Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills-the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user’s free-text responses, and probes answers whenever needed. To investigate the effectiveness and limitations of such a chatbot in conducting surveys, we conducted a field study involving about 600 participants. In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey. Our detailed analysis of over 5,200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses measured by Gricean Maxims in terms of their informativeness, relevance, specificity, and clarity. Based on our results, we discuss design implications for creating AI-powered chatbots to conduct effective surveys and beyond.

Various methods have been proposed to help find answerers for a question in QA communities, but almost all work heavily depends on users' QA history. In this paper we seek to investigate the feasibility of leveraging users' non-QA social activities as a way of gaining insight into their question answering behavior. We collected data of 4,484 users on a QA community in an enterprise and examined the relationship of their QA behaviors and non-QA activities supported by other social tools. We found that the two sets of behavioral indicators are significantly correlated. The top user group for non-QA activities outperformed lower groups in both number and quality of the answers. Regression analysis showed involvement in personal blog, micro-blog and forum and replying and recommending behaviors in non-QA communities were predictive of a user's likelihood of answering questions. Online observations provided a qualitative understanding. Design implications and future work were discussed.

Previous research has revealed the following three challenges for knowledge sharing: awareness of expertise distribution, motivation for sharing, and network ties. In this case study, we examine how different generations of information and communication technologies (ICTs), ranging from e‐mail to micro‐blogging, can help address these challenges. Twenty‐one interviews with employees from a multinational company revealed that although people think social media can better address these challenges than older tools, the full potential of social media for supporting knowledge sharing has yet to be achieved. When examining the interconnections among different ICTs, we found that employees′ choice of a combination of ICTs, as affected by their functional backgrounds, could create “technological divides” among them and separate resources. This finding indicates that having more ICTs is not necessarily better. ICT integration, as well as support for easy navigation, is crucial for effective knowledge search and sharing. Adaptation to local culture is also needed to ensure worldwide participation in knowledge sharing.

Dandelion is a wiki-based tool that supports coordinated, collaborative authoring. In this paper, we present an ex-tended version of Dandelion, which provides an email inter-face for users to accomplish their tasks by email in a coordinated, collaborative authoring process. Specifically, Dandelion employs a semi-structured, template-based approach that allows users to use templates to specify their requests in email. These emailed requests can be interpreted by Dandelion and are then used to automatically drive the collaboration flow. As part of its actions, Dandelion automatically creates a wiki page and dynamically updates it to record co-authoring tasks and collate co-authored content. As a result, users can use their familiar tool (email) to accomplish their tasks in a co-authoring process, while leveraging a wiki for additional benefits (e.g., obtaining collaboration awareness and formatting the text). Our preliminary study with two groups of users shows the usefulness of both Dandelion email and wiki features and their impact on collaboration effectiveness.

In this paper we feature a set of research projects done at several IBM Research laboratories across the world. The work featured here focuses on the topic of smart social collaboration, which studies, designs, and develops social collaboration principles and technologies that can help customize and enhance existing social collaboration tools to suit specific user needs, including cultural, business, and personal needs.

In the current study, we explored Twitter as a useful and practical extension to a wiki-based collaborative work space. A two-week experiment and a survey study shed some light on the potential benefits of integrating Twitter, or other existing social networking tools with a formal collaborative work space in encouraging meta-data level communication and promoting informal awareness.

Dandelion is a tool that extends wikis to support coordinated, collaborative authoring using a tag-based approach. Specifically, users can insert tags in a wiki page to specify various co-authoring tasks. These tags can then be executed to help drive and manage the collaboration workflow, and provide content-centric collaboration awareness for all the co-authors. Four successful pilot deployments and positive user feedback show the practical value of Dandelion, especially its value in supporting a structured, collaborative authoring process often seen in business settings.

Efficient testing of the collaborative and real-time aspects of collaborative real-time editing tools (CRETs) is extremely challenging. Changes in testing parameters and conflict resolution policies result in significant time and effort for modifying the corresponding test cases. In this paper, we propose a time-line diagram approach to visually model aspects of timing and collaborative conflicts useful for efficient generation of test cases. A specification language, ACDATE, is introduced to formally specify the corresponding test scenarios. We develop an algorithm that allows configuring test parameters and collaboration policies on the fly, automatically generates textual test cases corresponding to the timeline diagram, and creates corresponding test scripts in the ACDATE language. A prototype implementation shows good results, with automatically generated test cases consistent across from a visual and textual perspective.