Researchers created a stigma-aware WhatsApp chatbot for menstrual health education in Pakistan through co-design workshops and a two-week deployment, yielding insights on its use for challenging taboos alongside tensions around trust and cultural explanations.
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4 Pith papers cite this work, alongside 342 external citations. Polarity classification is still indexing.
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Pith papers citing it
342
external citations · OpenAlex
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2026 4verdicts
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The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.
A 2x2 between-subjects experiment finds contextualization lowers AI persuasiveness but warmth restores it through crossover interaction, with reliance invariant to design, trust predicting outcomes independently, and AI literacy decoupling trust from behavior.
Proposes a low-code/no-code pipeline for modeling and generating personalized conversational agents, implemented on an open-source platform with a pilot usability study.
citing papers explorer
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Designing Around Stigma: Human-Centered LLMs for Menstrual Health
Researchers created a stigma-aware WhatsApp chatbot for menstrual health education in Pakistan through co-design workshops and a two-week deployment, yielding insights on its use for challenging taboos alongside tensions around trust and cultural explanations.
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Position: AI as Part of Self -- Extending the Mind Requires Cognitive Co-Regulation
The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.
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Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
A 2x2 between-subjects experiment finds contextualization lowers AI persuasiveness but warmth restores it through crossover interaction, with reliance invariant to design, trust predicting outcomes independently, and AI literacy decoupling trust from behavior.
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A Low-Code Approach for the Automatic Personalization of Conversational Agents
Proposes a low-code/no-code pipeline for modeling and generating personalized conversational agents, implemented on an open-source platform with a pilot usability study.