The Use of Eye Gaze Data and Personality Traits: A Scoping Review of the Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This scoping review examines the use of eye movement tracking in personality research across various domains, including job interviews, education and training, human-robot interaction, and user interface design. Eye-tracking has proven effective in capturing behavioral cues linked to personality traits such as emotional responses, leadership potential, and learning preferences. To map existing research and identify prevailing use case scenarios, a systematic search was conducted in the ACM and IEEE digital libraries. From an initial pool of 170 studies, 21 met the inclusion criteria and were subjected to full-text analysis. The purpose of this review is to provide a structured overview of current research trends, methodological approaches, and application contexts. Its contribution lies in synthesizing key insights and highlighting opportunities for future research, particularly in the use of eye-tracking for advancing personalized technologies and behavior-based analytics in fields such as education, marketing, and psychological analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it