already know enough to proceed without it. The presence of verbal explanations alongside gaze overlays can either enhance learning by revealing the expert’s covert cognitive processes or, for perceptually simple tasks, overload the learner with redundant information. Interestingly, one study of medical image diagnosis found that experienced experts benefited from gaze modeling even more than novices did, improving diagnostic performance, scanning efficiency, and their ability to adapt skills to unfamiliar visualizations. This suggests that eye movement modeling examples can promote adaptive expertise, helping even seasoned professionals confront the evolving technologies that constantly reshape their fields.

The authors are candid about the limits of their evidence. Only eight studies met the strict inclusion criteria, and sample sizes were small and methodologically heterogeneous, so the performance advantages of gaze training should be interpreted with caution and cannot yet be generalized. Eye trackers measure only foveal vision, missing the covert shifts of attention that let people process information in peripheral and parafoveal regions, and the cost of research-grade systems, ranging from thousands to tens of thousands of euros, remains a barrier to widespread adoption. The exclusive reliance on Scopus may also have omitted relevant studies indexed elsewhere. Still, the review’s core conclusion stands: experts are more focused, organized, and deliberate in their visual behavior than novices, gaze-trained individuals show measurable performance advantages in the tasks examined, and rendering the invisible perceptual strategies of expertise visible may be one of the most promising frontiers in professional training, with implications for medicine, aviation, industry, sport, and education alike.

Subject of Research: A systematic review of skills-transfer and gaze strategies studied through eye-tracking across professional domains

Article Title: Skills-Transfer and Gaze Strategies Studied by Eye-Tracking: A Systematic Review

Article References: Lupia, E., Bortolotti, A., & Palumbo, R. (2026). Skills-Transfer and Gaze Strategies Studied by Eye-Tracking: A Systematic Review. Trends in Psychology. https://doi.org/10.1007/s43076-026-00535-6

Image Credits: AI Generated

DOI: 10.1007/s43076-026-00535-6

Keywords: eye-tracking, expertise, skill transfer, gaze training, quiet eye, eye movement modeling examples, visual attention, surgical training, human performance, systematic review, cognitive science, training methods

The multidisciplinary team, led by associate professor Jason Coronel, embarked on a mission to pinpoint the precise moments within an educational video that captivate children’s attention and enhance their cognitive absorption. By meticulously analyzing eye movements of children aged four to eight as they engaged with science-oriented video content, the study aimed to decode how visual attention correlates with knowledge acquisition.

Central to the research was an immersive four-minute video sourced from popular children’s educational series “SciShow Kids” and “Learn Bright,” focusing on the biological concept of animal camouflage. As the young participants watched the video, state-of-the-art eye-tracking devices recorded their gaze patterns with millisecond precision, capturing fine-grained data on where and when their attention shifted.

Post-viewing assessments evaluated each child’s grasp of camouflage concepts, revealing intriguing correlations between eye movement patterns and learning outcomes. The AI-driven analysis identified several pivotal segments in the video—referred to as “event boundaries”—where shifts in gaze significantly predicted whether children correctly answered comprehension questions. These event boundaries were conceptually aligned with moments in the video when the narrative transitioned, elucidating new information or introducing visual aids designed to consolidate understanding.

One of the most compelling findings emerged from the early segment of the video, where the host invited children to aid in locating an anthropomorphic character named Squeaks. Machine learning algorithms determined that children who visually tracked this cue exhibited heightened engagement, which was strongly predictive of their ability to comprehend subsequent, more abstract scientific concepts introduced later in the lesson. This suggests that early focused attention acts as a cognitive primer, preparing the brain to assimilate complex information.

The research also underscored the significance of explicit definitions paired with visual stimuli: when the narrator defined camouflage and simultaneously displayed the term on screen, children’s eye movements became more synchronized, indicating elevated attentional engagement. These findings emphasize the value of integrating carefully timed multimodal cues—verbal and visual—to anchor learning experiences effectively.

While the study’s results are preliminary, they chart an innovative path toward “real-time” educational feedback systems. Coronel envisions a future in which AI-powered algorithms interpret live eye-tracking data to ascertain a learner’s comprehension status instantaneously. In such a scenario, educational videos could adapt dynamically—altering explanations, providing alternative examples, or modifying difficulty—thereby tailoring learning to individual needs much like a personal tutor.

This vision leverages the rapid advancements in affordable eye-tracking hardware and sophisticated machine learning frameworks. As these technologies become more accessible, the potential to revolutionize remote and classroom learning grows exponentially, offering educators new tools to monitor and enhance student understanding continuously, rather than relying solely on post-lesson tests.

Furthermore, the integration of bio-sensing data with temporal analysis, as advanced by this research, enriches the theoretical frameworks of multimedia learning. It extends beyond static assessments by considering the temporal dimension—when in a dynamic narrative learning occurs—thus enabling the design of educational content informed by cognitive event segmentation theory.

The implications reach far beyond natural sciences or early childhood education. Similar methodologies could be applied to myriad other disciplines where bespoke learning paths are advantageous, including language acquisition, mathematics, and critical thinking skills development. The promise is an educational ecosystem where data-driven insights empower the creation of optimally engaging and instructive content that responds fluidly to learner feedback.

As machine learning models continue to refine their predictive capacities, they may uncover subtler patterns within eye-tracking data, such as micro-saccades or blink rates, further illuminating cognitive states spanning curiosity, confusion, or mastery. These nuanced biosignals hold the key to creating truly empathetic educational technologies, fostering not just knowledge retention but deeper understanding and curiosity.

Nonetheless, challenges remain. The ethical deployment of real-time monitoring, safeguarding children’s privacy, and ensuring equitable access to emerging technologies must accompany these advancements. Researchers emphasize the preliminary nature of their findings and advocate for broader, longitudinal studies to validate and expand upon these results.

In conclusion, this pioneering study stands at the convergence of cognitive science, machine learning, and educational technology, offering a tantalizing glimpse into a future where learning is personalized and dynamic. By harnessing the subtle cues of eye movements and embedding them within AI frameworks, educators and technologists together are poised to redefine the landscape of science education and beyond.

Subject of Research: How children learn science concepts from educational videos through analysis of eye-tracking and AI.

Article Title: Fusing theory-guided machine learning and bio-sensing: considering time in how children learn science from dynamic multimedia

News Publication Date: 5-Aug-2025

Web References: http://dx.doi.org/10.1093/joc/jqaf036

References: Journal of Communication, Advance Article, DOI: 10.1093/joc/jqaf036

Keywords: Eye tracking, Artificial intelligence, Educational videos, Children’s learning, Machine learning, Multimedia learning, Event boundaries, Science education, Personalized learning, Cognitive engagement