First Impressions from Comparing Form-Based and Conversational Interfaces for Public Service Access in I ndia (2025)

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.

## introduction india's central and state governments have long adopted a welfare-oriented approach to governance, offering numerous social protection schemes to support the elderly, low-income, and marginalised populations. these schemes also target workers in the unorganised sector, which comprises approximately 92% of the country's workforce @xcite . while well-intentioned and potentially transformative, the actual uptake and utilisation of these services remains low @xcite . several barriers hinder effective access to welfare schemes. although e-governance platforms have made these services digitally accessible, emergent users, individuals with limited digital experience, often facing low literacy, low income, and poor infrastructure access @xcite struggle to engage meaningfully with them. key obstacles include a lack of awareness about available schemes, difficulties in navigating complex formbased interfaces, and associated costs of access, such as relying on cyber cafés to fill out forms for nominally "free" services @xcite . these barriers reflect broader mismatches between the expectations embedded in digital interfaces and the lived realities of emergent users. norman's concept of the gulf of execution (norman, 1986) offers a useful lens here: systems like myscheme require users to translate their needs into the language and structure of the interface, rather than allowing users to express their goals in familiar terms. furthermore, these systems often violate zipf's law of least effort @xcite , which suggests that users prefer interaction paths that demand the least cognitive and physical effort. by relying heavily on hierarchical forms, structured data fields, and pre-defined filters, current platforms place the burden of adaptation on the user, thus worsening exclusion. this paper explores whether interaction modality-specifically, traditional form-based interfaces versus conversational, voice-based ones-affects the usability and accessibility of mobile information systems for emergent users. we address the research question: how do different mobile interaction modalities-namely, form-based graphical interfaces versus voice-based conversational systems-affect emergent users' ability to seek and access information about government welfare schemes? to investigate this, we conducted a comparative user study with 15 participants drawn from low-income, blue-collar workers employed at a university campus in south india. the two platforms we evaluated deliver identical government welfare information but differ in design philosophy and interaction modality. the first is myscheme, a government-built android application that relies on hierarchical menus and form-filling interfaces. the second is prabodhini (sanskrit for awakening) (figure 1 ), a prototype conversational app developed in our lab. prabodhini uses a backend powered by applied to data sourced from the official myscheme website. it is designed through a user-centred process and includes voice-input capabilities in regional indian languages using offthe-shelf text-to-speech (tts) and speech-to-text (stt) engines. the technical details of prabodhini are available in @xcite . we compared prabodhini with the myscheme application, as it is the only government-released platform of its kind, and the information it provides is considered authoritative and valid. unlike myscheme, which presents users with dense static text and long application forms, prabodhini breaks down information into small, actionable conversational nuggets, enabling users to query the system using natural language-either spoken or typed. this design not only reduces the gulf of execution but also aligns with the mental models and digital practices of mobile-first emergent users. many participants are already accustomed to voice interactions through tools like google search, and conversational systems like prabodhini leverage these affordances to improve accessibility and confidence. our findings show that users preferred prabodhini over the form-based alternative. conversational, voice-first systems improved access, inclusion, and trust for users often excluded from digital services. this study bridges hci and nlp by applying a large language model (gpt-4) to reduce usability barriers for low-literate, mobile-first users in india. prabodhini uses a retrieval-augmented generation (rag) pipeline to deliver scheme information through natural language queries. by combining speech input and scenario-based design, it makes government services more accessible in low-resource settings. this work brings nlp research closer to real-world, socially relevant hci challenges. ## methodology we conducted a comparative user study to examine how interaction modality influences the usability of mobile welfare apps for emergent users. we evaluated two android applications: the governmentdeveloped myscheme, which uses form-based, text-heavy interfaces, and prabodhini, a voice-based conversational app developed in our lab. both apps used the same backend data, allowing a controlled comparison of interaction design. participants engaged in structured tasks on both apps. we measured task completion time, recorded observational notes, and administered a usability questionnaire. a doctoral researcher and two undergraduate students facilitated the sessions, which lasted approximately 30 minutes each. all interactions were conducted in telugu or hindi. a pilot with two participants revealed difficulties with the standard system usability scale (sus) due to low literacy and unfamiliarity with likert scales. we therefore created a simplified binaryresponse questionnaire, inspired by sus constructs but adapted to suit the participant group (see ## findings we conducted both qualitative and quantitative analyses of the data obtained through the study. qualitative data were logged through observations while the participants interacted with the apps and any feedback the participants provided after the tasks. the quantitative data collected is the task completion time for the tasks defined in section 3.4 and response to the usability questionnaire ( ## discussion and conclusion our findings highlight the considerable challenges faced by emergent users when engaging with digital services that rely on form-based interfaces, technical jargon, or limited language options. participants in our study struggled with myscheme's rigid form structure, abstract categories (e.g., "ur the preference for voice input also underscores the growing familiarity of emergent users with conversational modalities. participants likened prabodhini to google's voice search, referencing their existing use of voice-based interaction in apps like youtube or messaging platforms. this familiarity and the sense of control it fostered contributed to the success of prabodhini's mobile interface and demonstrate how leveraging well-understood input methods can enhance usability. our study also revealed that the quality and reliability of voice interaction matter greatly. some participants struggled to discern the output when prabodhini defaulted to an offline tts engine lacking natural indian accents. others were confused when the app failed to clearly indicate whether it was actively listening. these issues highlight the importance of responsive feedback and robust system design-particularly in mobile contexts where connectivity may be intermittent. future iterations must incorporate more effective visual and auditory cues to support multimodal interaction feedback. our findings reinforce the importance of localising interface language and terminology. terms like "city" and "village" resonated more with participants than "urban" or "rural", illustrating how familiar vocabulary can reduce cognitive load. prior work has shown that culturally resonant interfaces enhance user trust and improve task success among underserved groups @xcite . designers of mobile services must move beyond mere translation and towards localisation strategies that reflect users' linguistic, social, and cognitive contexts. designing mobile governance platforms for emergent users demands resilient, mobile-native, voice-first interfaces. scaling such systems requires addressing language diversity, interface robustness, and continuous participatory evaluation. this study contributes to the intersection of hci and nlp by showing how large language models and speech interfaces can support information access in low-resource settings. prabodhini combines gpt-4 with a retrieval-augmented generation (rag) pipeline, speech-to-text input, and text-to-speech output to support natural language queries in regional languages. these components helped reduce the cognitive load of form-based systems and enabled mobile-first, low-literate users to find relevant government scheme information. our findings offer design implications for building inclusive conversational agents that work reliably in multilingual, low-connectivity environments. future nlp systems must prioritise transparency, localisation, and robustness to serve marginalised users more effectively.