# "Kya family planning after marriage hoti hai?": Integrating Cultural Sensitivity in an LLM Chatbot for Reproductive Health

arXiv: https://arxiv.org/abs/2502.15939 · HTML: https://mlsanity.com/paper/2502.15939/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settings](https://mlsanity.com/paper/2509.24506/) (2025)
- [Beyond the Rubric: Cultural Misalignment in LLM Benchmarks for Sexual and Reproductive Health](https://mlsanity.com/paper/2511.17554/) (2025)
- [Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency](https://mlsanity.com/paper/2511.07277/) (2025)
- [Between Myths and Metaphors: Rethinking LLMs for SRH in Conservative Contexts](https://mlsanity.com/paper/2511.01907/) (2025)
- [HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication](https://mlsanity.com/paper/2609.20684/) (2026)

## Abstract (arXiv)

Access to sexual and reproductive health information remains a challenge in many communities globally, due to cultural taboos and limited availability of healthcare providers. Public health organizations are increasingly turning to Large Language Models (LLMs) to improve access to timely and personalized information. However, recent HCI scholarship indicates that significant challenges remain in incorporating context awareness and mitigating bias in LLMs. In this paper, we study the development of a culturally-appropriate LLM-based chatbot for reproductive health with underserved women in urban India. Through user interactions, focus groups, and interviews with multiple stakeholders, we examine the chatbot's response to sensitive and highly contextual queries on reproductive health. Our findings reveal strengths and limitations of the system in capturing local context, and complexities around what constitutes "culture". Finally, we discuss how local context might be better integrated, and present a framework to inform the design of culturally-sensitive chatbots for community health.