Bystander intervention, or upstanding, is an effective antidote to cyberbullying, but entails many challenges (e.g. self-efficacy, not knowing what to do or how to upstand). Through two studies, this paper investigates collaborative upstanding, examining how a conversational partner (human or AI) can guide bystanders through these challenges in-situ. In a paired role-play study (n=24), we found that bystanders faced significant challenges in how to intervene. Even after deciding to act, how-to challenges often reignited doubts about their self-efficacy and responsibility. Using these insights, we designed ConCUR, a chatbot that (1) encourages bystanders to co-author an upstanding message, leading them to confront how-to challenges sooner, and (2) addresses how-to challenges simultaneously with other challenges that are introduced through a flexible process. Our second study (n=20) suggests such a chatbot is effective in promoting upstanding behavior in the lab setting. We discuss the implications of in-situ collaborative upstanding to upstanding education research, framing upstanding as an iterative and flexible process rather than sequential.

Emerging adults (EAs) often struggle with their identity, making them vulnerable to mental health issues.This paper examines how EAs explore their identity through life-story writing, with an eye on how AI might help.Our interview study found that mandatory writing assignments, such as a Statement of Purpose for college applications, often triggered EAs' identity exploration.These writing/identity exploration processes were collaborative between EAs and those closest to them.Collaborations succeeded when both parties had the skills and confidence to discuss EAs' identities with enough intensity and directness, but not so much that it crossed the boundaries of their relationship.When collaborations failed, EAs resorted to consulting AI.These findings offer an alternative perspective to the traditional design of cognitive AI writing assistants, which assumes writers write about their lives proactively and privately.This work suggests that AI assistants might be more effective if they help (1) initiate EAs' identity reflection while moderating its intensity, and (2) serve as a connective tissue among EAs and their support networks.

Previous studies of face-to-face (f2f) communication have suggested that speakers rely heavily on a variety of multi-modal cues to make real-time predictions about upcoming words in rapid turn-taking. To understand how computer-mediated communication (CMC) differs from f2f communication in terms of the prediction mechanism, this study assessed how the loss of multi-modal cues would affect word predictability in turn-taking. Participants watched videos, listened to audio, or read a transcript of f2f conversations. Across these three conditions, they predicted the same set of omitted words with different levels of predictability and semantic relatedness to other words in the context. Results showed that words of higher predictability were more accurately predicted regardless of CMC types. Higher response accuracy but longer response time were observed in conditions with richer cues, and for participants with more positive and less negative self-emotions. Meanwhile, semantic relatedness did not affect predictability. These results confirmed the key role of prediction in language processing and conversation smoothness, especially its importance in CMC.

Cyberbullying harms teenagers’ mental health, and teaching them upstanding intervention is crucial. Wizard-of-Oz studies show chatbots can scale up personalized and interactive cyberbullying education, but implementing such chatbots is a challenging and delicate task. We created a no-code chatbot design tool for K-12 teachers. Using large language models and prompt chaining, our tool allows teachers to prototype bespoke dialogue flows and chatbot utterances. In offering this tool, we explore teachers’ distinctive needs when designing chatbots to assist their teaching, and how chatbot design tools might better support them. Our findings reveal that teachers welcome the tool enthusiastically. Moreover, they see themselves as playwrights guiding both the students’ and the chatbot’s behaviors, while allowing for some improvisation. Their goal is to enable students to rehearse both desirable and undesirable reactions to cyberbullying in a safe environment. We discuss the design opportunities LLM-Chains offer for empowering teachers and the research opportunities this work opens up.

Many digital technologies have been invented to support mindfulness, the practice of bringing attention to the present moment without judgment. While most technologies focus on mindfulness meditation training for novices, in this paper, we explore designing technology to support everyday mindfulness activities for people with varying levels of experience. Through 9 magic machine workshops, 30 mindfulness practitioners explored and reflected on their personal experiences of everyday mindfulness, and generated designs that support their daily practice. Our findings identified six categories of designs conceptualized by our participants: everyday objects, physical spaces, wearables, metaphorical art, companions, and toys. We further analyze the practitioners’ thought processes and considerations for designs that support everyday mindfulness, such as eliciting and regulating emotion and associating mindfulness with routine daily activities. Finally, we discuss the implications of designing individualized mindfulness products and the potential of using co-design magic machine workshops to explore a practical design space.