Sruthi Viswanathan

Caleb Adu, Neil Kapadia, Binhe Liu et al.

Universities are microcosms of urban ecosystems, with concentrated consumption patterns in food, transport, energy, and product usage. These environments not only contribute substantially to sustainability pressures but also provide a unique opportunity to advance sustainability education and behavioural change at scale. As in most sectors, digital sustainability initiatives within universities remain narrowly focused on carbon calculations, typically providing static feedback that limits opportunities for sustained behavioural change. To address this gap, we propose Eco-Bee, integrating large language models, a translation of the Planetary Boundaries framework (as Eco-Score), and a conversational agent that connects individual choices to environmental limits. Tailored for students at the cusp of lifelong habits, Eco-Bee delivers actionable insights, peer benchmarking, and gamified challenges to sustain engagement and drive measurable progress toward boundary-aligned living. In a pilot tested across multiple campus networks (n=52), 96% of the student participants supported a campus-wide rollout and reported a clearer understanding of how daily behaviours collectively impact the planet's limits. By embedding planetary science, behavioural reinforcement, and AI-driven personalisation into a single platform, Eco-Bee establishes a scalable foundation for climate-conscious universities and future AI-mediated sustainability infrastructures.

Sruthi Viswanathan, Seray Ibrahim, Ravi Shankar et al.

Parenting brings emotional and physical challenges, from balancing work, childcare, and finances to coping with exhaustion and limited personal time. Yet, one in three parents never seek support. AI systems potentially offer stigma-free, accessible, and affordable solutions. Yet, user adoption often fails due to issues with explainability and reliability. To see if these issues could be solved using a co-design approach, we developed and tested NurtureBot, a wellbeing support assistant for new parents. 32 parents co-designed the system through Asynchronous Remote Communities method, identifying the key challenge as achieving a "successful chat." As part of co-design, parents role-played as NurtureBot, rewriting its dialogues to improve user understanding, control, and outcomes. The refined prototype, featuring an Interaction Layer, was evaluated by 32 initial and 46 new parents, showing improved user experience and usability, with final CUQ score of 91.3/100, demonstrating successful interaction patterns. Our process revealed useful interaction design lessons for effective AI parenting support.

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal et al.

Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.

Cécile Boulard, Sruthi Viswanathan, Wanda Fey et al.

Interaction between humans and AI systems raises the question of how people understand AI systems. This has been addressed with explainable AI, the interpretability arising from users' domain expertise, or collaborating with AI in a stable environment. In the absence of these elements, we discuss designing Actionable AI, which allows non-experts to configure black-box agents. In this paper, we experiment with an AI-powered cartpole game and observe 22 pairs of participants to configure it via direct manipulation. Our findings suggest that, in uncertain conditions, non-experts were able to achieve good levels of performance. By influencing the behaviour of the agent, they exhibited an operational understanding of it, which proved sufficient to reach their goals. Based on this, we derive implications for designing Actionable AI systems. In conclusion, we propose Actionable AI as a way to open access to AI-based agents, giving end users the agency to influence such agents towards their own goals.

Shreepriya Shreepriya, Danilo Gallo, Sruthi Viswanathan et al.

Mobile devices are ubiquitous and support us in a myriad of situations. In this paper, we study the support that mobile devices provide for navigation. It presents our findings on the Situational Induced Impairments and Disabilities (SIID) during running. We define the context of runners and the factors affecting the use of mobile devices for navigation during running. We discuss design implications and introduce early concepts to address the uncovered SIID issues. This work contributes to the growing body of research on SIID in using mobile devices.