Lim M, et al. Restor Dent Endod. 2019 Nov;45:e35. /Synapse/10.5395/rde.2020.45.e35

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.

Artificial intelligence (AI) is increasingly being deployed in high-stakes domains, such as disaster relief and radiology, to aid practitioners during the decision-making process. Explainable AI techniques have been developed and deployed to provide users insights into why the AI made certain predictions. However, recent research suggests that these techniques may confuse or mislead users. We conducted a series of two studies to uncover strategies that humans use to explain decisions and then understand how those explanation strategies impact visual decision-making. In our first study, we elicit explanations from humans when assessing and localizing damaged buildings after natural disasters from satellite imagery and identify four core explanation strategies that humans employed. We then follow up by studying the impact of these explanation strategies by framing the explanations from Study 1 as if they were generated by AI and showing them to a different set of decision-makers performing the same task. We provide initial insights on how causal explanation strategies improve humans' accuracy and calibrate humans' reliance on AI when the AI is incorrect. However, we also find that causal explanation strategies may lead to incorrect rationalizations when AI presents a correct assessment with incorrect localization. We explore the implications of our findings for the design of human-centered explainable AI and address directions for future work.

Kim JS, et al. Restor Dent Endod. 2013 Feb;38(1):36-42. http://dx.doi.org/10.5395/rde.2013.38.1.36

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.