Journal of Cognitive Human-Computer Interaction

When governments use algorithmic systems to determine eligibility for housing support, welfare benefits, or social services, the citizens whose lives are most directly affected are often the least equipped to understand, scrutinise, or challenge the outcomes. Standard decision notices provide statutory reference numbers and outcome statements without any meaningful account of which data was used, why the algorithm produced the result it did, or what a citizen can realistically do next. This accountability gap is not merely a design inconvenience; it erodes the procedural fairness that democratic governance requires, and it disproportionately affects the most vulnerable service users. This paper reports a three-phase research programme in which a principled transparency framework for citizen-facing algorithmic decision interfaces was developed and validated through sustained engagement with end users. A needs assessment with 142 citizens and 18 civil servant interviews established what transparency citizens actually require. Three iterative co-design workshops with 24 citizens and 8 frontline officials produced progressively refined interface prototypes organised around three distinct transparency layers—process disclosure, rationale explanation, and contestation support. A subsequent think-aloud evaluation with 36 citizens compared four interface conditions ranging from the current opaque standard to the full three-layer framework. The fully layered interface substantially outperformed the existing standard and all partial implementations across trust, perceived actionability, comprehension, and transparency satisfaction. The paper contributes the framework itself as a theoretically grounded and empirically validated design resource, a set of evidence-based design guidelines derived from across all three study phases, and a replicable participatory methodology for involving affected citizens in the design of AI governance interfaces.

Virtual reality interface quality is not determined by visual appeal alone. A scene may look convincing while still producing unstable gaze, uncomfortable depth switching, excessive head movement, or slow target selection. This paper presents a scene-level assessment framework for measuring comfort, legibility, and spatial control in VR interfaces. The work is deliberately organized as a design-science evaluation rather than as a conventional classifier study: it begins with interface failure mechanisms, defines observable headset and scene variables, computes a Virtual Reality Interface Comfort score, and then translates the results into review actions. The empirical analysis uses a processed feature-level extract aligned with public VR eye-tracking task structures and combines gaze stability, pupil variability, vergence error, head-turn demand, tracking loss, selection latency, contrast balance, target comfort, depth pressure, and spatial-memory support. The results indicate that comfortable VR scenes are characterized by stable fixation, consistent depth placement, strong spatial memory support, and modest interaction latency, while high-risk scenes are mainly associated with head-turn demand, tracking loss, pupil variability, and depth pressure. The paper contributes a transparent measurement model, a set of scene pattern diagnostics, and a practical governance workflow for deciding when a VR interface should be released, revised, or retested.

Stories have always been the primary medium through which human beings share emotions, build empathy, and make sense of experience. The emergence of large language models capable of generating coherent, contextually rich narratives raises a fundamental question for human-computer interaction: when a story is generated by a machine, does it still carry the emotional weight and imaginative pull of one written by a human, and can the design of the interaction itself amplify or diminish that pull? This paper reports a controlled within-subjects experiment in which thirty-six participants read or actively co-shaped stories produced by a large language model under four conditions that crossed two levels of interactivity—passive reading versus branching-choice interaction—with two levels of personalisation—generic narrative versus one adapted to the participant’s stated interests and preferences. Emotional engagement was measured through narrative transportation, positive and negative affect, sense of narrative agency, trust in the AI narrator, and perceived story quality. The study finds that both interactivity and personalization independently increase emotional transportation, and that their combined presence produces an amplified effect that is larger than either factor alone, while trust in the AI narrator emerges as a partial mediator of the personalization advantage. Individual differences in baseline narrative engagement propensity predict the magnitude of benefit from the most engaging condition, providing actionable guidance for adaptive storytelling interface design.

Human-AI collaboration can improve decision quality only when users know when to rely on an AI recommendation and when to resist it. Explanations are often proposed as a remedy, but explanation content can also intensify automation bias or reinforce a user’s initial belief. This paper presents a cognitive explanation selection model for mitigating over-reliance and under-reliance in AI-assisted decision tasks. The study compares no explanation, feature-based, contrastive, example-driven, and hybrid explanations across simulated novice, intermediate, and expert decision makers using a public medical decision dataset as the task substrate. The analysis focuses on reliance behaviour rather than on model accuracy alone. The proposed model estimates when the user is likely to accept a wrong recommendation, reject a correct recommendation, or accept advice simply because it confirms an initial judgment. The results indicate that contrastive and hybrid explanations are more effective for reducing automation bias, while example-driven explanations preserve trust for lower-expertise users. The paper concludes with a transparent interface loop for high-stakes environments in which explanation style is selected according to user expertise, AI confidence, and human-AI agreement.

High-stakes computer work often requires users to interpret dense visual information while responding to timesensitive events. Static interfaces can become counterproductive in such conditions because the amount of information presented to the user does not change when mental demand rises. This paper presents an adaptive interface personalization approach that detects cognitive load from pupillometry, heart-rate variability, gaze behaviour, and interaction traces, then selects a transparent interface response. The proposed approach does not simply reduce screen content; it chooses between full, highlighted, simplified, and critical-only modes while preserving user control and explanation cues. A feature-level experimental analysis was conducted using a multimodal workload table structured around public cognitive-load datasets and high-stakes monitoring tasks. The results show that pupil expansion, lower HRV, response delay, gaze dispersion, and screen density jointly indicate rising cognitive load. The adaptation policy reduced predicted interaction errors and shortened response latency in high-load windows while maintaining explanation support for user trust. The findings suggest that cognitive-load detection should be treated as a personalization service rather than a hidden automation layer.

Computer usability is often discussed through screen layout, navigation, and task flow, although the experience of using a computer also depends on whether processor, memory, storage, and network resources remain available when the user needs them. This paper develops a Computer Resource Usability Index (CRUI) for interpreting virtual-machine resource traces as indicators of user-facing usability risk. The proposed index converts CPU, memory, disk, and network measurements into a bounded resource-friction score and then maps this score into four actionable usability states: comfortable, watch, constrained, and strained. The analysis uses a processed extract following the public GWA-T-12 Bitbrains trace structure, which records VM-level resource metrics for enterprise applications. The results show that resource usability is not explained by CPU usage alone; imbalance across resource channels, I/O pressure, and variability also contribute to predicted friction. The findings provide a practical bridge between infrastructure monitoring and cognitive HCI by translating low-level resource traces into interface-relevant decisions such as when to defer background tasks, warn the user, or allocate additional headroom.

Efficient retrieval of relevant legal cases is critical for judicial decision-making, particularly for high-severity crimes where timely reference to precedents can influence outcomes. Our work presents IHLawRecommender, i.e., Intelligent Hybrid Law Recommender, a hybrid framework for recommending Indian Penal Code (IPC) cases based on textual descriptions provided by users. The system operates through a multi-stage workflow: first, case descriptions are normalized to remove inconsistencies and embedded into semantic vectors using a Bi-directional Long Short-Term Memory (BiLSTM) network. These embeddings are compared with the user query to measure semantic similarity. In parallel, an IPC-specific keyword map evaluates the relevance of each case, while legal aware filters distinguish between sexual and non-sexual violent crimes to ensure contextually appropriate recommendations. The outputs from these stages are integrated using a weighted payoff function that considers semantic similarity, keyword relevance, and crime severity to produce a ranked list of top-k cases. The system also provides interpretable visualizations, including heatmaps that illustrate correlations between similarity, keyword score, severity, and payoff. Evaluation on a curated IPC dataset demonstrates that IHLawRecommender consistently prioritizes legally critical cases, reduces irrelevant matches, and offers a practical, workflow-driven tool for legal professionals to efficiently navigate case law while maintaining adherence to judicial priorities.

New cybersecurity and operational resilience issues have been brought about by the growing use of cloud managedsmart street lighting in metropolitan settings, especially in the event of network partitioning and Distributed Denial of Service (DDoS) assaults. Current systems still rely mostly on centralized cloud control, which creates a single point of failure that might compromise public safety and interfere with vital lighting functions. In the context of the author’s Streetlight-as-a-Service (SLaaS) framework, where streetlights operate as intelligent, service-capable infrastructure nodes rather than discrete lighting devices, this paper proposes Guardian Light, an edge-resilient fail-safe mechanism for intelligent street lighting. The suggested design uses AWS IoT Core, AWS IoT Device Defender, and AWS IoT Greengrass to combine device-side autonomous governance with cloud-side anomaly detection. With the help of an internal real-time clock, state-aware failover logic, persistent offline scheduling, and local threshold monitoring, Guardian Light makes it possible for lighting nodes to continue operating safely and consistently even in the event that malicious traffic is discovered or cloud connectivity is compromised. The study emphasizes how current smart lighting research goes beyond energy saving and scheduling to cyber-resilient operational continuity through the integration of edge intelligence and service-oriented streetlight design. By doing this, the study offers a workable and theoretically sound solution to improve the autonomy, security, and dependability of next-generation SLaaS-enabled smart city systems.