Mohammad Naiseh

Use of internet-based technologies (IBTs) and artificial intelligence (AI) via eHealth could enhance mental healthcare delivery and accessibility. However, ethical concerns, perceptions of AI capabilities, past experience, and individual differences can influence acceptance and engagement. We qualitatively explored attitudes and behaviours toward IBTs, AI and eHealth, given the overlapping nature of these digital environments, trust, and the possible role of mental health. This exploratory study conducted semi-structured interviews between March and October 2023 with 22 UK-based participants: eight with lived experience of a mental health problem and 14 without. There was no clear difference in participants’ levels of trust in IBTs across mental health status. Participants reported using IBTs despite privacy concerns. Trust was influenced by perceptions of developer trustworthiness, individual traits, and affective responses to IBTs. Convenience, ease of use, and lack of alternatives often outweighed concerns, driving engagement. Our findings highlight that in this group trust is a key factor in IBT and eHealth uptake, but not the sole determinant, highlighting ethical concerns and responsibilities for both UK healthcare providers and developers. There is a need for transparent, ethically grounded, value-sensitive design and implementation of IBTs and AI-enabled eHealth tools that supports genuine user choice, voluntary engagement, and human interaction.

Use of internet-based technologies (IBTs) and artificial intelligence (AI) via eHealth could enhance mental healthcare delivery and accessibility. However, ethical concerns, perceptions of AI capabilities, past experience, and individual differences can in-fluence acceptance and engagement. We qualitatively explored attitudes and behaviours toward IBTs, AI and eHealth, given the overlapping nature of these digital environments, trust, and the possible role of mental health. This exploratory study conducted semi-structured interviews between March and October 2023 with 22 UK-based participants: 8 with lived experi-ence of a mental health problem and 14 without. There was no clear difference in participants’ levels of trust in IBTs across mental health status. Participants reported using IBTs despite privacy concerns. Trust was influenced by perceptions of de-veloper trustworthiness, individual traits, and affective responses to IBTs. Convenience, ease of use, and lack of alternatives often outweighed concerns, driving engagement. Our findings highlight that in this group trust is a key factor in IBT and eHealth uptake, but not the sole determinant, highlighting ethical concerns and responsibilities for both UK healthcare pro-viders and developers. There is a need for transparent, ethically grounded, value-sensitive design and implementation of IBTs and AI-enabled eHealth tools that supports genuine user choice, voluntary engagement, and human interaction.

Appropriate reliance on AI, following system recommendations when correct and overriding them when incorrect, is the central behavioural objective of human–AI interaction research. Despite the importance of this objective, researchers draw on a heterogeneous set of behavioural, self-report, performance-based, and process-level measures, with little principled guidance on their suitability and no shared reporting standard to enable cross-study comparison. This paper presents a systematic review of how reliance is measured across 67 HCI papers published between 2014 and 2026. We develop a taxonomy of four measurement categories and evaluate each against seven methodological dimensions grounded in measurement theory and human factors research. Sixty-one percent of included papers combine two or more measure types, yet the majority report divergent signals across instruments. We identify five structural problems limiting cumulative progress: construct ambiguity across definitions, the conflation of trust and reliance as interchangeable constructs, static measurement of an inherently dynamic construct, an ecological validity gap driven by crowdsourced single-task designs, and measurement fragmentation that prevents cross-study synthesis. We translate this diagnosis into a prescriptive measurement framework providing measure selection guidance by research question type and a minimum reporting standard applicable to all reliance studies.

Defence often uses rationalistic approaches for the adoption of AI, but we question whether this is appropriate. By studying adoption using naturalistic approaches in the work environment where AI is actually employed, we better can capture situated issues around trust and adoption. We conducted a longitudinal study of expert intelligence analysts adopting a machine-vision Object Detection and Recognition Overlay (ODRO) within their mission teams and tooling, premised on improving analyst functions. We employ and adapt naturalistic decision-making models to identify key situational challenges, showing that naturalistic approaches can better inform Defence’s AI adoption. Specifically, we find that organisational rewards for accuracy influence analysts’ willingness to adopt and rely on imperfect tools, and that analysts develop a non-uniform relationship with ODRO over time, which we translate into design implications. Crucially, our results support a model of interdependence over prevalent task-allocation paradigms that assume human and AI as functionally divided, rational entities.

The design of AI systems prioritises frictionless interaction, often at the cost of human judgement and accountability. This contribution discusses designed friction—deliberate inefficiencies designed to foster engagement, critical reasoning, and user agency. Drawing from friction-in-design and responsible AI, we explore key mechanisms such as cognitive forcing functions, seamful design, and programmed inefficiencies. We propose a research agenda for Hybrid Human-AI systems that balance usability and trust through frictional AI, aiming to provide a stepping stone for the future development of a dedicated research community.

This paper explores the complex relationship between social media problematic use and their contribution to well-being. While social media platforms offer opportunities for connection, information access, and self-expression, concerns have arisen regarding their impact on mental health and in some cases their obsessive use leading to conflict with other life priorities. This study investigates the duality of social media’s effects, recognising that its impact on well-being can be both beneficial and harmful, depending on personal, psychological, and situational factors. Additionally, the paper examines whether problematic social media use (PSMU) correlates with perception of social media’s contribution to well-being (SM-WB) and explores common influencing factors and motivations. The data was collected via an online survey conducted among two distinct populations, the Arab (281, 141 females) and the UK (281, 155 females). We employed PERMA profiler to assess SM-WB whereas PSMU was measured through Social Media Disorder (SMD) scale. The results show that PSMU symptoms of preoccupation, tolerance, and withdrawal correlates positively with SM-WB in both Arab and UK samples. In a linear regression model, internal locus of control predicted SM-WB, whereas external locus of control and social media usage time predicted PSMU in both Arab and UK samples. Competence in using social media significantly predicted both PSMU and SM-WB in the Arab sample. These findings underscore the complexities of promoting healthier social media use and question the effectiveness of simply suggesting the reduction of usage as the sole solution, considering that usage can also enhance well-being.

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators’ workload or affecting the system's usability.