Artificial Intelligence (AI) is increasingly integrated into Building Automation Systems (BAS) to enhance energy efficiency and occupant comfort. Yet, rather than functioning as neutral optimization tools, AI in BAS operates within fragile infrastructures, limited resources, and institutional politics. We present a qualitative study of 23 interviews with energy professionals, AI researchers, and student representatives at the University of Toronto, an institution recognized for its sustainability leadership. Participants expressed ambivalence: AI was valued for forecasting and optimization, yet concerns arose around legitimacy, labor demands, and environmental paradoxes. Fairness in occupant comfort was highlighted, not as an inherent property of models but as a situated practice shaped by infrastructural governance negotiated across roles and inequities. Communication also emerged as a form of occupant agency, where human, machine, and AI-mediated dialogue makes automated decisions legible and contestable. These findings reframe AI in BAS as socio-technical infrastructure and inform our design recommendations for transparent, participatory, and just systems.

As a flexible, scalable, and affordable learning paradigm, live-streaming-based learning (LS learning) has become increasingly popular among older adults, shaping a digitally mediated ''silver classroom''. Despite its growing prevalence and its potential to address gaps in lifelong learning access and educational equity, meet older adults' learning needs, enhance their social connection, this learning paradigm remains largely underexplored in the CSCW literature. Given older adults' unique learning characteristics in terms of motivations, expectations, and learning abilities, it is critical to deeply examine their LS learning behaviors and experiences to inform better design. This study presents an empirical study in China to unpack this LS-based silver classroom phenomenon, focusing on its infrastructure, practices and lived experiences. Our findings reveal a human-technology integrated infrastructure alongside a volunteer-based, self-organized, autonomous collaborative community, which works together in fostering a supportive LS learning environment for older adults and meeting their additional emotional and social needs. We discuss how these sociotechnical arrangements shape the unique learning experiences of older adults and highlight the opportunities and challenges in designing for later-life learning.

Self-directed learning is becoming a significant skill for learners. However, learners may suffer from difficulties such as distractions, a lack of motivation, and so on. While self-tracking technologies have the potential to address these challenges, existing tools and systems mainly focused on tracking computer-based learning data in classroom contexts. Little is known about how students track and make sense of their learning data from non-classroom learning activities and which types of learning data are personally meaningful for learners. In this paper, we conducted a qualitative study with 24 users of Timing, a mobile learning tracking application in China. Our findings indicated that users tracked a variety of qualitative learning data (e.g., videos, photos of learning materials, and emotions) and made sense of this data using different strategies such as observing behavioral and contextual details in videos. We then provided implications for designing non-classroom and non-computer-based personal learning tracking tools.

Blind or low vision (BLV) people were recently reported to be live streamers on the online platforms that employed content curation algorithms. Recent research uncovered perceived algorithmic biases suppressing the content created by marginalized populations (e.g., people of color, the LGBT+ community, and content creators of lower socioeconomic status). However, little is known about how BLV streamers, as a marginalized population, perceive the effects of the algorithms adopted by live streaming platforms. We interviewed BLV streamers (N=19) of Douyin — a popular live stream platform in China — to understand their perceptions of algorithms, perceived challenges, and mitigation strategies. Our findings show the perceived factors contributing to disadvantages under algorithmic evaluation of BLV streamers’ content (e.g., issues with filming and timely interaction with viewers) and perceived algorithmic suppression (e.g., content not amplified to sighted users but suppressed within the BLV community). Their mitigation strategies (e.g., not watching other BLV streamers’ shows) tended to be passive. We discuss design considerations to design a more inclusive and fair live streaming platform.