arXiv subjects

Debaleena Chattopadhyay

Publications and source records attributed to Debaleena Chattopadhyay.

4 recordsLinked to original sources

arXiv2026

Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support

Older adults often struggle to articulate technology support needs due to unfamiliar technical terminology and age-related cognitive changes. We explore how large language models (LLMs) can facilitate this problem articulation process. Through a diary study (n = 27), we identified four communication barriers in older adults' queries: verbosity, incompleteness, over-specification, and under-specification. To mitigate these barriers, we developed an LLM pipeline that clarifies context and paraphrases unstructured queries. LLM-rephrased queries significantly improved automated solution accuracy (69% vs. 35%). Furthermore, younger adults (n = 48) acting as technology helpers understood LLM-rephrased queries better (93.7% vs. 65.8%) and reported greater ease in providing support. Older adults (n = 34) also found the resulting solutions highly actionable (94.7%). Finally, we contribute the first synthetic dataset of older adults' technology assistance queries (STAQ). This work demonstrates how LLMs can improve technology support seeking for older adults by addressing age-related communication barriers.

cs.HC↗

arXiv2026

Technology Caregiving: Reframing How Older Adults Are Supported in Everyday Digital Activities

Transformed by digitization, everyday activities-paying bills, shopping, managing transportation-increasingly require older adults to navigate digital systems. To accomplish these digital activities of daily living (DADLs), older adults often rely on help that looks less like IT support-institutional, episodic, and product-oriented-and more like caregiving: relational, ongoing, and aimed at preserving their functional independence. We argue that this practice is technology caregiving and introduce a framework characterizing it along four dimensions: why support is needed, who provides it, when it occurs, and how it is delivered. Applying this framework, we then systematically review the literature on how older adults are supported in DADLs. From 3,381 unique records, 36 articles met the inclusion criteria. Findings show that technology caregiving involves burden, like traditional care, but is distinctly shaped as much by digital systems and their constant change as by technology caregivers' and older adults' abilities.

cs.HC↗

arXiv2024

Design Requirements for Human-Centered Graph Neural Network Explanations

Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease trust in them as well as deter any collaboration opportunities between the AI expert and non-technical, domain expert. Here, we first discuss the two papers that aim to provide GNN explanations to domain experts in an accessible manner and then establish a set of design requirements for human-centered GNN explanations. Finally, we offer two example prototypes to demonstrate some of those proposed requirements.

cs.LG↗

arXiv2024

A Value-Oriented Investigation of Photoshop's Generative Fill

The creative industry is both concerned and enthusiastic about how generative AI will reshape creativity. How might these tools interact with the workflow values of creative artists? In this paper, we adopt a value-sensitive design framework to examine how generative AI, particularly Photoshop's Generative Fill (GF), helps or hinders creative professionals' values. We obtained 566 unique posts about GF from online forums for creative professionals who use Photoshop in their current work practices. We conducted reflexive thematic analysis focusing on usefulness, ease of use, and user values. Users found GF useful in doing touch-ups, expanding images, and generating composite images. GF helped users' values of productivity by making work efficient but created a value tension around creativity: it helped reduce barriers to creativity but hindered distinguishing 'human' from algorithmic art. Furthermore, GF hindered lived experiences shaping creativity and hindered the honed prideful skills of creative work.

cs.HC↗