Maryam Mustafa

Muhammad Saad Ali, Daanish U. Khan, Laiba Intizar Ahmad et al.

We present Kissan-Dost, a multilingual, sensor-grounded conversational system that turns live on-farm measurements and weather into plain-language guidance delivered over WhatsApp text or voice. The system couples commodity soil and climate sensors with retrieval-augmented generation, then enforces grounding, traceability, and proactive alerts through a modular pipeline. In a 90-day, two-site pilot with five participants, we ran three phases (baseline, dashboard only, chatbot only). Dashboard engagement was sporadic and faded, while the chatbot was used nearly daily and informed concrete actions. Controlled tests on 99 sensor-grounded crop queries achieved over 90 percent correctness with subsecond end-to-end latency, alongside high-quality translation outputs. Results show that careful last-mile integration, not novel circuitry, unlocks the latent value of existing Agri-IoT for smallholders.

Amna Shahnawaz, Ayesha Shafique, Ding Wang et al.

Menstrual health education (MHE) in Pakistan is constrained by cultural taboos and inadequate formal curricula, leaving women with few trusted resources to lean on. In response to these challenges, we introduce a WhatsApp-based chatbot powered by a large language model (LLM) and Retrieval Augmented Generation (RAG), co-designed with Pakistani college women. Workshops (N=30) revealed key design requirements -- support for Roman Urdu, use of subsidized platforms, and an expert -- curated knowledge base. We then deployed the chatbot with 13 participants for two weeks (403 messages and interviews). Women used it to challenge cultural taboos, legitimize health concerns often dismissed as normal, and build reproductive health knowledge through iterative questioning. Yet, interactions also exposed tensions: reliance on cultural explanatory models, questions of trust and validation, and gendered persona of the chatbot itself. We contribute empirical insights, a stigma-aware design framework for culturally sensitive conversational AI, and a methodological lens foregrounding expert validation in intimate health domains.

Maryam Mustafa, Imaan Hameed, Amna Shahnawaz et al.

Despite steady global advances, maternal mortality remains alarmingly high in Pakistan (155 deaths per 100,000 live births in 2023); largely as a consequence of fragmented paper records, low literacy, poor access to quality healthcare, and gendered barriers that compromise care continuity. Over three years, we designed, deployed, and iteratively developed Awaaz-e-Sehat, a speech-based artificial intelligence (AI) system that generates electronic medical records (EMRs) and supports decision-making in maternal health. The tool evolved from a clinician-facing AI assistant that automated Urdu speech-to-EMR generation into a patient-centred WhatsApp-based platform, enabling women to generate their own structured clinical notes, receive AI-generated antenatal guidance, and share QR-coded records with providers anywhere in the country. This case study documents that translational journey, i.e., how the ground realities of workload, linguistic nuance, and infrastructural constraints reshaped our design. The result is not merely a new method of record-keeping, but a reimagining of antenatal care and electronic medical records themselves. In settings where clinicians are time-constrained and have little institutional incentive to document, Awaaz-e-Sehat proposes a model of care that centres patients as active participants in generating and owning their health data. By keeping patients informed about their own risk factors and integrating them into the clinical decision-support loop, the system transforms EMRs and CDSS from static institutional artefacts into dynamic tools for self-advocacy and shared accountability in maternal health.