http://bura.brunel.ac.uk/handle/2438/8642 2026-07-06T09:45:36Z http://bura.brunel.ac.uk/handle/2438/33565 Title: The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study Authors: Kopka, M; Majeed, A; Spinelli, G; El-Osta, A; Feufel, M Abstract: Artificial Intelligence (AI) tools such as large language models (LLMs) are increasingly used by the public to obtain health information and support health-related decisions. Because such use involves advice-taking, following or rejecting AI-generated advice can have direct clinical and safety implications and consequences for the healthcare system. Although trust plays an important role in the adoption of health-related AI advice, existing instruments only assess trust in generic technology and perceived trustworthiness. There are currently no validated instruments that specifically measure users’ state trust in AI-generated health advice, and an alternative for self-developed one-item scales is missing.This study aimed to develop and validate the Trust in AI-Generated Health Advice (TAIGHA) scale and its four-item short form (TAIGHA-S) as theory-based questionnaires for measuring trust and distrust in AI-generated health advice. We used a generative AI approach to generate new use-case specific candidate items based on existing theory that each comprised cognitive and affective components. After automated validation, we conducted manual validation in three steps: (i) content validation with ten domain experts, (ii) face validation with 30 lay participants, and (iii) psychometric validation with 385 UK participants receiving AI-generated health advice for symptom assessment. After automated item reduction, 28 items were retained and further reduced to 10 based on expert ratings. The final TAIGHA scale showed excellent content validity (S-CVI/Ave = 0.99) and face validity (S-FVI/Ave = 0.99). CFA confirmed a two-factor model with excellent fit (CFI = 0.98, TLI = 0.98, SRMR = 0.03). Internal consistency was high (α = 0.94, ω = 0.94 for trust; α = 0.93, ω = 0.93 for distrust). The short form correlated highly with the full scale (r = 0.96) and showed high reliability (α = 0.88, ω = 0.89 for trust; α = 0.84, ω = 0.85 for distrust). TAIGHA and TAIGHA-S are validated instruments for assessing users’ state trust and distrust in AI-generated health advice, with excellent psychometric properties and stronger associations with reliance than existing general trust scales. Description: Data Availability: The data is openly accessible via Zenodo: https://doi.org/10.5281/zenodo.19355964 .; Supporting information is available online at: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001488#sec030 . 2026-07-02T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33548 Title: Making the invisible visible: Supporting older adults’ expression of emotional needs for companion robots through visual co-design tools Authors: Xu, C; Dong, H; El Souri, M; Wang, M Abstract: As populations age, companion robots are increasingly explored to support emotional well-being in later life. However, older adults often find it difficult to express abstract emotions or imagine interactions with unfamiliar technologies, limiting their participation in early-stage design. This study develops and validates a structured toolkit designed to support emotional articulation and co-design participation. The toolkit, consisting of companionship cards, robot role cards, and recording canvases, was refined through expert evaluation and user testing, and validated through comparative interviews with older adults (n=10). Results show that the toolkit supported richer emotional expression, smoother dialogue, and more sustained engagement, helping participants transform lived emotional experience into design-relevant insights. The study contributes a replicable methodological approach for emotion-centred participatory design, showing how structured visual scaffolding can foster emotional and conceptual inclusion, empower older adults to articulate their perspectives, and help their voices more actively shape more resonant Human–Robot Interaction. 2026-06-08T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33546 Title: A hierarchical and ontology-based taxonomy of stimuli–smart materials–transformation effects in 4D printing as a new interactive modality Authors: Qin, J; Pei, E; Cifter, A Abstract: 4D printing functions not merely as a smart manufacturing technique but as an interactive material technology: materials respond to human or environmental stimuli and generate a direct feedback loop. This study proposes a taxonomy that links Stimuli, Smart Materials, and Transformation Effects, developed through a combined Hierarchical Classification and Ontology-Based Classification approach grounded in a systematic literature review and ontology mapping. By organising complex stimulus–material–effect relationships into a designer-accessible structure, the taxonomy supports non-technical designers in identifying feasible pathways from intended interactions to material outcomes. It also provides a structured data foundation for developing 4D printing design guidelines and toolkits, enabling more direct translation of 4D printing concepts into real projects. Positioned as an interface between human intent and material expression, the proposed framework highlights how 4D printing can enable new forms of interaction design and fluid, responsive artefacts across design domains. 2026-06-08T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33466 Title: Bridging human insight and automation: improving alt text generation with human-curated contextual data Authors: Droutsas, N; Spyridonis, F; Daylamani-Zad, D; Glass, PE; Ghinea, G Abstract: The rapid growth of image-based multimedia content on the Web has intensified the challenge of generating high-quality alternative (alt) text descriptions, which is an essential requirement for inclusive online experiences for people with visual impairments. Although recent advances in machine learning (ML) have enabled large-scale automated alt text generation, the accessibility value of such outputs remains limited. This is due to the context-agnostic datasets used to train existing models, resulting in generic descriptions that fail to meet users’ needs in alt text. In this work, we introduce and utilise a human-curated, context-driven dataset of alt text descriptions to train two proof-of-concept ML models aimed at improving alt text quality. We evaluate these models within a controlled, reproducible pipeline and demonstrate that context-aware training leads to statistically significant improvements in human-perceived alt text quality compared to a model trained without contextual inputs. We further examine the role of context-dependent routing and the integration of contextual cues in shaping generated descriptions, both of which are critical but underexplored aspects of alt text accessibility. The findings highlight the value of structured, human-curated contextual data in advancing ML-supported alt text generation and point towards opportunities for hybrid human-AI approaches to inclusive web design. Description: Data availability statement: The data collection protocol using a GWAP is currently under review for separate publication; data and detailed collection protocols will be made publicly available upon acceptance and can be provided upon reasonable request. For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) Licence to any Accepted Author Manuscript version arising from this submission. 2026-06-05T00:00:00Z