@inproceedings{r-etal-2025-first,
    title = "First Impressions from Comparing Form-Based and Conversational Interfaces for Public Service Access in {I}ndia",
    author = "R, Chaitra C  and
      Voora, Pranathi  and
      Bikkina, Bhaskar Ruthvik  and
      Boddapati, Bharghavaram  and
      Jain, Vivan  and
      Upadhyay, Prajna  and
      Chakraborty, Dipanjan",
    editor = "Blodgett, Su Lin  and
      Curry, Amanda Cercas  and
      Dev, Sunipa  and
      Li, Siyan  and
      Madaio, Michael  and
      Wang, Jack  and
      Wu, Sherry Tongshuang  and
      Xiao, Ziang  and
      Yang, Diyi",
    booktitle = "Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.hcinlp-1.6/",
    pages = "60--71",
    ISBN = "979-8-89176-353-1",
    abstract = "Accessing government welfare schemes in India remains difficult for emergent users{---}individuals with limited literacy, digital familiarity, or language support. This paper compares two mobile platforms that deliver the same scheme-related information but differ in interaction modality: myScheme, a government-built, form-based Android application, and Prabodhini, a voice-based conversational prototype powered by generative AI and Retrieval-Augmented Generation (RAG). Through a task-based comparative study with 15 low-income participants, we examine usability, task completion time, and user preference. Drawing on theories such as the Gulf of Execution and Zipf{'}s Law of Least Effort, we show that Prabodhini{'}s conversational design and support for natural language input better align with emergent users' mental models and practices. Our findings highlight the value of multimodal, voice-first NLP systems for improving trust, access, and inclusion in public digital services. We discuss implications for designing accessible language technologies for marginalised populations."
}