Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study ($n$=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.
Conversational AI-Enhanced Exploration System to Query Large-Scale Digitised Collections of Natural History Museums
School of Computer Science, University of Technology Sydney（技术科技大学计算机科学学院）；School of Design, University of Technology Sydney（技术科技大学设计学院）；Australian Museum Research Institute, Australian Museum（澳大利亚博物馆研究 institute, 澳大利亚博物馆）；Australian Museum（澳大利亚博物馆）
RemiVoice: Supporting Reminiscence Therapy for Older Adults with Mild Dementia Through Voice-First Conversational AI
Florida International University（佛罗里达国际大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Arizona（亚利桑那大学）；University of Cincinnati（辛辛那提大学）；Miami VA Health Care System（迈阿密退伍军人健康护理系统）
Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for…
Conversational Tactile Data Interfaces: Co-Designing Accessible Data Experiences with Blind Users Using Refreshable Tactile Displays and Conversational AI
PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation
University of Warsaw（华沙大学）；Warsaw University of Technology（华沙理工大学）
Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds o…
