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Knowledge dissemination in educational settings is profoundly influenced by the curse of knowledge, a cognitive bias that causes experts to underestimate the challenges faced by learners due to their own in-depth understanding of the subject. This bias can hinder effective knowledge transfer and pedagogical effectiveness, and may be exacerbated by inadequate instructor-student communication. To encourage more effective feedback and promote empathy, we introduce TSConnect, a bias-aware, adaptable interactive MOOC (Massive Open Online Course) learning system, informed by a need-finding survey involving 129 students and 6 instructors. TSConnect integrates instructors, students, and Artificial Intelligence (AI) into a cohesive platform, facilitating diverse and targeted communication channels while addressing previously overlooked information needs. A notable feature is its dynamic knowledge graph, which enhances learning support and fosters a more interconnected educational experience. We conducted a between-subjects user study with 30 students comparing TSConnect to a baseline system. Results indicate that TSConnect significantly encourages students to provide more feedback to instructors. Additionally, interviews with 4 instructors reveal insights into how they interpret and respond to this feedback, potentially leading to improvements in teaching strategies and the development of broader pedagogical skills.

Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. To address the lack of meaningful user-facing privacy choices, we conducted two vignette-based survey studies with 486 participants and a follow-up interview study with 16 participants. We examined users' needs and preferences for privacy choice design across both GenAI personalization and data-sharing scenarios. Our findings reveal context-dependent and sometimes paradoxical patterns in trust, perceived control, and privacy concerns, and inform the design of privacy choice interfaces that enhance transparency, control, and trust in GenAI ecosystems.

Collaborative programming is widely used to encourage peer scaffolding, yet real-time collaboration can break down into parallel individual work with limited interaction. We present Canary, a system that supports peer scaffolding by breaking programming obstacles into smaller steps tailored to a student's skill level. Canary alerts potential helpers to specific places where they can begin contributing and uses AI to turn complex problems into a step-by-step ladder that progresses from easier fixes to more difficult logic. Our evaluation shows that this staged approach reduces the effort required to begin helping and supports more frequent and effective collaboration among students.

People with disabilities (PWD) increasingly use avatars to express disability identities in social virtual reality (VR), but greater visibility can also invite targeted harassment. Existing safety features often overlook PWD's experiences and needs. We co-designed protection mechanisms with 11 PWD using a social lens grounded in Hall's Proxemics Theory. We structured social VR spaces into Intimate, Personal, Social, and Public zones and used protection mechanism probes to elicit participant preferences. Our findings highlight how social proximity shapes harassment perceptions and protection preferences, as well as tensions between maintaining safety and preserving social image. We conclude with design recommendations for protection mechanisms that protect PWD while supporting their desired social interactions.

People with disabilities (PWD) are increasingly engaging in social virtual reality (VR) platforms, where immersive and embodied interactions can intensify negative experiences. While prior work has examined harassment in VR, little is known about the harms experienced by PWD and the perceived severity associated with different harassment and disability types. To characterize and model harms for PWD, we conducted a literature review followed by an online survey with 67 PWD to understand participants' harassment experiences and resulting harms. We identified 19 types of harm in 5 categories and reported the perceived severity of each type of harm. We further analyzed these findings through the lens of critical disability theory and discussed design implications for specialized safety mechanisms that mitigate harm for PWD.