Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities

Abstract

Our online interactions are constantly mediated through Artificial Intelligence (AI), especially our social interactions. AI-mediated social interaction is the AI-facilitated process of building and maintaining social connections between individuals through information inferred from people's online posts. With its impending application across a number of contexts, the challenges and opportunities of AI-mediated social interaction remain underexplored. This paper seeks to understand the design space of AI-mediated social interaction in the context of online learning, where students frequently face social isolation. We deployed an AI agent named SAMI in three class discussion forums to help online learners build social connections. Using SAMI as a probe, we conducted semi-structured interviews with 26 students to understand their difficulties in remote social interactions and their experiences with SAMI. Through the lenses of social translucence and social-technical gap, we illustrate online learners' difficulties in remote social interactions and how SAMI resolved some of the difficulties. We also identify potential ethical and social challenges of SAMI such as user agency and privacy. Based on our findings, we outline the design space of AI-mediated social interaction. We discuss the design tension between AI performance and ethical design and pinpoint two design opportunities for AI-mediated social interaction in designing towards human-AI collaborative social matching and artificial serendipity.

Insights of Parents and Parents‐To‐Be in Using Chatbots to Improve Their Preconception, Pregnancy, and Postpartum Health: A Mixed Studies Review

“…The full-text screening was conducted for the remaining 73 papers, and 15 articles were selected for inclusion in this review: conference papers (n = 4), unpublished dissertations (n = 1), and peer-reviewed journal articles (n = 10). [14][15][16]30,31,[37][38][39][40][41][42][43][44][45][46] Search outcomes are reported using the PRISMA flow diagram (Figure 1).…”

“…17 Programming with NLP and AI capabilities will facilitate the accuracy and effectiveness of the chatbot in communicating with users. 38,44,46 Local health care workers and intended chatbot users can help improve the chatbots' understanding of the languages and cultural norms of targeted communities. 56…”

“…These studies were conducted in 8 different countries with the majority from Western countries: United Kingdom (n = 1), United States (n = 7), Norway (n = 1), Japan (n = 1), South Korea (n = 1), Kenya (n = 2), India (n = 1), and China (n = 1). Seven studies delivered preconception care to nonpregnant women of childbearing age, 15,30,40,41,[43][44][45] 7 studies delivered perinatal care to pregnant or postnatal women only, 14,16,[37][38][39]42,46 and the remaining study delivered care to men and women who were preparing for pregnancy, were pregnant, or were in the postnatal phase. 31 Only one study recruited male participants.…”

“…Many participants described chatbots as engaging, efficient, creative, and easy to use. 15,30,31,37,41,44 Chatbots were helpful to both individuals and groups, 46 men and women, 31 women of different ages, 30 and individuals across various cultures 31,37,38 and ethnicities. 39,40 Chatbots were also appreciated by participants living in both low-resource 14,38,42 and high-resource areas.…”

Through the Lens of Virtual Students: Challenges and Opportunities

“…In this study, female secondary students (𝑥̅ =2.534, SD=0.548) found virtual learning environment communication more problematic than did the males (𝑥̅ =2.263, SD=0.566). Wang et al (2022) observed that females find it tougher to communicate socially and digitally due to the various digital platforms. Young women were twice as likely to feel depression in an environment with a notable lack of in-person connection and engagement, as did young men (Davis et al, 2022).…”