arXiv subjects

Sahiti Dharmavaram

Publications and source records attributed to Sahiti Dharmavaram.

4 recordsLinked to original sources

arXiv2026

MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System

Mindfulness meditation is a widely accessible and evidence-based method for supporting mental health. Despite the proliferation of mindfulness meditation apps, sustaining user engagement remains a persistent challenge. Personalizing the meditation experience is a promising strategy to improve engagement, but it often requires costly and unscalable manual effort. We present MindfulAgents, a multi-agent system powered by large language models that (1) generates guided meditation scripts based on an expert-established mindfulness framework, (2) encourages users' reflection on emotional states and mindfulness skills, and (3) enables real-time personalization of the mindfulness meditation experience for each user. In a formative lab study (N=13), MindfulAgents significantly improved in-session engagement (p = 0.011) and self-awareness (p = 0.014), and reduced momentary stress (p = 0.020). Furthermore, a four-week deployment study (N=62) demonstrated a notable increase in long-term engagement (p = 0.002) and level of mindfulness (p = 0.023). Participants reported that MindfulAgents offered more relevant meditation sessions personalized to individual needs in various contexts, supporting sustained practice. Our findings highlight the potential of LLM-driven personalization for enhancing user engagement in digital mindfulness meditation interventions.

cs.HC↗

arXiv2026

More than Decision Support: Exploring Patients' Longitudinal Usage of Large Language Models in Real-World Healthcare-Seeking Journeys

Large language models (LLMs) have been increasingly adopted to support patients' healthcare-seeking in recent years. While prior patient-centered studies have examined the capabilities and experience of LLM-based tools in specific health-related tasks such as information-seeking, diagnosis, or decision-supporting, the inherently longitudinal nature of healthcare in real-world practice has been underexplored. This paper presents a four-week diary study with 25 patients to examine LLMs' roles across healthcare-seeking trajectories. Our analysis reveals that patients integrate LLMs not just as simple decision-support tools, but as dynamic companions that scaffold their journey across behavioral, informational, emotional, and cognitive levels. Meanwhile, patients actively assign diverse socio-technical meanings to LLMs, altering the traditional dynamics of agency, trust, and power in patient-provider relationships. Drawing from these findings, we conceptualize future LLMs as a longitudinal boundary companion that continuously mediates between patients and clinicians throughout longitudinal healthcare-seeking trajectories.

cs.HC↗

arXiv2024

From Cash to Cashless: UPI's Impact on Spending Behavior Among Indian Users and Prototyping Financially Responsible Interfaces

Unified Payments Interface (UPI) is a groundbreaking innovation making waves in digital payment systems in India. It has revolutionised financial transactions by offering enhanced convenience and security. While previous research has primarily focused on the macroeconomic effects of digital payments, our study examines UPI's impact on individual spending behavior. Through a survey of 276 respondents and 20 follow-up interviews, we found that approximately 75% of participants reported increased spending due to UPI. Many attributed this to UPI's intangible nature, which reduced feelings of guilt typically associated with spending. Additionally, participants provided suggestions to improve the user experience of existing UPI applications. Utilizing this feedback, we developed a high-fidelity prototype based on a popular UPI app in India and conducted usability testing with 34 participants. The insights gathered from this testing shaped the final prototype and its features. This study offers valuable design recommendations for UPI app developers and other stakeholders.

cs.CY↗

arXiv2024

"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students

This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students. Although a number of research studies in the computing education community have explored the possibility of using LLMs for a variety of tasks, there is a lack of comprehensive research comparing different LLMs and evaluating which LLMs are most effective for different tasks. Our research systematically assesses some of the publicly available LLMs such as Google Bard, ChatGPT(3.5), GitHub Copilot Chat, and Microsoft Copilot across diverse tasks commonly encountered by undergraduate computer science students in India. These tasks include code explanation and documentation, solving class assignments, technical interview preparation, learning new concepts and frameworks, and email writing. Evaluation for these tasks was carried out by pre-final year and final year undergraduate computer science students and provides insights into the models' strengths and limitations. This study aims to guide students as well as instructors in selecting suitable LLMs for any specific task and offers valuable insights on how LLMs can be used constructively by students and instructors.

cs.CY↗