Bangladesh’s expansion of online public services has improved access on paper, yet literacy barriers, fragile infrastructure, and inconsistent design continue to exclude many citizens. Through a convergent mixed-methods study, 293 survey responses, 28 administrator and mediator interviews, and heuristic evaluation, we show how digital literacy strongly predicts task success and autonomy, while qualitative accounts reveal how irreversibility, payment failures, and mistrust drive users toward informal mediators. These dynamics produce a Digital Democracy Paradox: systems justified as efficient and publicly framed as accessible can, in practice, deepen exclusion. We contribute: (1) the Literacy-Adaptive Usability Framework (LAUF), which treats literacy as a dynamic design parameter and integrates adaptation, mediation, and infrastructural resilience; (2) Civix UI, a lightweight illustrative prototype demonstrating bilingual, low-bandwidth, literacy-sensitive components; and (3) a mediator-inclusive design model that formalizes assisted use through accountable, gender-inclusive support. Together, these offer a pathway toward more equitable and trustworthy digital public services.
This systematic review examines harassment in virtual reality (VR), synthesizing findings from 85 studies published between 2017 and 2025. We propose a nuanced typology of harassment, encompassing spatial intrusion, sexual and verbal abuse, identity-based discrimination, group-targeted harassment, and systemic harms, and demonstrate how VR’s immersive and embodied features amplify risk and impact. Marginalized users, such as women, LGBTQ+ individuals, children, and people with disabilities, face disproportionate harm. We further analyze the psychological and behavioral consequences of harassment, as well as the effectiveness and limitations of current governance, design, and AI-driven interventions. Our review identifies persistent research gaps in theory, measurement, and inclusive protection, and advocates for ethical, participatory, and preventive approaches to platform safety. This work aims to guide researchers and designers in building more equitable and safe VR environments.
Recent work has shown that front-end code generated by Large Language Models (LLMs) can embed deceptive designs. To assess the magnitude of this problem, identify the factors that influence deceptive design production, and test strategies for reducing deceptive designs, we carried out two studies which generated and analyzed 1,296 LLM-generated web components, along with a design rationale for each. The first study tested four LLMs for 15 common ecommerce components. Overall 55.8% of components contained at least one deceptive design, and 30.6% contained two or more. Occurence varied significantly across models, with DeepSeek-V3 producing the fewest. Interface interference emerged as the dominant strategy, using color psychology to influence actions and hiding essential information. The first study found that prompts emphasizing business interests (e.g., increasing sales) significantly increased deceptive designs, so a second study tested a variety of prompting strategies to reduce their frequency, finding a values-centered approach the most effective. Our findings highlight risks in using LLMs for coding and offer recommendations for LLM developers and providers.
As Large Language Models (LLMs) become increasingly integral to daily life, users are engaging with multiple LLM chatbots for various needs; however, prior research on LLM risks often remains lab-based or focuses on single LLMs like ChatGPT or singular risks like privacy. To gain a multi-risk, cross-chatbot understanding of user experiences, we analyze Reddit discussions around seven major LLM chatbots using the NIST AI Risk Management Framework. We find that user-reported risks are unevenly distributed and chatbot-specific: ChatGPT is associated with safety and fairness concerns, Gemini with privacy, and Claude with security and resilience. Less frequent risks, such as explainability and privacy, appear as user trade-offs, whereas prevalent risks like fairness are experienced as direct harms. Our findings underscore the need to operationalize chatbot-specific risk mitigation, moving beyond system-centered risk mitigation to human-centered interventions that align with users' lived experiences.
TikTok exhibits attention-capture damaging patterns (ACDPs) that research suggests may tap into users’ psychological vulnerabilities, prolonging engagement and contributing to users’ lack of agency over their usage. Although various approaches to mitigate the effects of such patterns have been proposed, empirical evidence of their impact remains scarce. We conducted a quantitative study of targeted interventions on ACDPs in TikTok, combining behavioral, eye-tracking, user experience, and agency measures (N=48). We added friction to three existing ACDPs: autoplay, infinite scroll, and social investments. Our findings revealed complex trade-offs: while disabling autoplay and increasing scroll friction reduced compulsive engagement patterns, these fundamental disruptions to the established interface architecture decreased user experience with limited or negative impact on agency. Obstructing social investments had negligible effects. Our work validates the argument on negative impacts of ACDPs and demonstrates how to measure them holistically. Future studies can build on these findings to advance understanding of damaging interfaces.
Large language models often produce biased or stereotypical outputs. One way to reduce this possibility is to be more inclusive in our prompts, but doing so may not come naturally to most users. Therefore, we designed a tool that coaches users to write more inclusive prompts—a strategy that leverages design friction to provide a media literacy intervention. Data from a user study (N=344) show that compared to no coaching, inclusive prompt coaching directly increased users’ awareness of algorithmic bias and their perceived prompting efficacy. It also indirectly enhanced their trust in the system and perceived trust calibration through cognitive elaboration. However, inclusive prompt coaching resulted in a less satisfying user experience. These findings have implications for ethical interventions in prompting for better communicating and combating algorithmic bias. We discuss the benefits and limitations of inclusive prompt coaching, as well as ways to balance usability for long-term adoption of generative AI systems.
Older adults with multimorbidity remain under-served by digital health self-management interventions, despite being among the highest users of healthcare. Their engagement with such technologies is poorly understood, particularly in real-world, longitudinal contexts. This paper presents a mixed-methods analysis of engagement with the ProACT self-management platform during a six-month trial involving older adults with multimorbidity. Drawing on quantitative usage data and qualitative interviews, we examine patterns of engagement with symptom and wellbeing monitoring and management, and explore the influence of age, gender, and triage nurse support. Findings reveal that engagement is non-linear, highly individualised, and shaped by clinical support, motivation, usability, and life context. Participants developed personalised routines, adjusting use based on symptom variability and life disruptions. Triage nurse support played a key role in sustaining engagement, offering reassurance, guidance and motivation. We offer implications for designing digital health technologies that support episodic, meaningful, and contextually adaptive use in later life.