About

Aditya Vashistha is an assistant professor in information science at Cornell Bowers, where he advises students in information science and computer science. He leads the Cornell Global AI Initiative – an interdisciplinary, university-wide effort to integrate global perspectives into the design, development, evaluation, and governance of AI technologies. Vashistha’s research aims to design, build, and evaluate globally equitable AI technologies to improve socioeconomic outcomes for historically marginalized communities. His research lies at the intersection of human-computer interaction (HCI), ICT & Development (ICTD), human-centered AI (HAI), and social computing. He is the recipient of numerous awards, including Cornell’s Faculty Award for Excellence in Research, Teaching, and Service through Diversity. Before joining the Cornell faculty, he completed a Ph.D. in computer science and engineering at the University of Washington, where his dissertation was recognized with the William Chan Memorial Dissertation Award and the WAGS/ProQuest Innovation in Technology Award.

Research topics

• Computer Science
• Sociology
• Political Science
• Psychology
• Public relations
• Medicine
• Internet privacy
• Engineering
• World Wide Web
• Social Science
• Gender studies
• Aesthetics
• Psychotherapist
• Knowledge management
• Business
• Data science
• Management
• Advertising
• Environmental health
• Philosophy
• Nursing
• Epistemology

Selected publications

• Sharing the Care: Investigating How Conversational AI Might Facilitate Coordination Among Home Care Workers and Family Caregivers

  2026-04-13 · 1 citations

  articleOpen access

  This paper presents a qualitative study with 17 participants that uses video elicitations to investigate how conversational AI agents driven by large language models might support “shared care,” or coordination of home-based care among family caregivers (FCs) and home care workers (HCWs) who care for the same care recipient (CR). Participants saw conversational AI as a promising tool that might help streamline communication, coordinate shift handovers, bridge language gaps, and support onboarding of new or substitute caregivers. That said, caregivers assumed AI agents would inevitably make mistakes and should thus be designed to signal uncertainty and make it easy to report errors. More broadly, participants discussed how AI agents designed for sensitive home care contexts will need to explicitly preserve the human essence of care, minimize extra data work that might distract from caregiving, and always complement—not replace—human judgment.

  PublisherDOI

• Shiksha Copilot: Teacher-AI Collaboration for Curating and Customizing Lesson Plans in Low-Resource Schools CSCW038

  Proceedings of the ACM on Human-Computer Interaction · 2026-04-30

  articleOpen accessSenior author

  This study investigates Shiksha Copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI; teachers then further customized these curated plans for their classrooms using their own expertise alongside AI support. Drawing on a large-scale mixed-methods study involving 1,043 teachers and 23 curators, we examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices within multilingual, low-resource environments. Our findings show that teachers used Shiksha Copilot both to meet administrative documentation needs and to support their teaching. The tool eased bureaucratic workload, reduced lesson planning time, and lowered teaching-related stress, while promoting a shift toward activity-based pedagogy. However, systemic challenges such as staffing shortages and administrative demands constrained broader pedagogical change. We frame these findings through the lenses of teacher-AI collaboration and communities of practice to examine the effective integration of AI tools in teaching. Finally, we propose design directions for future teacher-centered EdTech, particularly in multilingual and Global South contexts.

  PublisherDOI

• Computational hermeneutics: evaluating generative AI as a cultural technology

  Frontiers in Artificial Intelligence · 2026-02-26 · 2 citations

  articleOpen access

  Generative AI (GenAI) systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the humanities, we argue that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness (meaning only emerges in context), plurality (multiple valid interpretations coexist), and ambiguity (interpretations naturally conflict). We present computational hermeneutics as an emerging framework offering an interpretive account of what GenAI systems do, and how they might do it better. We offer three principles for hermeneutic evaluation-that benchmarks should be iterative, not one-off; include people, not just machines; and measure cultural context, not just model output. This perspective offers a nascent paradigm for designing and evaluating contemporary AI systems: shifting from standardized questions about accuracy to contextual ones about meaning.

  PublisherOA PDFDOI

• LLMs Homogenize Values in Constructive Arguments on Value-Laden Topics

  2026-04-13 · 1 citations

  preprintOpen accessSenior author

  Large language models (LLMs) are increasingly used to promote prosocial and constructive discourse online. Yet little is known about how these models negotiate and shape underlying values when reframing people’s arguments on value-laden topics. We conducted experiments with 465 participants from India and the United States, who wrote comments on homophobic and Islamophobic threads, and reviewed human-written and LLM-rewritten constructive versions of these comments. Our analysis shows that LLM systematically diminishes Conservative values while elevating prosocial values such as Benevolence and Universalism. When these comments were read by others, participants opposing same-sex marriage or Islam found human-written comments more aligned with their values, whereas those supportive of these communities found LLM-rewritten versions more aligned with their values. These findings suggest that value homogenization in LLM-mediated prosocial discourse runs the risk of marginalizing conservative viewpoints on value-laden topics and may inadvertently shape the dynamics of online discourse.

  PublisherDOI

• Creating Group Rules with AI: Human-AI Collaboration in WhatsApp Moderation

  ArXiv.org · 2026-05-12

  articleOpen access

  WhatsApp is one of the most widely used messaging platforms globally, with billions of users sharing information in private groups. Yet, it offers little infrastructure to support moderation and group governance. In the absence of platform-level oversight, group admins bear the responsibility of governing group behavior. In this paper, we explore how WhatsApp group admins collaborate with AI tools to create, enforce, and maintain group rules. Drawing on a two-phase speculative design study with 20 admins in India, we examine how participants interacted with an AI assistant (Meta AI) to co-create rules and responded to a series of probes illustrating AI-assisted moderation features. Our findings show that while admins appreciated the AI's ability to surface overlooked rules and reduce their moderation burden, they were highly sensitive to issues of relational trust, data privacy, tone, and social context. We identify how group type and admin style shaped their willingness to delegate authority, and surface the limitations of current chatbot interfaces in supporting collaborative rule-making. We conclude with design implications for building moderation tools that center human judgment, relational nuance, contextual adaptability, and collective governance.

  PublisherOA PDF

• Creating Group Rules with AI: Human-AI Collaboration in WhatsApp Moderation

  arXiv (Cornell University) · 2026-05-12

  preprintOpen access

  WhatsApp is one of the most widely used messaging platforms globally, with billions of users sharing information in private groups. Yet, it offers little infrastructure to support moderation and group governance. In the absence of platform-level oversight, group admins bear the responsibility of governing group behavior. In this paper, we explore how WhatsApp group admins collaborate with AI tools to create, enforce, and maintain group rules. Drawing on a two-phase speculative design study with 20 admins in India, we examine how participants interacted with an AI assistant (Meta AI) to co-create rules and responded to a series of probes illustrating AI-assisted moderation features. Our findings show that while admins appreciated the AI's ability to surface overlooked rules and reduce their moderation burden, they were highly sensitive to issues of relational trust, data privacy, tone, and social context. We identify how group type and admin style shaped their willingness to delegate authority, and surface the limitations of current chatbot interfaces in supporting collaborative rule-making. We conclude with design implications for building moderation tools that center human judgment, relational nuance, contextual adaptability, and collective governance.

  PublisherDOI

• TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories

  2026-04-13 · 1 citations

  preprintOpen access

  Millions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how they represent diverse cultures. However, evaluating cultural representations in open-ended tasks remains challenging and underexplored. In this work, we present TALES, an evaluation of cultural misrepresentations in LLM-generated stories for diverse Indian cultural identities. First, we develop TALES-Tax, a taxonomy of cultural misrepresentations by collating insights from participants with lived experiences in India through focus groups (N=9) and individual surveys (N=15). Using TALES-Tax, we evaluate 6 models through a large-scale annotation study spanning 2,925 annotations from 108 annotators with lived experience and native language proficiency from across 71 regions in India and 14 languages. Concerningly, we find that 88% of the generated stories contain misrepresentations, and such errors are more prevalent in mid- and low-resourced languages and stories based in peri-urban regions in India. We also transform the annotations into TALES-QA, a standalone question bank to evaluate the cultural knowledge of models.

  PublisherDOI

• Designing Culturally Aligned AI Systems For Social Good in Non-Western Contexts

  2026-04-13

  articleOpen accessSenior author

  AI technologies are increasingly deployed in high-stakes domains such as education, healthcare, law, and agriculture to address complex challenges in non-Western contexts. This paper examines eight real-world deployments spanning seven countries and 18 languages, combining 17 interviews with AI developers and domain experts with secondary research. Our findings identify six cross-cutting factors — Language, Institution, Safety, Task, End-User Demography, and Domain — that structured how systems were designed and deployed. These factors were shaped by Sociocultural (diversity, practices), Institutional (resources, policies), and Technological (capabilities, limits) influences. We find that building effective AI systems required extensive collaboration between AI developers and domain experts, with human resources proving more critical to achieving safe and effective outcomes in high-stakes domains than technological expertise alone. Additionally, we present 12 guidelines synthesizing these dynamics for designing AI for social good systems that are culturally grounded, equitable, and responsive to the needs of non-Western contexts.

  PublisherDOI

• Feasibility, Acceptability, and Perspectives Regarding the Use of Activity Tracking Wearable Devices Among Home Health Aides: Mixed Methods Study

  Journal of Medical Internet Research · 2026-01-26

  articleOpen access

  BACKGROUND: Home health aides and attendants (HHAs) provide in-home care to the growing population of older adults who want to age in place. Despite their vital role in patient care, HHAs are an underserved and vulnerable population of health care professionals who often experience poor health themselves. Activity tracking devices offer a promising way to improve HHAs' health-related awareness and promote health behavior change, particularly regarding physical activity and sleep quality, 2 areas in which the workforce struggles. OBJECTIVE: This study aimed to understand how feasible it is for HHAs to use activity tracking devices and assess their perceptions of such devices for improving their health. Specifically, we conducted (1) a field study to assess the use, feasibility, and acceptability of these devices among HHAs and (2) a qualitative study to understand HHAs' perspectives on and reactions to activity trackers on and off the job. METHODS: We partnered with the 1199 Service Employees International Union Training and Employment Fund to conduct a field study with home care agency-employed HHAs working in New York City, New York. Participants wore activity tracking devices for 4 weeks that collected data on physical activity and sleep. The HHAs were subsequently interviewed on their experiences with and attitudes toward the devices and asked to reflect on personalized visualizations of their data to prompt them to think aloud. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using grounded theory. RESULTS: A total of 17 HHAs participated; their mean age was 48.7 (SD 12.2) years, 15 (88%) were women, 11 (65%) identified as Black, 5 (29%) identified as Hispanic or Latinx, and they had worked as HHAs for a mean of 11.7 (SD 7.5) years. In total, 94% (n=16) of the HHAs wore their activity trackers for the full 28-day study period. Participants took a mean of 10,230 (SD 3586) daily steps during the study period and slept for a mean of 6.27 (SD 0.58) hours per night. Overall, 4 key themes emerged: (1) activity tracking devices enhanced participants' health awareness by providing empirical data for self-reflection; (2) this increased awareness led to positive behavior changes, including setting and achieving health-related goals; (3) HHAs believed that these devices could improve not only their own health but also that of their patients through positive behavior changes; and (4) despite this optimism, participants emphasized that their ability to modify sleep and activity patterns was constrained by social and occupational determinants, with sleep improvements being particularly challenging. CONCLUSIONS: Our findings suggest that appropriately designed personal tracking interventions could offer a promising approach to supporting positive health-related changes in this historically overlooked workforce, potentially improving their well-being and the quality of care they provide to their patients.

  PublisherDOI

• Fluent but Foreign: Even Regional LLMs Lack Cultural Alignment

  ArXiv.org · 2025-05-25 · 1 citations

  preprintOpen accessSenior author

  Large language models (LLMs) are used worldwide, yet exhibit Western cultural tendencies. Many countries are now building ``regional'' or ``sovereign'' LLMs, but it remains unclear whether they reflect local values and practices or merely speak local languages. Using India as a case study, we evaluate six Indic and six global LLMs on two dimensions -- values and practices -- grounded in nationally representative surveys and community-sourced QA datasets. Across tasks, Indic models do not align better with Indian norms than global models; in fact, a U.S. respondent is a closer proxy for Indian values than any Indic model. We further run a user study with 115 Indian users and find that writing suggestions from both global and Indic LLMs introduce Westernized or exoticized writing. Prompting and regional fine-tuning fail to recover alignment and can even degrade existing knowledge. We attribute this to scarce culturally grounded data, especially for pretraining. We position cultural evaluation as a first-class requirement alongside multilingual benchmarks and offer a reusable, community-grounded methodology. We call for native, community-authored corpora and thickxwide evaluations to build truly sovereign LLMs.

  PublisherOA PDFDOI

Frequent coauthors

• Nicola Dell

  Cornell University

  19 shared

• Richard Anderson

  University of Washington

  14 shared

• William Thies

  10 shared

• Edward Cutrell

  Microsoft (United States)

  10 shared

• Farhana Shahid

  8 shared

• Kalika Bali

  6 shared

• Rama Adithya Varanasi

  New York University

  6 shared

• Madeline R. Sterling

  Cornell University

  6 shared

Labs

• Graduate Student GroupsPI

Awards & honors

• Cornell’s Faculty Award for Excellence in Research, Teaching…
• William Chan Memorial Dissertation Award
• WAGS/ProQuest Innovation in Technology Award