Handbook of Cognitive Behavioral Therapies

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

While advanced text generation algorithms (e.g., GPT-3) have enabled writers to co-create stories with an AI, guiding the narrative remains a challenge. Existing systems often leverage simple turn-taking between the writer and the AI in story development. However, writers remain unsupported in intuitively understanding the AI’s actions or steering the iterative generation. We introduce TaleBrush, a generative story ideation tool that uses line sketching interactions with a GPT-based language model for control and sensemaking of a protagonist’s fortune in co-created stories. Our empirical evaluation found our pipeline reliably controls story generation while maintaining the novelty of generated sentences. In a user study with 14 participants with diverse writing experiences, we found participants successfully leveraged sketching to iteratively explore and write stories according to their intentions about the character’s fortune while taking inspiration from generated stories. We conclude with a reflection on how sketching interactions can facilitate the iterative human-AI co-creation process.

TaleBrush: Sketching Stories with Generative Pretrained Language Models

We may not be able to make you love reading, but designing and engineering time the psychology of time perception in software will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

Designing And Engineering Time The Psychology Of Time Perception In Software

Textual comments from peers with informational and emotional support are beneficial to members of online mental health communities (OMHCs). However, many comments are not of high quality in reality. Writing support technologies that assess (AS) the text or recommend (RE) writing examples on the fly could potentially help support providers to improve the quality of their comments. However, how providers perceive and work with such technologies are under-investigated. In this paper, we present a technological prototype MepsBot which offers providers in-situ writing assistance in either AS or RE mode. Results of a mixed-design study with 30 participants show that both types of MepsBots improve users' confidence in and satisfaction with their comments. The AS-mode MepsBot encourages users to refine expressions and is deemed easier to use, while the RE-mode one stimulates more support-related content re-editions. We report concerns on MepsBot and propose design considerations for writing support technologies in OMHCs.

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Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community

As service robots are envisioned to provide decision-making support (DMS) in public places, it is becoming essential to design the robot's manner of offering assistance. For example, robot shop assistants that proactively or reactively give product recommendations may impact customers' shopping experience. In this paper, we propose an anticipation-autonomy policy framework that models three levels of proactivity (high, medium and low) of service robots in DMS contexts. We conduct a within-subject experiment with 36 participants to evaluate the effects of DMS robot's proactivity on user perceptions and interaction behaviors. Results show that a highly proactive robot is deemed inappropriate though people can get rich information from it. A robot with medium proactivity helps reduce the decision space while maintaining users' sense of engagement. The least proactive robot grants users more control but may not realize its full capability. We conclude the paper with design considerations for service robot's manner.

/pdf/design-and-evaluation-of-service-robot-s-proactivity-in-a1pj2dawk0.pdf

Design and Evaluation of Service Robot's Proactivity in Decision-Making Support Process

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

CASS: Towards Building a Social-Support Chatbot for Online Health Community

Maintaining a positive group emotion is important for team collaboration. It is, however, a challenging task for self-managing teams especially when they conduct intra-group collaboration via text-based communication tools. Recent advances in AI technologies open the opportunity of using chatbots for emotion regulation in group chat. However, little is known about how to design such a chatbot and how group members react to its presence. As an initial exploration, we design GremoBot based on text analysis technology and emotion regulation literature. We then conduct a study with nine three-person teams performing different types of collective tasks. In general, participants find GremoBot useful for reinforcing positive feelings and steering them away from negative words. We further discuss the lessons learned and considerations derived for designing a chatbot for group emotion management.

GremoBot: Exploring Emotion Regulation in Group Chat

Recently, major software development platforms have started to provide automatic reviewer recommendation (ARR) services for pull requests, to improve the collaborative coding review process. However, the user experience of ARR is under-investigated. In this paper, we use a two-stage mixed-methods approach to study how software developers perceive and work with the Facebook mention bot, one of the most popular ARR bots in GitHub. Specifically, in Stage I, we conduct archival analysis on projects employing mention bot and a user survey to investigate the bot's performance. A year later, in Stage II, we revisit these projects and conduct additional surveys and interviews with three user groups: project owners, contributors and reviewers. Results show that developers appreciate mention bot saving their effort, but are bothered by its unstable setting and unbalanced workload allocation. We conclude with design considerations for improving ARR services.

https://dl.acm.org/doi/pdf/10.1145/3202667.3202694

Zhenhui Peng

Papers

Design and Evaluation of Service Robot's Proactivity in Decision-Making Support Process

Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community

CASS: Towards Building a Social-Support Chatbot for Online Health Community

GremoBot: Exploring Emotion Regulation in Group Chat

Exploring How Software Developers Work with Mention Bot in GitHub