# Mapping Eye-Tracking Research in Human–Computer Interaction: A Science-Mapping and Content-Analysis Study

## TL;DR

This study maps recent eye-tracking research in human-computer interaction, identifying key trends and future directions in the field.

## Contribution

The novel contribution is a comprehensive science-mapping and content analysis of eye-tracking research in HCI from 2020 to 2025.

## Key findings

- Four main research areas emerged: deep-learning gaze estimation, XR interaction paradigms, cognitive load assessment, and accessibility-focused interface design.
- Highly cited papers focus on gaze interaction in immersive environments, deep learning for gaze estimation, and multimodal interaction.
- Eye tracking is transitioning from a measurement tool to a core technology in interaction design and cognitive assessment.

## Abstract

Eye tracking has become a central method in human–computer interaction (HCI), supported by advances in sensing technologies and AI-based gaze analysis. Despite this rapid growth, a comprehensive and up-to-date overview of eye-tracking research across the broader HCI landscape remains lacking. This study combines records from Web of Science (WoS) and Scopus to analyse 1033 publications on eye tracking in HCI published between 2020 and 2025. After merging and deduplicating the datasets, we conducted bibliometric network analyses (keyword co-occurrence, co-citation, co-authorship, and source mapping) using VOSviewer and performed a qualitative content analysis of the 50 most-cited papers. The literature is dominated by journal articles and conference papers produced by small- to medium-sized research teams (mean: 3.9 authors per paper; h-index: 29). Keyword and overlay visualisations reveal four principal research axes: deep-learning-based gaze estimation; XR-related interaction paradigms within HCI; cognitive load and human factors; and usability- and accessibility-oriented interface design. The most-cited studies focus on gaze interaction in immersive environments, deep learning for gaze estimation, multimodal interaction, and physiological approaches to assessing cognitive load. Overall, the findings indicate that eye tracking in HCI is evolving from a measurement-oriented technique into a core enabling technology that supports interaction design, cognitive assessment, accessibility, and ethical considerations such as privacy. This review identifies research gaps and outlines future directions for benchmarking practices, real-world deployments, and privacy-preserving gaze analytics in HCI.