FarmChat: A Conversational Agent to Answer Farmer Queries
Abstract
Farmers constitute 54.6% of the Indian population, but earn only 13.9% of the national GDP. This gross mismatch can be alleviated by improving farmers' access to information and expert advice (e.g., knowing which seeds to sow and how to treat pests can significantly impact yield). In this paper, we report our experience of designing a conversational agent, called FarmChat, to meet the information needs of farmers in rural India. We conducted an evaluative study with 34 farmers near Ranchi in India, focusing on assessing the usability of the system, acceptability of the information provided, and understanding the user population's unique preferences, needs, and challenges in using the technology. We performed a comparative study with two different modalities: audio-only and audio+text. Our results provide a detailed understanding on how literacy level, digital literacy, and other factors impact users' preferences for the interaction modality. We found that a conversational agent has the potential to effectively meet the information needs of farmers at scale. More broadly, our results could inform future work on designing conversational agents for user populations with limited literacy and technology experience.
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Multiple studies have raised concerns about using LLMs for healthcare scenarios due to their inconsistency, potential errors, and bias in clinical work.
“multiple studies have raised concerns about using LLMs for healthcare scenarios, due to their inconsistency, potential errors, and bias in clinical work [17,55,64]”
From Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older Adults (2024) · cites this paper as [55]
Talk2Care is intentionally designed to not provide health advice due to the concern about AI reliability in healthcare.
“Talk2Care is intentionally designed to not provide health advice due to the concern about AI reliability in healthcare [24,55,82].”
From Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older Adults (2024) · cites this paper as [55]
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Self-citationConvey: Exploring the Use of a Context View for Chatbots
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“Additionally, such personalized learning environments not only enhance the quality of instruction, but also promote informal learning and services to underserved and vulnerable populations (Jain et al., 2018) including students in need of special education (Chassignol et al., 2018).”
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Business model canvas to create and capture AI-enabled public value
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- Authors
- Jain, M., Kumar, P., Bhansali, I., Liao, Q. V., Truong, K., & Patel, S.
- Affiliations
- University of Washington (US)
- Indian Institute of Technology Madras (IN)
- IBM (United States) (US)
- University of Toronto (CA)
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2018-12-27
- Volume
- 2
- Issue
- 4
- Pages
- 1-22
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2907049732
- Cited by
- 117
- References
- 4
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 17 · 2023: 14 · 2022: 22 · 2021: 24 · 2020: 12 · 2019: 5
- FWCI
- 16.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Development and Management
- Keywords
- chatbots, farmers, agricultural information access, rural India, digital literacy, literacy levels, usability, acceptability, user preferences, low-literacy users, expert advice
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