Sexual and Reproductive Health (SRH) education remains largely limited to awareness-based interventions, particularly in stigmatized socio-cultural contexts. Researchers are extensively focusing on pedagogical theories, reshaping teaching and learning practices to enhance educational outcomes. This paper investigates the application of Bloom’s taxonomy as a design framework for scaffolding SRH learning from foundational knowledge to higher-order cognitive engagement. We conducted a mixed-methods study with 45 participants who engaged in learning activities across different cognitive levels, followed by semi-structured interviews. Our analysis shows that integrating higher-order learning objectives—such as application, analysis, and reflection—supports deeper engagement and understanding of SRH topics beyond surface-level awareness. We also highlight how technologies such as VR and LLMs enable flexible, exploratory learning and create safe, judgment-free spaces for engaging with sensitive topics, supporting users’ comfort. Based on these findings, we derive design implications for developing structured, inclusive, and stigma-sensitive SRH education technologies. This work contributes to HCI by empirically demonstrating how established learning theories can advance learning in sensitive educational domains.

The rapid growth of large language models (LLMs) is reshaping access to health information, yet their role in sexual and reproductive health (SRH)—a highly stigmatized and sensitive domain is a nascent area of research, particularly in the Indian context. This study investigates how individuals perceive and engage with an LLM-based chatbot for SRH, examining trust boundaries, privacy expectations, and the cultural dimensions of LLM-mediated health communication. We designed a Retrieval-Augmented Generation (RAG) chatbot for sexual health and conducted a user study in which fifteen participants interacted with the system, followed by semi-structured interviews. Our findings reveal three interconnected themes: First, LLMs serve as low-stakes knowledge companions that reduce the cognitive and social burden of SRH information-seeking; secondly, privacy needs are relational and situational rather than uniform, shaped by domestic contexts and family structures; and lastly, LLM responses exhibit systematic gender bias in SRH framing—a pattern which carries significant implications for equitable chatbot design. We contribute design recommendations for culturally sensitive, privacy-aware, and gender-equitable LLM systems for SRH communication.

Collaboration and community building are crucial for equitable participation in research, especially in disability and accessibility research fields.Engaging with communities of disabled individuals helps understand their unique challenges, expectations, and needs, which should be reflected in assistive technology design.However, a gap exists between research in resource-rich societies and the practices of low-resource settings, where socio-economic and infrastructural constraints are often overlooked.Conversely, the innovation and sustainable solutions in these settings are often