Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Digital transformation in financial services provision: A Nigerian perspective to the adoption of chatbot

Abstract: Purpose: Recognising the high numbers of unbanked and financially excluded adults in Nigeria, this study positions chatbot as a digital transformation tool to radically change business model, improve customer experience, and enhance financial inclusion in emerging markets. Methodology: The Search-Access-Test (S-A-T) model was adopted to understand how Nigerian banks are adopting chatbots. Findings: Majority of Nigerian banks now have chatbots which enhance customer engagement and financial inclusion. WhatsApp was the most frequently used platform. Chatbots were often branded and presented with female gender identification. The chatbots were less responsive beyond their pre-defined path. While Nigeria is a multilingual country with English being the original language, none of the chatbots used any of the Nigerian’s local languages. Originality: While many theoretically based model for investigating the adoption of digital technologies has often placed focus on users’ ability to engage, this study takes an alternative perspective; by using the Search-Access-Test (S-A-T) model, it lays the responsibilities on the banks and chatbot developer to ensure that their chatbots are secure, responsive and able to meet the needs of the customers. Practical implications: Brands needs to reevaluate their chatbots with regards to responsiveness, pre-defined questions, verification and privacy. There are also possibilities of branding the chatbot and developing content creation strategies for proper engagement. Beyond English, the integration of African languages into chatbot is essential for digital transformation. Digital literacy and skills, particularly in the field of science, technology, engineering and mathematics (STEM) should be supported to equip future developers and create more jobs.

Human-Aided Bots

Abstract: A chatbot is an example of a text-based conversational agent. While natural language understanding and machine learning techniques have advanced rapidly, current fully automated chatbots still struggle to serve their users well. Human intelligence, brought by crowd workers, freelancers, or even full-time employees can be embodied in the chatbot logic to fill the gaps caused by limitations of fully automated solutions. In this paper, we investigate human-aided bots, i.e., bots (including chatbots) using humans in the loop to operate. We survey industrial and academic examples of human-aided bots, discuss their differences and common patterns, and identify open research questions.

Developing a Chatbot for College Student Programme Advisement

Abstract: Elective course selection often challenges students to make decisions concerning their academic interests and other practical issues such as graduation plan, class scheduling, and difficulty of course content Conversations with academic advisors and peers are usually considered as a useful process for obtaining official and informal information, rearranging priorities, and making compromise in the decision The paper describes the design and development of a conversational agent called EASElective for elective course selection EASElective is designed to complement existing academic advising services with an online natural language interactive interface that will support a conversation on topics from basic official course data to informal students' opinions The major design components of EASElective include intent detection, routines for conversation management, dialogue design, sustainable students' opinion collection and analysis, and course information management The paper also describes a study on the perceived usefulness of EASElective The findings were found to be largely positive and EASElective has unique functions and characteristics when compared to other conventional academic advising services

Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys.

Abstract: 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 when 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, 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 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses in terms of relevance, depth, and readability. Based on our results, we discuss design implications for creating AI-powered chatbots to conduct effective surveys and beyond.

Gender Bias in Chatbot Design

Abstract: A recent UNESCO report reveals that most popular voice-based conversational agents are designed to be female. In addition, it outlines the potentially harmful effects this can have on society. However, the report focuses primarily on voice-based conversational agents and the analysis did not include chatbots (i.e., text-based conversational agents). Since chatbots can also be gendered in their design, we used an automated gender analysis approach to investigate three gender-specific cues in the design of 1,375 chatbots listed on the platform chatbots.org. We leveraged two gender APIs to identify the gender of the name, a face recognition API to identify the gender of the avatar, and a text mining approach to analyze gender-specific pronouns in the chatbot’s description. Our results suggest that gender-specific cues are commonly used in the design of chatbots and that most chatbots are – explicitly or implicitly – designed to convey a specific gender. More specifically, most of the chatbots have female names, female-looking avatars, and are described as female chatbots. This is particularly evident in three application domains (i.e., branded conversations, customer service, and sales). Therefore, we find evidence that there is a tendency to prefer one gender (i.e., female) over another (i.e., male). Thus, we argue that there is a gender bias in the design of chatbots in the wild. Based on these findings, we formulate propositions as a starting point for future discussions and research to mitigate the gender bias in the design of chatbots.