"Koyi Sawaal Nahi Hai": Reimagining Maternal Health Chatbots for Collective, Culturally Grounded Care

TL;DR

This paper explores how culturally grounded, collective maternal health chatbots can be designed for low-resource settings, emphasizing family involvement, infrastructural challenges, and social norms to improve adoption and care.

Contribution

It introduces the Relational Chatbot Design Grammar (RCDG), a framework for designing chatbots that support collective decision-making and cultural context in maternal health care.

Findings

Adoption is influenced by family mediation and proxy consent.

Silence around questions reflects cultural norms, not disinterest.

Infrastructural issues and contested authority affect chatbot use.

Abstract

In recent years, LLM-based maternal health chatbots have been widely deployed in low-resource settings, but they often ignore real-world contexts where women may not own phones, have limited literacy, and share decision-making within families. Through the deployment of a WhatsApp-based maternal health chatbot with 48 pregnant women in Lahore, Pakistan, we examine barriers to use in populations where phones are shared, decision-making is collective, and literacy varies. We complement this with focus group discussions with obstetric clinicians. Our findings reveal how adoption is shaped by proxy consent and family mediation, intermittent phone access, silence around asking questions, infrastructural breakdowns, and contested authority. We frame barriers to non-use as culturally conditioned rather than individual choices, and introduce the Relational Chatbot Design Grammar (RCDG): four commitments that enable mediated decision-making, recognize silence as engagement, support episodic use, and treat fragility as baseline to reorient maternal health chatbots toward culturally grounded, collective care.