Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.

The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots

Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.
Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.
Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.
Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.
Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.

Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited functionality is exposed within the user interface. This approach to user interaction misses an opportunity for users to more deeply engage with the model by having the context of their previous interactions, as well as the context of their code, inform the model’s responses. We developed a prototype system – the Programmer’s Assistant – in order to explore the utility of conversational interactions grounded in code, as well as software engineers’ receptiveness to the idea of conversing with, rather than invoking, a code-fluent LLM. Through an evaluation with 42 participants with varied levels of programming experience, we found that our system was capable of conducting extended, multi-turn discussions, and that it enabled additional knowledge and capabilities beyond code generation to emerge from the LLM. Despite skeptical initial expectations for conversational programming assistance, participants were impressed by the breadth of the assistant’s capabilities, the quality of its responses, and its potential for improving their productivity. Our work demonstrates the unique potential of conversational interactions with LLMs for co-creative processes like software development.

/pdf/the-programmers-assistant-conversational-interaction-with-a-3uddd4hd.pdf

The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development

This book provides a comprehensive introduction to Conversational AI.
 While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.

pdf/conversational-ai-dialogue-systems-conversational-agents-and-4w2ce8l32i.pdf

Conversational Ai: Dialogue Systems, Conversational Agents, and Chatbots

Chatbots are becoming increasingly popular as a human-computer interface. The traditional best practices normally applied to User Experience (UX) design cannot easily be applied to chatbots, nor can conventional usability testing techniques guarantee accuracy. WeightMentor is a bespoke self-help motivational tool for weight loss maintenance. This study addresses the following four research questions: How usable is the WeightMentor chatbot, according to conventional usability methods?; To what extend will different conventional usability questionnaires correlate when evaluating chatbot usability?; And how do they correlate to a tailored chatbot usability survey score?; What is the optimum number of users required to identify chatbot usability issues?; How many task repetitions are required for a first-time chatbot users to reach optimum task performance (i.e. efficiency based on task completion times)? This paper describes the procedure for testing the WeightMentor chatbot, assesses correlation between typical usability testing metrics, and suggests that conventional wisdom on participant numbers for identifying usability issues may not apply to chatbots. The study design was a usability study. WeightMentor was tested using a pre-determined usability testing protocol, evaluating ease of task completion, unique usability errors and participant opinions on the chatbot (collected using usability questionnaires). WeightMentor usability scores were generally high, and correlation between questionnaires was strong. The optimum number of users for identifying chatbot usability errors was 26, which challenges previous research. Chatbot users reached optimum proficiency in tasks after just one repetition. Usability test outcomes confirm what is already known about chatbots - that they are highly usable (due to their simple interface and conversation-driven functionality) but conventional methods for assessing usability and user experience may not be as accurate when applied to chatbots.

/pdf/usability-testing-of-a-healthcare-chatbot-can-we-use-2tgfhjy3g3.pdf

Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Obesity and Overweight !are significant risk factors for many chronic conditions, such as type 2 diabetes. Weight loss is difficult and maintaining weight loss is a greater challenge. Effective communication is hindered by conflicting information and the sensitivity of the subject of obesity. Recent technological solutions for weight loss maintenance are limited. A chatbot would be appropriate for supporting weight loss as it does not require a human operator, is available 24 hours a day, and supports natural communication while maintaining anonymity. Such a system may also be integrated with popular social media platforms such as Facebook Messenger. This paper describes the design and development of the WeightMentor chatbot, a self-help motivational tool for weight loss maintenance. Chatbots may have the potential to contribute to obesity prevention and management.

/pdf/weightmentor-a-new-automated-chatbot-for-weight-loss-fer0acjgj7.pdf

WeightMentor: A New Automated Chatbot for Weight Loss Maintenance

Obesity and overweight are significant health risks. Effective communication can improve weight loss maintenance. There are technologies to support communication in weight management, such as text messaging that has been shown to benefit self-reporting and motivation but has its limitations. Having a conversation is an emotional experience, and chatbots are conversation-driven intelligent systems. Chatbots have been reported to increase compliance with health interventions. The aim of this study was to identify the needs of individuals who are maintaining weight loss in order to design and develop WeightMentor, a weight loss maintenance chatbot. This was a needs assessment and technology (chatbot) development study. The needs assessment identified the needs adults aged 18+ who were maintaining weight loss using semi-structure interviews. These needs were used to inform the design and development of a chatbot. Data were analyzed using thematic analysis. Findings identified five key themes: (1) Weight loss maintenance is challenging; (2) Social contact is beneficial but may also reinforce unhealthy habits; (3) Apps should be convenient and support progress tracking; (4) Personal messages should be specific and relevant; (5) Chatbots have potential for weight loss maintenance. Chatbot, WeightMentor, was designed and developed based on findings from the needs assessment and a review of the most popular nutrition apps. WeightMentor operates within Facebook messenger, which is currently one of the most popular social media platform. WeightMentor's purpose is to positively influence the user's emotions while maintaining weight loss. Its functions include self-reporting of diet and physical activity, viewing previous self-reporting trends, and motivational messaging. Chatbots such as WeightMentor has the potential to aid weight loss maintenance. Further research is required to test the usability and effectiveness of this WeightMentor.

WeightMentor, bespoke chatbot for weight loss maintenance: Needs assessment & Development

A chatbot usability questionnaire (CUQ) was designed to measure the usability of chatbots. Study objectives: 1) to test the construct validity of CUQ (i.e. does it differentiate between chatbots that we rank as having poor, average or good usability), 2) to assess the intra-rater reliability of CUQ (i.e. do participants provide the same answers/scores when assessing the usability of the same chatbots two weeks apart), and 3) to undertake exploratory factor analysis to study the underlying factors that CUQ measures. Three chatbots were selected by co-authors that were regarded as having good, average and poor usability. Participants used each of the chatbots and completed the CUQ scale for each. Participants repeated this process two weeks later to facilitate the measurement intra-rater variability. Paired t-tests were used to compare CUQ scores from each of the three chatbots. Exploratory factor analysis was used to identify the factors within the CUQ. Paired t-tests and correlation was used to measure intra-rater reliability. There was a total of 156 CUQ survey completions (26 participants completed the CUQ for 3 different chatbots and for 2 rounds: 26 * 3 * 2 = 156). Intra-rater reliability was supported as there was a good correlation between how participants completed the CUQ for the same chatbot at approximately two weeks apart (r > 0.7). As a form of construct validity, the CUQ scores for each of the three chatbots were statistically significant (p < 0.05). Factor analysis shows that the CUQ measures four factors 1) personality, 2) user experience, 3) error handling and 4) onboarding of the chatbot. 

Towards Validating a Chatbot Usability Scale

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Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

WeightMentor: A New Automated Chatbot for Weight Loss Maintenance

WeightMentor, bespoke chatbot for weight loss maintenance: Needs assessment & Development

Towards Validating a Chatbot Usability Scale