Digital journaling offers a means for older adults to express themselves, document their lives, and engage in self-reflection, contributing to the maintenance of cognitive function and social connectivity. Although previous works have investigated the motivations and benefits of digital journaling for older adults, little technical support has been designed to offer assistance. We conducted a formative study with older adults and uncovered their encountered challenges and preferences for technical support. Informed by the findings, we designed a Large Language Model (LLM) empowered tool, JournalAIde, which provides vicarious experience, idea organization, sample text generation, and visual editing cues to enhance older adults' confidence, writing ability, and sustained attention during digital journaling. Through a between-subjects study and a field deployment, we demonstrated the JournalAIde's significant effectiveness compared to a baseline system in empowering older adults in digital journaling. We further investigated older adults' experiences and perceptions of LLM writing assistance.

In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by marking their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot features to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.

An effective task-oriented chatbot should be able to exert a certain level of Social Intelligence (SI), the ability to emulate human social behaviors to reduce user frustration and dissatisfaction. However, few studies explored using humor, a common rhetorical device in human-human interactions, to improve chatbots’ overall SI. To fill this gap, we proposed to apply self-mockery humor to a customer service chatbot in different interaction stages with users. We proposed a pipeline to create situated self-mockery for the chatbot and conducted a within-subject experiment (N=28) to compare it with a chatbot without self-mockery utterance. Results showed that the self-mockery chatbot was perceived as significantly funnier, more satisfactory, and delivering higher performance in two out of the five measured characteristics of SI with comparable performance in the rest. We further discussed how participants’ individual factors might affect the perceived helpfulness of self-mockery on SI and concluded with design considerations.

Community-based Question Answering (CQA) platforms can provide rich experience and suggestions for people who seek to construct Activity Plans (AP), such as bodybuilding or sightseeing. However, answer posts in CQA platforms could be too unstructured and overwhelming to be easily applied to AP construction, as validated by our formative study for understanding relevant user challenges. We therefore proposed an answer-post processing pipeline, based on which we built PlanHelper, a tool assisting users in processing the CQA information and constructing AP interactively. We conducted a within-subject study (N=24) with a Quora-like interface as the baseline. Results suggested that when creating AP with PlanHelper, users were significantly more satisfied with the informational support and more engaged during the interaction. Moreover, we performed an in-depth analysis on the user behaviors with PlanHelper and summarized the design considerations for such supporting tools.