As AI systems become increasingly prevalent, older adults engage with them through diverse approaches, ranging from active use to deliberate non-use. But how does trust in AI differ across these orientations? Our explanatory sequential mixed-methods study, including a survey (n = 217) and interviews (n = 20), reveals a paradox: older adults who actively use AI and those who intentionally choose not to report similarly moderate levels of trust, yet arrive at that trust through fundamentally different pathways. Among self-identified users, greater AI familiarity predicts higher trust, mediated by growing confidence through hands-on experience. Among non-users, the pattern reverses: the most AI-familiar individuals report the lowest trust, as knowledge without direct engagement fosters skepticism rather than confidence. Interview findings unpack the sense-making behind these patterns, revealing two distinct logics: practice-based trust, built through iterative use, error calibration, and continuous verification; and delegated trust, maintained by routing AI-related decisions through accountable human intermediaries while personally opting out. We discuss implications for inclusive AI design and literacy that support older adults' distinct pathways to trust, validating non-use as an informed decision rather than a deficit. RESEARCH HIGHLIGHTS Older AI users and non-users report similarly moderate trust, yet reach it through different sense-making logics.Familiarity raises trust among those who use AI but deepens skepticism among those who do not, driving trust in opposite directions.What distinguishes the two groups is whether they see AI errors as calibration cues or moral breaches.Users build resilient, practice-based trust through iterative use, error calibration, and continuous verification of AI outputs.Non-users sustain a delegated trust, granting AI conditional legitimacy only when an accountable person or institution vouches for its outputs.Self-reported use and non-use form a porous spectrum rather than a clean binary, complicating how we measure and design for AI engagement.
With recent developments in the life sciences and computer technology blurring the boundaries between organisms and artifacts, innovative applications of microorganisms in the field of human-computer interaction (HCI) and Biodesign have fueled the development of Microbe-HCI as a new area of investigation. As an emerging interdisciplinary research field, Microbe-HCI challenges existing knowledge structures and previous approaches of designers and HCI researchers. Using the PRISMA (Preferred reporting items for systematic reviews and meta-analyses) framework, this systematic literature review analyzes 53 selected articles from the Scopus database, the ACM (Association for computing machinery) database, and Google Scholar to examine the developmental pathways, microbial categories, and research themes of Microbe-HCI. We quantitatively analyze the literature data to explore the constituent elements of the biological interaction system and identify the role of microbes within the interaction system components. The results of the research theme categorization show six areas of focus in current Microbe-HCI research: maintaining microbial activity, microbial function, future-oriented speculative bioethics, education and entertainment, interactive living media, design methods, and tools. Based on the results, we discuss the implications and future research directions of Microbe-HCI from the perspectives of sustainability and bioethics, encouraging interdisciplinary researchers and designers to further explore the integration of microbial and interactive technologies. RESEARCH HIGHLIGHTS This systematic literature review maps the emerging field of Microbe-HCI by clarifying its definition, development pathway, microbial applications, interaction system components, and dominant research directions.Using the PRISMA framework, this systematic review analyzed 53 studies and characterized Microbe-HCI as a rapidly developing interdisciplinary research field spanning microbiology, HCI, design, computer science, and related disciplines.The study identified seven categories of microorganisms used across interactive system components and six major research themes, highlighting the interdisciplinary and rapidly evolving nature of Microbe-HCI.The findings indicate a shift from viewing microorganisms as passive biological materials toward recognizing them as active participants in interactive activities.Despite growing technological experimentation, gaps remain in ethical review, bio-digital virtual interaction, dynamic user experience evaluation, standardized design practices, and human-microbe interaction in natural environments.
Assistive technologies are increasingly being developed to support the diverse cognitive, social, and sensory needs of neurodivergent individuals to improve their quality of life. Technologies such as virtual reality (VR), mobile applications, and smartwatches facilitate participation in daily activities, education, work, and social interactions. This paper presents a systematic literature review of 38 articles examining assistive technologies designed for neurodivergent individuals, with the aim of identifying technological advances and their impact on independence and quality of life. Following the PRISMA framework, papers published between 2018 and 2024 were retrieved from the IEEE Xplore and ACM Digital Library databases using neurodiversity-oriented terminology. The findings highlight how assistive technologies such as mobile applications, VR headsets, and smartwatches support daily activities including social interaction, physical exercise, routine management, and self-regulation. The review also underscores the importance of participatory and co-design approaches in technology development and identifies a significant research gap regarding the needs of older neurodivergent populations, calling for further investigation in this area. RESEARCH HIGHLIGHTS Mobile applications ($n = 11$), virtual reality ($n = 7$), and smartwatches ($n = 4$) are the dominant assistive technology platforms in neurodivergent-focused research, with communication and self-regulation emerging as the most frequently targeted functional domains.Participatory and co-design methods are the most widely employed development strategies across the reviewed studies, positioning neurodivergent individuals as active design contributors rather than passive research subjects.A critical underrepresentation of older neurodivergent adults exists in the literature: only 2 of 38 reviewed studies included participants aged 50 or older, identifying a significant demographic gap with implications for age-responsive assistive technology design.Persistent methodological limitations, including small and gender-imbalanced samples, short-term lab-based evaluations, and geographic concentration in Western contexts, constrain the generalizability of current findings and call for longitudinal, in-the-wild research with globally diverse populations.
Neurodivergent employees, encompassing those with conditions like attention deficit/hyperactivity disorder, autism spectrum disorder, and giftedness, offer unique cognitive strengths yet face challenges in neurotypical environments. This article presents CoDiT-AI, a framework integrating digital twins (DTs) and artificial intelligence (AI) to create workplaces optimized for neurodivergent individuals. It introduces the concept of a Neuro-Workplace-Twin, a digital representation that simulates neurodivergent sensory experiences and employs an AI-based perceptual model to transform workplace designs, thereby supporting the design of inclusive workspaces. Additionally, AI technologies, particularly large language models like GPT-4, are utilized to bridge communication gaps and to identify tasks. The combination of DTs and AI technologies supports the transformation of workplaces into neurodivergent-friendly environments. Empirical data from workshops, interviews, and surveys highlight key needs and preferences for adaptations in the workplace and can be used as a basis for the DT. RESEARCH HIGHLIGHTS This article presents CoDiT-AI, a framework integrating digital twins and artificial intelligence (AI) to support the development of neuroinclusive workplaces.The proposed Neuro-Workplace-Twin combines a digital representation with an AI-based perceptual model to simulate neurodivergent sensory experiences and optimize workplace environments.In addition, large language models, such as GPT-4, are used to bridge communication gaps and support task identification.Empirical data from workshops, interviews, and surveys provide the foundation for identifying workplace adaptation needs and developing more inclusive work environments.
Despite their increasing adoption, understanding black-box models that underpin AI technologies is not trivial. To address this challenge, explainable AI (XAI) tools have received increased interest, albeit their HCI exploration is limited. We report a systematic review of 142 papers focused on the design, use, or evaluation of XAI tools, and in particular, the types of XAI tools, their exploration, the type of data input and output, the type of explanation, available XAI interfaces, and evaluation of XAI tools. Our outcomes highlight a large number of XAI tools, albeit a small number is commonly used, most often by AI experts as users rather than novice users. Our findings led to four research, design, and methodological implications, including the call for more HCI-driven user interaction with XAI tools, for designing richer portfolios of user-data interaction scenarios for XAI tools, and of novel XAI user interfaces, and for leveraging hybrid methods and measures for XAI tools evaluation.
