@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T First Impressions from Comparing Form-Based and Conversational Interfaces for Public Service Access in India
%A R, Chaitra C.
%A Voora, Pranathi
%A Bikkina, Bhaskar Ruthvik
%A Boddapati, Bharghavaram
%A Jain, Vivan
%A Upadhyay, Prajna
%A Chakraborty, Dipanjan
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dev, Sunipa
%Y Li, Siyan
%Y Madaio, Michael
%Y Wang, Jack
%Y Wu, Sherry Tongshuang
%Y Xiao, Ziang
%Y Yang, Diyi
%S Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-353-1
%F r-etal-2025-first
%X 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.
%U https://aclanthology.org/2025.hcinlp-1.6/
%P 60-71
Markdown (Informal)
[First Impressions from Comparing Form-Based and Conversational Interfaces for Public Service Access in India](https://aclanthology.org/2025.hcinlp-1.6/) (R et al., HCINLP 2025)
ACL