Chatbot Design Research Articles

Design of an AI Chatbot Using Generative AI to Access Local Database

The "College Chatbot" project aims to create an advanced conversational agent tailored for college-related inquiries. This innovative chatbot incorporates a versatile interface, capable of seamlessly processing both text and voice inputs, enhancing user accessibility. Its distinctive feature includes multilingual output capabilities, enabling responses in local languages to cater to a diverse user base. The primary function of the chatbot is to comprehensively address a wide array of college-related queries, providing students and users with accurate and timely information on various aspects of college life, academic affairs, and related topics. By combining a user-friendly interface with linguistic flexibility, the College Chatbot seeks to revolutionize the way students interact with and retrieve information from their educational institutions. Keywords- AI chatbot, Generative AI, Local database, Natural Language processing, Data Integrarion,User Intraction, Query mechanism, Model training.

CHATBOT USING LARGE LANGUAGE MODEL

The concept of Natural Language Processing has seen a remarkable advancement in the recent years. This remarkable advancement was particularly with the development of Large Language Models (LLM). Large Language Models are used to develop a human like conversations. This LLM is a part of Natural Language Processing which focuses on enabling computers to understand, interpret, and generate human language. The existing system of chatbots does not generate human like responses. The proposed system of chatbots uses the power of Large Language Models to generate more human like responses, providing the conversation in a natural way. By genereating human like respones, it will be in a natural way for the user. To enhance user experience, the chatbot uses a dynamic learning mechanism, by which it continuously adapt to user preferences and evolving conversational patterns. This system uses feedbacks from the users to refine its responses everytime.Moreover, the chatbot is designed with a multi-turn conversational context awareness, allowing it to maintain coherence and relevance throughout extended dialogues.The effectiveness of the proposed chatbot is evaluated through user testing, comparing its performance against traditional rule-based chatbots and existing conversational agents. This report explains about the usage of Large Language Models in the design and implementation of conversational chatbots. The outcomes of this research contribute to the advancement of intelligent chatbot systems, demonstrating the potential of large language models to significantly enhance conversational AI applications.

Making the Case for Audience Design in Conversational AI: Users’ Pragmatic Strategies and Rapport Expectations in Interaction with a Task-Oriented Chatbot

Abstract With chatbots becoming more and more prevalent in commercial and service contexts, they need to be designed to provide equitable access to services for all user groups. This paper argues that insights into users’ pragmatic strategies and rapport expectations can inform the audience design of chatbots and ensure that all users can equally benefit from the services they facilitate. The argument is underpinned by the analysis of simulated user interactions with a chatbot facilitating health appointment bookings, users’ introspective comments on their interactions, and users’ qualitative survey comments. The study shows that users’ pragmatic strategies show considerable variation. It also shows the negative impact of user experiences when the chatbot’s language and interaction patterns do not align with users’ rapport expectations. In closing, the paper uses these findings to define audience design for chatbots and discuss how audience design can be realized and supported by research.

HEALPAL: YOUR PERSONAL HEALTH COMAPANION

In an era where rapid technological advancements intersect with healthcare needs, the development of an intelligent healthcare chatbot has emerged as a promising solution. This study presents a comprehensive exploration into the conceptualization, design, and implementation of an AI-powered chatbot tailored to provide accurate and personalized healthcare information. Integrating Natural Language Processing (NLP) algorithms and Artificial Intelligence Markup Language (AIML), the chatbot aims to bridge the gap between individuals and reliable medical knowledge. Keywords—Chatbot, NLP, Markup Language

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.

Towards more trusted virtual physicians: the combinative effects of healthcare chatbot design cues and threat perception on health information trust

ABSTRACT Although virtual healthcare assistants have been extensively adopted in the healthcare industry, users still question their reliability. Based on the Computers are Social Actor (CASA) paradigm, the current study conducted an experiment to examine how different chatbot design cues (Chatbot vs. Layperson vs. Doctor) affect users’ trust towards virtual healthcare assistants. Results indicate that the doctor-like design cues and bot-like design cues (vs. layperson-like design cues) elicited significantly greater perceived chatbot expertise, which enhanced users’ trust in health information. This study further found the significant moderating effects of users’ perceived threat on chatbot expertise and privacy concerns. Positive effects of the doctor-like design cue and the bot-like design cue on perceived expertise were found to be significant only for those whose perceived threat was high. Interestingly, the doctor-like design cues led to greater (less) privacy concerns when the perceived threat level was low (high). The findings provide human–computer interaction researchers and chatbot UX/UI designers with important theoretical and practical implications.

Measuring and Clustering Heterogeneous Chatbot Designs

Conversational agents, or chatbots, have become popular to access all kind of software services. They provide an intuitive natural language interface for interaction, available from a wide range of channels including social networks, web pages, intelligent speakers or cars. In response to this demand, many chatbot development platforms and tools have emerged. However, they typically lack support to statically measure properties of the chatbots being built, as indicators of their size, complexity, quality or usability. Similarly, there are hardly any mechanisms to compare and cluster chatbots developed with heterogeneous technologies. To overcome this limitation, we propose a suite of 21 metrics for chatbot designs, as well as two clustering methods that help in grouping chatbots along their conversation topics and design features. Both the metrics and the clustering methods are defined on a neutral chatbot design language, becoming independent of the implementation platform. We provide automatic translations of chatbots defined on some major platforms into this neutral notation to perform the measurement and clustering. The approach is supported by our tool Asymob , which we have used to evaluate the metrics and the clustering methods over a set of 259 Dialogflow and Rasa chatbots from open-source repositories. The results open the door to incorporating the metrics within chatbot development processes for the early detection of quality issues, and to exploit clustering to organise large collections of chatbots into significant groups to ease chatbot comprehension, search and comparison.

Chatbot Design and Implementation: Towards an Operational Model for Chatbots

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and a model is developed and validated to support the planning and implementation of chatbot projects. The presented model can serve as an exemplary guide for researchers and practitioners working in this field. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience in chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Dialoging Resonance in Human-Chatbot Conversation: How Users Perceive and Reciprocate Recommendation Chatbot's Self-Disclosure Strategy

Using chatbots to make recommendations is increasingly popular. The design of recommendation chatbots has mainly been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and relational strategies, such as self-disclosure from a chatbot, may influence users' perception and acceptance of the recommendation. In this work, we designed, implemented, and evaluated a social chatbot capable of performing three different levels of self-disclosure: factual information (low), cognitive opinions (medium), and emotions (high). In the evaluation, we recruited 372 participants to converse with the chatbot on two topics: movies and COVID-19 experiences. In each topic, the chatbot conducted small talks and made relevant recommendations to the topic. Participants were randomly assigned to four experimental conditions where the chatbot used factual, cognitive, emotional, and adaptive strategies to perform self-disclosures. By training a text classifier to identify users' level of self-disclosure in real-time, the adaptive chatbot can dynamically match its self-disclosure language to the level of disclosure exhibited by the users. Our results show that users reciprocate with higher-level self-disclosure when a recommendation chatbot displays emotions throughout the conversation. The utilization of emotional disclosure by the chatbot resulted in enhanced enjoyment during interactions and a more favorable perception of the bot. This, in turn, led to greater effectiveness in making recommendations, including a higher likelihood of accepting the recommendation. We discuss the understandings obtained and implications to future design.

Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots

Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several alternatives beyond the famous ChatGPT. However, training, fine-tuning, and updating such intelligent chatbots consume substantial amounts of electricity, resulting in significant carbon emissions. The research and development of all intelligent LLMs and software, hardware manufacturing (e.g., graphics processing units and supercomputers), related data/operations management, and material recycling supporting chatbot services are associated with carbon emissions to varying extents. Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact. In this work, we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots. Based on a life-cycle and interaction analysis of these phases, we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints. While anticipating the enormous potential of this advanced technology and its products, we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.

Application of ChatGPT in Information Literacy Instructional Design

Recent developments in generative artificial intelligence tools have prompted immediate reactions in the academic library community. While most studies focus on the potential impact on academic integrity, this work explored constructive applications of ChatGPT in the design of instructional materials for courses in academic information literacy. The starting point was the use of openly licenced information resources or content infrastructure as facilitators in the creation of educational materials. In the first phase, course teaching material was developed using a prompt engineering strategy, predefined standards, and a prompt script. As a second step, we experimented with designing a custom chatbot model connected to a pre-defined corpus of source documents. The results demonstrated that the final teaching material required careful revision and optimisation before use in an actual instructional programme. The experimental design of the custom chatbot was able to query specific user-defined documents. Taken together, these findings suggest that the strategic and well-planned use of ChatGPT technology in content creation can have substantial benefits in terms of time and cost efficiency. In the context of information literacy, the results provide a practical and innovative solution to integrate the new technology tool into instructional practices.

Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications.

Chatbot Design for Interview Questions Using Neural Network Models on the CarTech Website

Abstract: This research focuses on analyzing interview questions using a neural network model, implemented on the CarTech website. With the main aim of optimizing the interaction between users and the system through the questions asked, this research takes an innovative step by utilizing Google Collab as a development platform. For this research, several paragraphs were carried out, namely problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. These stages were carried out so that this research could get good results, plus the integration between Google Collab and chatbot which made it possible for this research to get good results. Google Collab makes it easy to use neural network models and integrate with chatbots, enabling efficient and effective testing and deployment of models. The results of this study are quite impressive, with an accuracy of 92%, demonstrating the model's ability to process and understand interview questions with high precision. The aim of this research is not only to explore the potential of neural network models in automatically understanding questions and providing accurate responses, but also to show how this technology can be integrated into web applications to improve the quality of user interactions, making AI-based chatbots a viable solution and effective in improving user experience on the CarTech website. In conclusion, by utilizing AI you will also get good results. As in this research, AI can help analyze interview questions with neural network models.

Campus Companion : Creating a Supportive Chat – Assistant for Students

This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.

Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study

In recent years, AI technologies have been developed to promote students' self‐regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI‐based (SRLbot) and rule‐based AI chatbots (Nemobot) in a 3‐week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule‐based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT‐enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self‐regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule‐based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. Practitioner notesWhat is already known about this topic AI technologies have been used to support student self‐regulated learning (SRL) across subjects. SRL has been identified as an important aspect of student learning that can be developed through technological support. Generative AI technologies like ChatGPT have shown potential for enhancing student learning by providing personalized guidance and feedback. What this paper adds This paper reports on a case study that specifically examines the effectiveness of ChatGPT in promoting SRL among secondary students. The study provides evidence that ChatGPT can enhance students' science knowledge, motivation and SRL compared to a rule‐based AI chatbot. The study offers insights into how ChatGPT can be used as a tool to facilitate SRL and promote sustained learning habits. Implications for practice and/or policy The findings of this study suggest that educators should consider the potential of ChatGPT and other generative AI technologies to support student learning and SRL. Educators and students should be aware of the limitations of AI technologies and ensure that they are used appropriately to generate desired responses. It is also important to equip teachers and students with AI competencies to enable them to use AI for learning and teaching.

Proposing Usability-UX technologies for the design and evaluation of text-based chatbots

Chatbots are interactive systems that communicate using natural language with human users, via a textual interface or voice activation. These tools are useful for many spheres of business such as Customer Service, Sales, Education and Learning, Health and Entertainment. Recently, chatbots have become popular, with significant growth in the software industry, especially text-based chatbots. This is encouraging developers to create their own tools, as well as attracting efforts from researchers into this area. Despite this highlight, technologies to guarantee the quality of chatbots and user satisfaction are not keeping up with the growing demand for these tools. Considering this, there is a need to propose technologies capable of supporting developers and development teams in the process of building and evaluating chatbots. Therefore, this research proposes to develop artifacts applicable to the design and evaluation process of chatbots, based on quality attributes identified in systematic literature reviews related to Usability and User Experience (UX), due to the importance and impact that these aspects have on user satisfaction and the perceived quality of the system. The first artifact is the U2Chatbot inspection checklist, developed to assist development teams in the process of identifying defects in text-based chatbots. The second artifact is a set of interface design patterns, DP-U2Chatbot, containing useful examples to support developers in the process of building chatbots. The technologies were subjected to the necessary evaluations. The results of the empirical study regarding the U2Chatbot inspection checklist indicated that participants considered the technology useful for discovering defects in chatbots, however, ease of use could be improved. The participants' experience discreetly influenced the effectiveness and efficiency of the technique, leading us to believe that professionals with a certain level of inspection experience can benefit more from the checklist. Regarding the evaluation of DP-U2Chatbot design patterns, the results generally indicated that the technology is easy to understand and useful in supporting the design of chatbots, helping to build better tools.

Confirmatory Exploration of innovation in virtual exchanges through a studyon Virtual Agents

The use of Conversational Agents for consumer services continues to grow, and the service industry is no exception. Carried out among 340 Moroccan consumers, this study shows the importance of perceived anthropomorphism, social presence, perceived trust, social influence, perceived cost, and facilitating conditions of a conversational agent in increasing adoption intention using the UTAUT model. The expected effort had no effect. This research confirms the moderating role of age on the relationship between social influence and intention to use, while gender and level of education had no moderating effect. The technology acceptance model tested is a powerful tool for understanding why people do or do not use conversational agents. It can explain nearly 75,8% of the variance in behavioral intention. Various recommendations are put forward to optimize the design and successful implementation of chatbots, enabling them to take their rightful place among digital tools.

Validation of a Thai artificial chatmate designed for cheering up the elderly during the COVID-19 pandemic.

The COVID-19 pandemic severely affected populations of all age groups. The elderly are a high-risk group and are highly vulnerable to COVID-19. Assistive software chatbots can enhance the mental health status of the elderly by providing support and companionship. The objective of this study was to validate a Thai artificial chatmate for the elderly during the COVID-19 pandemic and floods. Chatbot design includes the establishment of a dataset and emotional word vectors in which data consisting of emotional sentences were converted into the word vector form using a pre-trained word2vec model. A word vector was then input into a convolutional neural network (CNN) and trained until the model converges using sentence embedding and similarity word segmentation. Sentence vectors were generated by averaging each word vector using an averaged vector method. For approximate similarity matching, the Annoy library was used to create the indices in tree sorting. Data were collected from 22 elderly and assessed by the Post-Study System Usability Questionnaire (PSSUQ). The study revealed that 72.73% of the respondents found the chatbot easy to learn and use, 63.64% of the respondents found the chatbot can autonomously determine the next course of action, and 59.09% of the respondents believed that troubleshooting guidelines were provided for overcoming errors. The accuracy of the chatbot providing a reasonable response is 56.20±13.99%. Most users were satisfied with the chatbot system. The proposed chatbot provided considerable essential insights into the development of assistance systems for the elderly during the coronavirus pandemic (COVID-19) and during the period of national disasters. The model can be expanded to other applications in the future.

Rethinking Conversation Styles of Chatbots from the Customer Perspective: Relationships between Conversation Styles of Chatbots, Chatbot Acceptance, and Perceived Tie Strength and Perceived Risk

Grounded in the Stereotype Content Model, Risk Perception Theory, Technology Acceptance Model, and Relational Embeddedness Theory, this research delves into the relationship between chatbot conversation styles, customer risk, and the mediating role of chatbot acceptance and tie strength in online shopping. A 2 (warm vs. cold) * 2 (competent vs. incompetent) between-subjects experiment is conducted on 320 participants and the results obtained from two-way ANOVA and PROCESS macro revealed that: (a) customer-perceived risk decreases with conversation warmth rather than conversation competence; (b) customer acceptance of chatbots improves with conversation competence rather than conversation warmth, while not acting as an intermediary factor between the conversation styles and customer-perceived risk; (c) customer perceived tie strength increases with both conversation warmth and conversation competence. The findings contribute to the existing literature about the impact of chatbot anthropomorphism on customer cognitive processes and offer executives insights into the design of customer-friendly chatbots.

Chatbot for Social Need Screening and Resource Sharing With Vulnerable Families: Iterative Design and Evaluation Study.

Health outcomes are significantly influenced by unmet social needs. Although screening for social needs has become common in health care settings, there is often poor linkage to resources after needs are identified. The structural barriers (eg, staffing, time, and space) to helping address social needs could be overcome by a technology-based solution. This study aims to present the design and evaluation of a chatbot, DAPHNE (Dialog-Based Assistant Platform for Healthcare and Needs Ecosystem), which screens for social needs and links patients and families to resources. This research used a three-stage study approach: (1) an end-user survey to understand unmet needs and perception toward chatbots, (2) iterative design with interdisciplinary stakeholder groups, and (3) a feasibility and usability assessment. In study 1, a web-based survey was conducted with low-income US resident households (n=201). Following that, in study 2, web-based sessions were held with an interdisciplinary group of stakeholders (n=10) using thematic and content analysis to inform the chatbot's design and development. Finally, in study 3, the assessment on feasibility and usability was completed via a mix of a web-based survey and focus group interviews following scenario-based usability testing with community health workers (family advocates; n=4) and social workers (n=9). We reported descriptive statistics and chi-square test results for the household survey. Content analysis and thematic analysis were used to analyze qualitative data. Usability score was descriptively reported. Among the survey participants, employed and younger individuals reported a higher likelihood of using a chatbot to address social needs, in contrast to the oldest age group. Regarding designing the chatbot, the stakeholders emphasized the importance of provider-technology collaboration, inclusive conversational design, and user education. The participants found that the chatbot's capabilities met expectations and that the chatbot was easy to use (System Usability Scale score=72/100). However, there were common concerns about the accuracy of suggested resources, electronic health record integration, and trust with a chatbot. Chatbots can provide personalized feedback for families to identify and meet social needs. Our study highlights the importance of user-centered iterative design and development of chatbots for social needs. Future research should examine the efficacy, cost-effectiveness, and scalability of chatbot interventions to address social needs.