Searcharxiv⌕ Search

arXiv subjects

Rina R. Wehbe

Publications and source records attributed to Rina R. Wehbe.

3 recordsLinked to original sources

Designing a digital word-learning intervention with neurodiverse children: Experiences and ideas from children with developmental language disorder

Introduction - Developmental language disorder (DLD) is a neurodevelopmental condition often characterised by word-learning difficulties that can lead to significant social and academic challenges. The disorder shares some features with other neurodevelopmental conditions such as autism spectrum disorder (ASD). Despite affecting 7 percent of children, the condition has received little coverage in participatory design research. This paper addresses this by reporting on the emotional responses and design outputs of children with DLD following participatory sessions to inform a word-learning intervention. Method - Principles from learner-centred, cooperative, and accessible co-design approaches were integrated to tailor activities for four children with DLD. Design sessions were refined through ongoing monitoring of the children's experiences. The data that informed the findings included design artefacts such as children's drawings, structured feedback obtained using sentence-starter prompts, and researcher field notes. Reflexive thematic analysis was applied to the data, complemented by findings from an emotion scale completed before and after sessions. Results - Children responded positively to design activities that incorporated familiar story characters and videogames, and that allowed a direct contribution of design ideas. Activities with higher cognitive demand led to reduced interactions. Key design requirements for the word-learning intervention included multimodal, personalised learning opportunities and the use of progress markers. Aligning digital and real-world learning, such as providing in-person adult support, was also important. Conclusions - Through careful planning and refinement, a bespoke participatory design approach proved meaningful and rewarding for children with DLD, whilst also yielding valuable insights for the development of the intervention.

cs.HC↗

Therapeutic AI and the Hidden Risks of Over-Disclosure: An Embedded AI-Literacy Framework for Mental Health Privacy

Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and personalization, their integration into mental health care brings privacy and safety challenges that have not been well-examined. Unlike traditional clinical interactions, LLM-mediated therapy often lacks a clear structure for what information is collected, how it is processed, and how it is stored or reused. Users without clinical guidance may over-disclose personal information, which is sometimes irrelevant to their presenting concern, due to misplaced trust, lack of awareness of data risks, or the conversational design of the system. This overexposure raises privacy concerns and also increases the potential for LLM bias, misinterpretation, and long-term data misuse. We propose a framework embedding Artificial Intelligence (AI) literacy interventions directly into mental health conversational systems, and outline a study plan to evaluate their impact on disclosure safety, trust, and user experience.

cs.HC↗

User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features

Sustainable software development involves creating software in a manner that meets present goals without undermining our ability to meet future goals. In a software engineering context, sustainability has at least four dimensions: ecological, economic, social, and technical. No interventions for improving social sustainability in software engineering have been tested in rigorous lab-based experiments, and little evidence-based guidance is available. The purpose of this study is to evaluate the effectiveness of two interventions-stakeholder maps and persona models-for improving social sustainability through software feature prioritization. We conducted a randomized controlled factorial experiment with 79 undergraduate computer science students. Participants were randomly assigned to one of four groups and asked to prioritize a backlog of prosocial, neutral, and antisocial user stories for a shopping mall's digital screen display and facial recognition software. Participants received either persona models, a stakeholder map, both, or neither. We compared the differences in prioritization levels assigned to prosocial and antisocial user stories using Cumulative Link Mixed Model regression. Participants who received persona models gave significantly lower priorities to antisocial user stories but no significant difference was evident for prosocial user stories. The effects of the stakeholder map were not significant. The interaction effects were not significant. Providing aspiring software professionals with well-crafted persona models causes them to de-prioritize antisocial software features. The impact of persona modelling on sustainable software development therefore warrants further study with more experience professionals. Moreover, the novel methodological strategy of assessing social sustainability behavior through backlog prioritization appears feasible in lab-based settings.

cs.SE↗