In HCI, frameworks function as a type of theoretical contribution, often supporting ideation, design, and evaluation. Yet, little is known about how they are actually used, what functions they serve, and which scholarly practices that shape them. To address this gap, we conducted a systematic review of 615 papers from a decade of CHI proceedings (2015-2024) that prominently featured the term framework. We classified these papers into six engagement types. We then examined the role, form, and essential components of newly proposed frameworks through a functional typology, analyzing how they are constructed, validated, and articulated for reuse. Our results show that enthusiasm for proposing new frameworks exceeds the willingness to iterate on existing ones. They also highlight the ambiguity in the function of frameworks and the scarcity of systematic validation. Based on these insights, we call for more rigorous, reflective, and cumulative practices in the development and use of frameworks in HCI.

Multiparty conversations are ubiquitous and indispensable in diverse social and collaborative contexts. However, current conversational agents (CAs) face significant challenges in effectively engaging in such interactions, particularly within text-based environments. While earlier limitations were often attributed to the inadequacies of AI models, recent advances in large language models now compel us to revisit both our understanding of multiparty conversation and the way we design CAs. This paper synthesizes findings from two complementary qualitative investigations and proposes a conceptual model for designing CAs that can genuinely participate, rather than merely function as tools or outsiders. The first study, employing retrospective think-aloud sessions (N=30) with users in text-based multiparty settings, uncovers 5 key interactional mechanisms (e.g., Turn-taking Management, Presence Management) that underpin successful human-human multiparty interactions, derived from participants’ articulated perceptions and reasoning. Subsequently, the second study, through semi-structured interviews (N=15), identifies user expectations for CA integration and key traits (e.g., proactivity, social authenticity) that shape an ideal CA persona perceived by users as a genuine participant. Drawing from these human-centric insights, we then derive design considerations, aiming to guide the development of CAs capable of more natural, effective, and socially intelligent participation in multiparty conversation.

The richness of the information in photos can often threaten privacy, thus image editing methods are often employed for privacy protection. Existing image privacy protection techniques, like blurring, often struggle to maintain the balance between robust privacy protection and preserving image usability. To address this, we introduce a generative content replacement (GCR) method in image privacy protection, which seamlessly substitutes privacy-threatening contents with similar and realistic substitutes, using state-of-the-art generative techniques. Compared with four prevalent image protection methods, GCR consistently exhibited low detectability, making the detection of edits remarkably challenging. GCR also performed reasonably well in hindering the identification of specific content and managed to sustain the image’s narrative and visual harmony. This research serves as a pilot study and encourages further innovation on GCR and the development of tools that enable human-in-the-loop image privacy protection using approaches similar to GCR.

Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort. Our model exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947 is achievable only using one shot, and its performance can be further boosted by adaptively learning from more data. This generalizability allowed us to develop a mobile silent speech interface empowered with on-device fine-tuning and visual keyword spotting. A user study demonstrated that with LipLearner, users could define their own commands with high reliability guaranteed by an online incremental learning scheme. Subjective feedback indicated that our system provides essential functionalities for customizable silent speech interactions with high usability and learnability.