What if text could be sculpted and refined like clay—or cultivated and pruned like a plant? Texterial reimagines text as a material that users can grow, sculpt, and transform. Current generative-AI models enable rich text operations, yet rigid, linear interfaces often mask such capabilities. We explore how the text-as-material metaphor can reveal AI-enabled operations, reshape the writing process, and foster compelling user experiences. A formative study shows that users readily reason with text-as-material, informing a conceptual framework that explains how material metaphors shift mental models and bridge gulfs of envisioning, execution, and evaluation in LLM-mediated writing. We present the design and evaluation of two technical probes: Text as Clay, where users refine text through gestural sculpting, and Text as Plants, where ideas grow serendipitously over time. This work expands the design space of writing tools by treating text as a living, malleable medium.

As children increasingly consume media on devices, parents look for ways this usage can support learning and growth, especially in domains like social-emotional learning. We introduce eaSEL, a system that (a) integrates social-emotional learning (SEL) curricula into children's video consumption by generating reflection activities and (b) facilitates parent-child discussions around digital media without requiring co-consumption of videos. We present a technical evaluation of our system's ability to detect social-emotional moments within a transcript and to generate high-quality SEL-based activities for both children and parents. Through a user study with N=20 parent-child dyads, we find that after completing an eaSEL activity, children reflect more on the emotional content of videos. Furthermore, parents find that the tool promotes meaningful active engagement and could scaffold deeper conversations around content. Our work paves directions in how AI can support children's social-emotional reflection of media and family connections in the digital age.

Conversational breakdowns in close relationships are deeply shaped by personal histories and emotional context, yet most NLP research treats conflict detection as a general task, overlooking the relational dynamics that influence how messages are perceived.In this work, we leverage nonviolent communication (NVC) theory to evaluate LLMs in detecting conversational breakdowns and assessing how relationship backstory influences both human and model perception of conflicts.Given the sensitivity and scarcity of real-world datasets featuring conflict between familiar social partners with rich personal backstories, we contribute the PERSONACONFLICTS CORPUS 1 , a dataset of N = 5, 772 naturalistic simulated dialogues spanning diverse conflict scenarios between friends, family members, and romantic partners.Through a controlled human study, we annotate a subset of dialogues and obtain finegrained labels of communication breakdown types on individual turns, and assess the impact of backstory on human and model perception of conflict in conversation.We find that the polarity of relationship backstories significantly shifted human perception of communication breakdowns and impressions of the social partners, yet models struggle to meaningfully leverage those backstories in the detection task.Additionally, we find that models consistently overestimate how positively a message will make a listener feel.Our findings underscore the critical role of personalization to relationship contexts in enabling LLMs to serve as effective mediators in human communication for authentic connection.

Despite living in an increasingly connected world, social isolation is a prevalent issue today. While social robots have been explored as tools to enhance social connection through companionship, their potential as asynchronous social platforms for fostering connection towards humanity has received less attention. In this work, we introduce the design of a social support companion that facilitates the exchange of emotionally relevant stories and scaffolds reflection to enhance feelings of connection via five design dimensions. We investigate how social robots can serve as “social proxies” facilitating human stories, passing stories from other human narrators to the user. To this end, we conduct a real-world deployment of 40 robot stations in users' homes over the course of two weeks. Through thematic analysis of user interviews, we find that social proxy robots can foster connection towards other people's experiences via mechanisms such as identifying connections across stories or offering diverse perspectives. We present design guidelines from our study insights on the use of social robot systems that serve as social platforms to enhance human empathy and connection.

This paper explores the design, development and evaluation of a digital platform that aims to assist young people who have experienced trauma in understanding and expressing their emotions and fostering social connections. Integrating principles from expressive arts and narrative-based therapies, we collaborate with lived experts to iteratively design a novel, user-centered digital tool for young people to create and share comics that represent their experiences. Specifically, we conduct a series of nine workshops with N=54 trauma-impacted youth and young adults to test and refine our tool, beginning with three workshops using low-fidelity prototypes, followed by six workshops with Cube, a web version of the tool. A qualitative analysis of workshop feedback and empathic relations analysis of artifacts provides valuable insights into the usability and potential impact of the tool, as well as the specific needs of young people who have experienced trauma. Our findings suggest that the integration of expressive and narrative therapy principles into Cube can offer a unique avenue for trauma-impacted young people to process their experiences, more easily communicate their emotions, and connect with supportive communities. We end by presenting implications for the design of social technologies that aim to support the emotional well-being and social integration of youth and young adults who have faced trauma.

Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories.While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style.Yet the relationship between empathy and narrative style is not fully understood.In this work, we empirically examine and quantify this relationship between style and empathy using LLMs and large-scale crowdsourcing studies.We introduce a novel, theory-based taxonomy, HEART (Human Empathy and Narrative Taxonomy) that delineates elements of narrative style that can lead to empathy with the narrator of a story.We establish the performance of LLMs in extracting narrative elements from HEART, showing that prompting with our taxonomy leads to reasonable, human-level annotations beyond what prior lexicon-based methods can do.To show empirical use of our taxonomy, we collect a dataset of empathy judgments of stories via a large-scale crowdsourcing study with N = 2, 624 participants.1 We show that narrative elements extracted via LLMs, in particular, vividness of emotions and plot volume, can elucidate the pathways by which narrative style cultivates empathy towards personal stories.Our work suggests that such models can be used for narrative analyses that lead to human-centered social and behavioral insights.1. Flatness/roundness (Keen, 2006) of the charac-

Escape rooms have become increasingly popular as a form of entertainment, in addition to being adopted by educators for their effectiveness in improving student engagement and learning. While they have been introduced in various educational contexts, from nursing to mathematics, and for different age groups, including K-12 and university students, little research has been conducted on the benefits of escape rooms for adult learning of artificial intelligence (AI). Furthermore, most escape room implementations lack relevance to real-world situations and challenges with using AI systems in the wild. This study explores the effectiveness of an escape room, DRONEscape, as a tool for teaching AI concepts to Air Force participants. The results suggest that escape rooms can most effectively facilitate engagement and collaboration, and have positive effects on learning AI concepts. This paper also provides considerations for improvements to future iterations of AI-themed escape rooms to enhance learning, collaboration, engagement, and enjoyment.

The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives.We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each others' experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP.Using insights from social psychology, we craft a framework that operationalizes empathic similarity in terms of three key features of stories: main events, emotional trajectories, and overall morals or takeaways.We create EM-PATHICSTORIES, a dataset of 1,500 personal stories annotated with our empathic similarity features, and 2,000 pairs of stories annotated with empathic similarity scores.Using our dataset, we finetune a model to compute empathic similarity of story pairs, and show that this outperforms semantic similarity models on automated correlation and retrieval metrics.Through a user study with 150 participants, we also assess the effect our model has on retrieving stories that users empathize with, compared to naive semantic similarity-based retrieval, and find that participants empathized significantly more with stories retrieved by our model.Our work has strong implications for the use of empathy-aware models to foster human connection and empathy between people.