With the emergence of new AI technologies, research on the potential for AI to function as teammates alongside humans has expanded. The recent introduction of highly capable large language models (LLMs) is particularly noteworthy, showing strong potential in human–AI teaming where communication is crucial. However, this novel technology has yet to be validated in human–AI teaming or as a teammate, hindering its application in research and practice. This article presents an empirical online experiment (N = 778) where participants engaged in a real-time and interdependent interaction with a commercially available LLM, with the presentation of the LLM manipulated to be either a tool or a teammate. Results show that when compared to presenting an LLM as a teammate rather than a tool significantly increases trust and significantly impacts the sentiment humans have when talking with their AI, with LLM teammates seeing more positive sentiment. Perceptions of trust, acceptance, and performance were generally high for LLMs presented as teammates. Despite these impacts, participants’ prior experiences with AI technology were still shown to predict the perceptions they formed with their AI teammate. Based on these findings, this article presents an important empirical result, which is that presenting highly capable AI, such as LLMs, as teammates can improve perception and interaction compared to presenting an AI as a tool. In turn, a discussion is had on how future research can continue to identify when and how to introduce LLMs and other AI technologies as teammates.

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other's working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality- derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.

Unemployed individuals experience emotional upheaval and financial constraints, and they struggle to share their challenges and obtain support from their family members and social circles. While prior work has shown that social media platforms enable their users to safely share their turmoil, hardships, and emotional upheavals and seek support, little is known about how the unemployed use such platforms. Based on a mixed-methods analysis of data retrieved from unemployment communities on Reddit, our study reveals that redditors in these communities engage in exchanges that range from queries seeking information about job searching to discussions about sensitive topics such as the health and social implications of unemployment. Also, while redditors use mostly negative emotional tonality in their texts when initiating a new post, the comments in response provide considerable social support in the form of esteem, emotional, informational, network, and instrumental support. Based on these findings, we share the theoretical implications of our work and offer design

Today, Conversational Agents (CA) are deeply integrated into the daily lives of millions of families, which has led children to extensively interact with such devices. Studies have suggested that the social nature of CA makes them a good learning companion for children. Therefore, to understand children's preferences for the use of CAs for the purpose of in-home learning, we conducted three participatory design sessions. In order to identify parents' requirements in this regard, we also included them in the third session. We found that children expect such devices to possess a personality and an advanced level of intelligence, and support multiple content domains and learning modes and human-like conversations. Parents desire such devices to include them in their children's learning activities, foster social engagement, and to allow them to monitor their children's use. This understanding will inform the design of future CAs for the purpose of in-home learning.

There is racial diversity as well as economic inequality in the United States (U.S.). To gain a nuanced understanding of how households from different socio economic and racial backgrounds integrate technology into their lives, we conducted a diary study with 22 parents who were Asian Indian (the fastest-growing immigrant population in U.S.) and 18 who were White American (the largest racial group in U.S.) parents from the working and middle classes. The participants logged in-situ instances of using smart phones and speaker use by, with, and around children for 8 weeks, and were interviewed once every four weeks (two times in total). Our findings reveal differences and similarities in parents' attitudes and practices of using or not using these devices around and with children, in parental restrictions of children's use of technology, and children's daily use patterns. The paper concludes with a discussions of the implications of our findings and suggestions for future design improvements in smart phones and speakers.

Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is "local", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.