E-commerce platforms increasingly deploy explainability features to address concerns about algorithmic opacity. However, most XAI research has focused on younger, tech-savvy users, leaving open questions about how older adults engage with these features in everyday shopping. To address this gap, we conducted a qualitative study with 20 older adults aged 60+ who regularly use NAVER Shopping, one of South Korea’s largest e-commerce platforms, examining their engagement with global (system-level) explanations, local (item-level) explanations, and a user-model dashboard. Our findings reveal that explainability does not operate uniformly. Many participants did not notice the explanation features during routine use or mistook them for advertisements. After guided interaction, global explanations elicited polarized responses: some participants deferred uncritically to algorithmic authority, whereas others dismissed the explanations as sophisticated marketing rhetoric. In contrast, local explanations grounded in users’ behavior helped recalibrate skepticism, while a user-model dashboard exposed tensions between empowerment and surveillance. Based on these findings, we propose actionable design strategies for building inclusive and adaptive XAI systems for older adults.

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants' future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants' engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults' career exploration.

Keyeun Lee, Seo Hyeong Kim, Seolhee Lee, Jinsu Eun, Yena Ko, Hayeon Jeon, Esther Hehsun Kim, Seonghye Cho, Soeun Yang, Eun-mee Kim, Hajin Lim. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The shift to online education, accelerated by the COVID-19 pandemic, has introduced challenges in monitoring student engagement, an essential aspect of effective teaching. In response, real-time student monitoring interfaces have emerged as potential tools to aid instructors, yet their efficacy has not been thoroughly examined. Addressing this gap, we conducted a controlled experiment with 20 instructors examining the impact of engagement cues (presence versus absence) and student engagement levels (high versus low) on instructors’ monitoring effectiveness, teaching behavior adjustments, and cognitive load in online classes. Our findings underscored the fundamental benefits of student engagement monitoring interfaces for improving monitoring quality and effectiveness. Furthermore, our study highlighted the critical need for customizable interfaces that could balance the informational utility of engagement cues with the associated cognitive load and psychological stress on instructors. These insights may offer design implications for the design of future student engagement monitoring interfaces.

Increasingly, older adults are shopping via mobile devices as technology has been incorporated into their lives. When older adults adopt and use mobile shopping, social interactions with close others greatly influence their experience. Therefore, this paper aimed to provide a comprehensive understanding of how social interactions with close others shaped older adults’ mobile shopping practices. We conducted in-depth semi-structured interviews with 31 older adults who reported using mobile shopping regularly. We found that older adults engaged in three types of social interaction: learning from, collaborating with, and assisting close others in adopting and using mobile shopping. Through these social interactions, they gradually built trust in mobile shopping systems and supported each other’s decision-making processes. In conclusion, we presented design implications for facilitating social interactions to improve older adults’ mobile shopping experience.