The paradigm of web search is currently shifting from reactive information retrieval to Agentic AI, where proactive systems autonomously synthesise information to assist users. However, for these agents to be effective, they must understand which user parameters drive behaviour to resolve the personalisation cold-start problem. While cognitive architecture is a recognised factor, empirical evidence linking specific traits to web search interaction remains unclear. This paper investigates the Verbal-Imagery (V-I) cognitive style dimension and its influence on proactive search behaviour, mental workload (MWL), and overall search user interface (SUI) alignment. Through a controlled user study (N = 20), web search behaviours were evaluated using interaction logs and think-aloud protocols, while MWL and usability were assessed via NASA-TLX and the System Usability Scale (SUS). Our findings reveal that verbalisers and imagers adopt statistically distinct navigational preferences: ver-balisers prefer sporadic, reactive interactions, while imagers rely on structured, proactive synthesis such as the Knowledge Panel. It is worth noting that qualitative data identifies a "trust gap" in AI-generated overviews based on cognitive modality preferences. These results demonstrate that the V-I dimension is a critical parameter for user modelling in agentic systems. We conclude by proposing requirements for user-aware agentic information retrieval, providing a framework for agents to dynamically adapt their representation strategies to minimise cognitive friction and enhance trust in proactive computing environments. This work is licensed under a Creative Commons "Attribution 4.0 International" license.

Cognitive style, a user's habitual approach to information processing, offers a promising approach to personalising information searching (IS) systems, yet the underlying style-related search behaviours remain poorly understood. This study investigates how the Wholist–Analytic and Verbal–Imagery dimensions of cognitive style influence search behaviour and prefrontal cortex (PFC) activation during the Post-focus stage of exploratory search. Forty participants completed comparison search tasks while we recorded behavioural metrics, subjective workload ratings, and functional near-infrared spectroscopy (fNIRS) data. Our results demonstrate that cognitive style significantly predicted search engine results page (SERP) interaction patterns: analytics and imagers adopted structured navigation with detailed reading, whereas wholists and verbalisers preferred sporadic navigation with rapid scanning. Critically, fNIRS revealed distinct PFC activation patterns, specifically in the Ventrolateral PFC (VLPFC) and Dorsolateral PFC (DLPFC), corresponding to these behavioural differences. By mapping neuro-cognitive profiles to IR behaviours, this work provides empirical grounding for designing ''style-aware'' adaptive IR interfaces that tailor information density and navigational support to individual cognitive profiles.

Many studies have shown that collaborative tasks in immersive virtual reality (VR) and augmented reality (AR) environments can enhance collaborative outcomes in learning, training, and game-based contexts. However, the literature offers limited information on how these environments differentially shape social presence and collaborative engagement, particularly among unfamiliar users. This study addresses this gap by examining differences in social presence and collaborative engagement between VR and AR environments for unfamiliar pairs. A between-subjects experiment used an escape room game that featured three collaborative tasks, identically implemented in both VR and AR. The key difference was that the VR experience was stationary, while the AR experience required physical movement between rooms. The study involved 52 participants, divided into VR and AR groups, where two unfamiliar participants were paired into teams to complete the tasks. The results indicate significant differences in collaborative dynamics and user perception between the two environments. Specifically, VR pairs reported a stronger sense of immersion and flow state, whereas AR pairs demonstrated greater contextual awareness and behavioral coordination. Cybersickness measures also differed between conditions; given the locomotion mismatch, this pattern should be interpreted cautiously and not attributed to the environment alone. This finding improves understanding of the impact of immersive environments on collaborative processes and offers insights for designing collaborative XR applications (e.g., training and game-based teamwork), particularly for unfamiliar users.

The adoption of immersive virtual reality (IVR) for gamified rehabilitation is increasing. However, a significant challenge relates to perceptions of virtual environments as empty and isolating, potentially increasing stress, particularly among older users (patients). This paper explores the use of a virtual peer mentor (VPM) in a custom IVR-rehabilitation game to provide guidance and companionship. This game specifically targets patients recovering from acute myocardial infarction (AMI), commonly known as a heart attack. Grounded in social support theory, the VPM provides three types of support: (1) informational support, through pre-exercise narratives detailing a shared medical history; (2) instrumental support, through real-time demonstrations of clinically-validated exercise movements; and (3) emotional support, through positive feedback and encouragement. A within-subjects study involving 30 hospitalized AMI patients (all over 47 years old) evaluated the effectiveness of the VPM-integrated IVR-rehabilitation game. Each participant experienced a baseline (no VPM) and VPM-integrated version of the game on separate days. The results from the Intrinsic Motivation Inventory (IMI) and the social presence module of the Game Experience Questionnaire (GEQ-SPM) show that the VPM resulted in significant increases in engagement, and statistically significant lower pressure/tension. Furthermore, participants exhibited high user acceptance (76.7%) and task-completion rates (98.5%), with minimal cybersickness. The findings demonstrate that a psychologically-grounded VPM can effectively reduce stress in middle- and older-age patients in an IVR rehabilitation setting.

Large language models (LLMs) have rapidly become a powerful tool in automated code generation, yet most research has focused on their correctness and efficiency rather than the stylistic patterns of their outputs. In this preliminary study, we analyze the code patterns generated by five popular LLMs—ChatGPT, Gemini, Claude, Grok, and DeepSeek—in their free versions, across three LeetCode problems, one top-ranking each from the easy, medium, and hard categories. Our evaluation employs key metrics including inline comment density, naming conventions, and edge case handling, highlighting both similarities and differences in verbosity, comprehensibility, and robustness among the codes generated by models. The findings of this study have important implications for software engineering and education, suggesting that LLM-generated code can serve as both a tool for rapid prototyping and an effective learning resource for beginners. Our future work will extend this analysis to a broader set of coding challenges and compare LLM outputs with human-written code to develop robust criteria for evaluating automated code generation.

Cognitive style has been shown to influence users’ interaction with search interfaces. However, as a fundamental dimension of cognitive styles, the relationship between the Verbal-Imagery (VI) cognitive style dimension and search behaviour has not been studied thoroughly, and it is not clear whether VI cognitive style can be used to inform search user interface design. We present a study (N=29), investigating how search behaviour and mental workload (MWL) changes relate to VI cognitive styles by examining participants’ search behaviour across three increasingly complex tasks. MWL was subjectively rated by participants, and blood oxygenation changes in the prefrontal cortex were measured using functional near-infrared spectroscopy (fNIRS).

Immersive Virtual Reality (IVR) exergaming presents a promising avenue to integrate physical exercise with engaging virtual experiences, potentially encouraging sustained physical activity. However, maintaining user motivation over extended periods poses a significant challenge. Recent research has introduced the Dynamic Difficulty Adjustment (DDA) mechanism, dynamically regulating exergame difficulty based on specific conditions to enhance user adaptation. While prior studies have predominantly focused on gaming performance to adjust difficulty, they often overlook the emotional impact on user motivation. This study investigates users' emotional responses to the game timer change (TC) as a DDA mechanism during IVR exergaming. Results indicate that subjects in the TC-implemented game displayed more neutral emotions, concomitant with improved gaming performance. Conversely, subjects in the game without TC exhibited a broader range of detected emotions (sad and happy), suggesting difficulties in adapting to in-game difficulty levels incongruent with their gaming abilities. Overall, this study establishes a foundation for future research in affective computing-based IVR exergaming, aiming to develop an intelligent autonomous DDA mechanism tailored to users' physical and mental conditions.

Observational studies are widely used in Human-Computer Interaction (HCI) research to evaluate usability and user experience with technologies. However, the act of observation may influence participant behaviour and performance, threatening the validity of study findings. This paper investigates the impact of three observation types on participant outcomes in a simulated HCI study context. Participants completed Sudoku puzzles under baseline (no observation), human observation, sensor-based observation, and combined human/sensor conditions. Performance was assessed by puzzle completion rates. The mental workload was measured via NASA-TLX surveys, heart rate, galvanic skin response, and infrared thermal imaging. Results showed observations negatively impacted performance versus baseline, with human observers inducing the greatest distraction. Experienced participants were more influenced than novices. Task medium also affected engagement and observation reactivity. Findings demonstrate observations introduce bias in HCI research, emphasising careful consideration of observation methods to improve result validity.

This paper presents a pilot study that examines the capacity of novice testers to generate Metamorphic Relations (MRs) for autonomous driving systems (ADSs), specifically fo-cusing on parking functions. By comparing MRs generated by human participants with those generated by artificial intelligence (AI), we seek to understand the variances in quality, particularly in terms of correctness, applicability, novelty, and utility. Our findings indicate that despite receiving only minimal training, human participants were capable of producing MRs with a wide range of effectiveness. Notably, humans exhibited a potential for creative thinking, contrasting with AI's ability to generate MRs that adhere closely to technical and applicability standards. The study underscores the need for improved educational strategies aimed at enhancing the quality and confidence of MRs produced by humans. Future research directions will explore the optimization of training approaches, particularly within a constrained timeframe to create a positive learning experience and maintain participant engagement, to fully harness the creative capabilities of human learners in the context of ADS testing.

This study introduces a website serving as an Open Educational Resource (OER), dedicated to Metamorphic Testing (MT) and Metamorphic Relation (MR) generation, with a specific focus on Autonomous Driving Systems (ADSs). It offers a comprehensive introduction to MT and ADSs, and presents a specially designed scenario template that simplifies the MR generation process for ADS functions. This template enhances accessibility, making it more user-friendly for a wider audience, and facilitates systematic application, ensuring that users can apply test case and MR generation in a structured and organized manner. The MR generation guidelines that work with the template lower the learning barrier for beginners in MT, thus facilitating easier adoption and application of MT to ADSs.

We introduce a web-based SQL query visualisation tool aimed at improving students' understanding of SQL query execution. The tool, available as an Open Educational Resource (OER), allows users to visualise the stages of a SQL query on an uploaded SQLite database. The web interface enables students to interactively explore each stage of the query and view results in a dynamically updated table. This initial version of the tool is intended to inspire further development and enhancement while providing educators with a valuable resource for teaching SQL concepts.

ABSTRACT The proliferation of driver‐assistance features in vehicles has resulted in a growing interest among the public in fully autonomous driving systems (ADSs). However, the integration of software and hardware in these complex systems presents significant testing challenges, particularly with respect to ensuring passenger safety. To address these challenges, simulation has emerged as a crucial step in the testing of ADSs. This paper presents a solution to the challenges faced in testing ADSs, with a focus on the validation of ADS simulators. The proposed approach involves using simulations and metamorphic testing (MT) to generate multiple concrete metamorphic relations (MRs) for testing ADS simulators. In order to accomplish this goal, we introduce three metamorphic relation patterns (MRPs). Each MRP is accompanied by a metamorphic relation input pattern (MRIP) that aids in generating detailed MRs. These MRs are designed to identify potential issues within the ADS simulator. To simplify the testing process and facilitate MT for testers, a self‐evolving scenario‐testing framework is also presented. The framework allows testers to improve test cases and MRs iteratively until issues detected are confirmed. The benefits and limitations of the framework are demonstrated using an industry case study. Overall, this study offers a practical solution to the challenges in testing ADSs and provides useful insights into improving testing efficiency for researchers and practitioners in the field.

Automated Driving Systems (ADSs) have gained popularity recently. However, the unstable and unsafe ADSs have caused many traffic accidents and received widespread attention. One way to alleviate such issues is to enhance the correctness and efficiency of testing ADSs. Due to the difficulty of checking ADSs’ behavior such as parking the car, confirming the correctness of the actual behavior may be non-trivial or impossible. This kind of problem is called the test oracle problem. Unlike traditional software testing, Metamorphic Testing (MT) does not focus on the correctness of the actual strategy but examines whether or not the inputs and outputs of multiple executions of a Software Under Test (SUT) satisfy certain relations of the SUT, called Metamorphic Relations (MRs). The paper also implements Mutation Analysis (MA) on Baidu Apollo ADS to evaluate our MT. MA involves small modifications to a program’s source code to see if test-cases can detect these changes. This work was part of a larger endeavour to create an Open Educational Resource (OER) to support learning about how to apply MT to ADSs. This paper reports on an experience of implementing MT to test the Automated Parking System (APS) of Apollo ADS and applying MA to evaluate the MT.

Indoor positioning in the firefighting ground shows promising application prospects for enhancing rescue safety and efficiency. Low visibility and signal interference caused by smoke pose significant difficulties for visual Simultaneous Localization and Mapping (SLAM) systems and radio frequency-based localization methods, while the performance of existing pedestrian dead reckoning (PDR) methods is affected by unpredictable user gaits. This paper introduces a gait analysis-based PDR (GA-PDR) for deriving heading estimation in PDR to improve its localization performance. The proposed method determines the step pattern by analyzing the features of inertial measurement unit data, thereby enabling the classification of forward, left- and right-turn and around-turn from left or right-side movements. In addition, this study introduces a redundant turn elimination method to differentiate false positive patterns via a time-domain heading analysis for turn movements. The location tracking performance with the proposed GA-PDR approach is validated using a self-created dataset established under a smoke-filled experiment, the results of which indicate a lower loop closure error compared with the traditional PDR in all of the tested scenarios.

The demand for firefighting has significantly risen in recent decades, accompanied by increased risks faced by firefighters. Tragic incidents, such as the Shanghai factory fires, have resulted in the loss of over thirty firefighter lives. One of the primary contributing factors is the abrupt breakdown in communication between firefighters inside a building and the commanding officer stationed outside, attributable to the harsh and complex indoor environment. This study aims to conduct a comparative analysis of different widely used wireless transmission methods, including Wi-Fi, Bluetooth Low Energy (BLE), and Long Range (LoRa). The experiments are conducted in a two-room setup, with two brick walls acting as a barrier. A wireless data transmitter is placed in one room, while smoke is generated. A receiver placed at varying distances collects the signal strengths. The findings indicate that LoRa exhibits the least drop in signal strength compared to the other methods. In contrast, BLE shows high signal strength variation for the same distances and is not recommended for firefighting communication purposes. This study provides valuable insights for selecting suitable wireless communication modules, particularly in the design of wearable devices for assessing safety risks faced by firefighters.

Smart health homes (SHHs) integrate wearable sensors and various interconnected devices using the Internet of Things (IoT) technologies. SHHs combine IoT, data communication, and health-related applications to deliver healthcare services at home. The existing regulations and standards for SHH design are insufficient for home health care. Technical and device standards are available for guiding SHH design and implementation, but ethical standards are lacking. We identified six ethical requirements important for SHH: safety/trust, privacy/data security, vulnerable groups, individual autonomy, transparency/explainability/fairness, and social responsibility/ morality. We identified a set of questions useful for software engineering (SE) process for ethically informed software in SHH design and mapped them to the steps of software process. We mapped related guidelines from relevant professional codes of conduct. These questions can guide ethically informed software process of SHH.

Computing systems are becoming increasingly complex and sophisticated. Technologies such as artificial intelligence, big data, and autonomous vehicles are pushing the boundaries of system size, complexity, and comprehensibility beyond anything seen before. These advances, however, have left the associated software quality assurance (SQA) tools and processes behind. This is compounded by many training and education programs also not attempting to address this inadequacy in the preparation of future software engineering professionals. We face a situation of extensively-deployed advanced computing systems, many of which lack sufficient SQA support. Metamorphic Testing (MT) and Metamorphic Exploration (ME) are SQA approaches that have a record of being able to alleviate some of the challenges associated with the advanced computer systems. This paper reports on an MT/ME experience with the Baidu Apollo autonomous driving system (ADS). The experience included identifying an apparent problem in Apollo, which was later confirmed to be a misunderstanding, but which illustrated the potential for ME to scaffold learning how to perform SQA on such complex systems. The report will be of benefit not only to other ADS developers and testers, but also to other SQA professionals, and especially to SQA trainers and educators.

Awareness of web accessibility issues is necessary for, amongst other things, good website design. Good website design can mean the difference between disabled users being able to access the website content, or not. This paper describes the impact of a student-led project to develop a VR application, as an Open Education Resource (OER), to increase users’ knowledge and awareness of accessibility. An evaluation for the intervention delivered by the VR application was conducted, according to which, the VR application generally increased knowledge and awareness of web accessibility, but also had a negative impact on some users. The project targetted a Human Computer Interaction (HCI) class taught at the first Sino-foreign higher education institution, University of Nottingham Ningbo China (UNNC). UNNC has already been involved in research into flipped classrooms, technology-enhanced teaching, and the development of several OERs. This paper introduces the background, motivation and objectives, design, and specification of the VR application. The impact of the application on users is evaluated, and some possible future work is discussed.

COVID-19 has caused heart-break and disruption. The People's Republic of China (PRC) was one of the first severely-impacted countries, but also one of the first to attempt to return to normalcy. Education, including Higher Education-the context of this paper-was disrupted, and the impact of these disruptions continues to be felt. A small consolation, or silver lining, has been the incredible efforts and innovations made by teachers to overcome the COVID-19 challenges. One such innovation is described in this paper: Drawing on the traditions of Metamorphic Exploration and Testing, the authors applied two different approaches to resolving a technical administration problem related to the management of assessments and marks for undergraduate software engineering team projects. This took place at University of Nottingham Ningbo China (UNNC), a Sino-foreign Higher Education Institution (HEI) with a history of staff-student collaboration, and education innovation. This paper outlines the experience and the insights learned from it. In addition to being of general interest to education administrators, especially those involved with project and marking administration, the innovative use of Metamorphic Testing ideas to develop the solution will be particularly interesting to computer scientists and software engineers.

As computer interfaces spread across and even into our bodies, so we find ourselves engaged in ever more deeply connected bodily interactions. From the mouse and keyboard, to wearables and gestural...

Smart pervasive sensor networks are becoming an important part of our daily lives. Low-power, high-availability and high-throughput 5G mobile networks provide the necessary communication means for highly pervasive sensor networks, introducing a technological disruption to health monitoring. The meaningful use of large concurrent sensor networks in healthcare requires multi-level health knowledge integration with sensor data streams. In this paper, we highlight some software engineering and data-processing issues that can be addressed by metamorphic testing. The proposed solution combines data streaming with filtering and cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices.

This paper explores the design space of bio-responsive entertainment, in this case using a film that responds to the brain and blink data of users. A film was created with four parallel channels of footage, where blinking and levels of attention and meditation, as recorded by a commercially available EEG device, affected which footage participants saw. As a performance-led piece of research in the wild, this experience, named #Scanners, was presented at a week long national exhibition in the UK. We examined the experiences of 35 viewers, and found that these forms of partially-involuntary control created engaging and enjoyable, but sometimes distracting, experiences. We translate our findings into a two-dimensional design space between the extent of voluntary control that a physiological measure can provide against the level of conscious awareness that the user has of that control. This highlights that novel design opportunities exist when deviating from these two-dimensions - when giving up conscious control and when abstracting the affect of control. Reflection on of how viewers negotiated this space during an experience reveals novel design tactics.

In this paper we present #Scanners, a digital arts installation that aims to bridge the gap between digital arts and neuroscience. #Scanners is an experience in which an individual wears a wireless brain scanners whilst being presented media which is dynamically affected by the individuals physiology. A prototype system has been successfully trialled on roughly 100 users over the past 18 months and has received unanimously positive feedback. We state the minimal additional requirements for demonstrating a Higher Fidelity prototype system and argue the value of including #Scanners at C&C2015.

Recent efforts have shown that functional near-infrared spectroscopy (fNIRS) has potential value for brain sensing in HCI user studies. Research has shown that, although large head movement significantly affects fNIRS data, typical keyboard use, mouse movement, and non-task-related verbalisations do not affect measurements during Verbal tasks. This work aims to examine the Reliability of fNIRS, by 1) confirming these prior findings, and 2) significantly extending our understanding of how artefacts affect recordings during Spatial tasks, since much of user interfaces and interaction is inherently spatial. Our results show that artefacts have a significantly different impact during Verbal and Spatial tasks. We contribute clearer insights into using fNIRS as a tool within HCI user studies.

The Think Aloud Protocol (TAP) is a verbalisation technique widely employed in HCI user studies to give insight into user experience, yet little work has explored the impact that TAPs have on participants during user studies. This paper utilises a brain sensing technique, fNIRS, to observe the effect that TAPs have on participants. Functional Near-Infrared Spectroscopy (fNIRS) is a brain sensing technology that offers the potential to provide continuous, detailed insight into brain activity, enabling an objective view of cognitive processes during complex tasks. Participants were asked to perform a mathematical task under 4 conditions: nonsense verbalisations, passive concurrent think aloud protocol, invasive concurrent think aloud protocol, and a baseline of silence. Subjective ratings and performance measures were collected during the study. Our results provide a novel view into the effect that different forms of verbalisation have on workload during tasks. Further, the results provide a means for estimating the effect of spoken artefacts when measuring workload, which is another step towards our goal of proactively involving fNIRS analysis in ecologically valid user studies.