The widespread use of generative AI has led to increased focus on human–AI interaction. However, AI systems can generate unexpected outputs, leading to disagreement or human–AI conflict. This paper focuses on modelling user disagreement using machine learning (ML) by observing users’ implicit viewing behaviour. We conducted a controlled study with 30 participants evaluating captions from a simulated ML image-captioning system. Participants indicated agreement or disagreement with each caption while we recorded their gaze and facial-expression data, which we used to predict (dis)agreement. We show that unimodal gaze-based personalised modelling (0.684 average balanced accuracy) outperforms generalised modelling (0.570), whereas multimodal approaches did not improve performance. Our exploratory post hoc gaze-based analysis highlights the importance of feature selection and temporal dynamics, which help guide system design and future work. We release the dataset to support reproducibility and further work. Due to the nature of this research, we also discuss the potential ethical and privacy implications of continuous passive gaze and facial monitoring.

As public displays become more ubiquitous, common authentication methods face security (e.g., shoulder surfing) and sanitary risks. Gaze-based systems offer a promising alternative, but their adoption is often limited by required calibration and the Midas Touch problem. We present EyeGestureLogin, a touch-free, knowledge-based method that uses gaze to enter lock patterns on a familiar 3 × 3 layout. EyeGestureLogin supports spontaneous walk-up use by replacing explicit calibration with an implicit, single-point offset estimation, and mitigates the Midas Touch using a short dwell-time trigger for fully hands-free input. The interface captures patterns by processing AOI-based fixations; we additionally evaluate an offline saccade-based detector. In a controlled study (n = 24), we achieved 91.59% accuracy (91.88% offline) with a mean entry time of 4.36 s. These results suggest that EyeGestureLogin enables fast and accurate hands-free authentication for public displays, motivating further evaluation under real-world deployment conditions.

Event detection (e.g., fixations and saccades) is a prerequisite for many eye-tracking analyses. However, existing solutions often require coding experience, rely on closed-source vendor tools, or focus on narrow paradigms (e.g., reading). Additionally, most are designed for stationary eye tracking, whereas head-mounted recordings are affected by head/scene motion, making event detection more difficult. Therefore, we present OpenGazeLab, a publicly available, browser-based toolkit that unifies event extraction, parameter configuration, and data inspection for both stationary and head-mounted eye tracking. OpenGazeLab implements I-DT and I-VT, and extends them for head-mounted data using scene-motion compensation and adaptive thresholds. The toolkit also provides a timeline-based visualisation that overlays gaze and detected events on the stimulus image or scene video, enabling quick visual verification. OpenGazeLab is implemented using widely used, well-maintained Python frameworks to support reproducible, easy-to-adopt workflows, and we plan to extend it with additional event classes (e.g., smooth pursuit) and alternative detectors.

A scanpath is an important concept in eye tracking that represents a person’s eye movements in a graph-like structure. Passive gaze-based interfaces, in which users do not consciously interact using their eyes, typically interpret users’ scanpaths to enable adaptive and personalised interaction. Despite the benefits of graph neural networks (GNNs) in graph processing, this technology has not been considered for that purpose. An example application is perceived relevance estimation, which still suffers from low classification performance. In this work, we investigate how and whether GNNs can be used to analyse scanpaths for readers’ perceived relevance estimation using the gazeRE dataset. This dataset contains eye tracking data from 24 participants, who rated the relevance of 12 short and 12 long documents in relation to a given query. The relevance was assigned either to an entire short document or to each paragraph within a long document, which allowed us to investigate two different GNN tasks. For comparison, we reproduced the gazeRE baseline using Random Forest and Support Vector classifiers, and an additional Convolutional Neural Network (CNN) from the literature. All models were evaluated using leave-users-out cross-validation. For short documents, the GNNs surpassed the baseline methods, with certain experiments showing an absolute balanced accuracy improvement of 7.6% and 14.3% over the CNN and gazeRE baselines, respectively. However, similar improvements were not observed in long documents. This work investigates and discusses the future potential of using GNNs as a scanpath analysis method for passive gaze-based applications, such as implicit relevance estimation.

Eye movements were shown to be an effective source of implicit relevance feedback in information retrieval tasks. They can be used to, e.g., estimate the relevance of read documents and expand search queries using machine learning. In this paper, we present the Reading Model Assessment tool (ReMA), an interactive tool for assessing gaze-based relevance estimation models. Our tool allows experimenters to easily browse recorded trials, compare the model output to a ground truth, and visualize gaze-based features at the token- and paragraph-level that serve as model input. Our goal is to facilitate the understanding of the relation between eye movements and the human relevance estimation process, to understand the strengths and weaknesses of a model at hand, and, eventually, to enable researchers to build more effective models.