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L E, Vannam, and Tien Hai PHUNG. "The Role of Moderators in Transitioning From GenAI Chatbot Customer Experience To Customer Satisfaction in Digital Marketing." International Journal of Social Science and Economic Research 09, no. 07 (2024): 2511–32. http://dx.doi.org/10.46609/ijsser.2024.v09i07.028.
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This study investigates the role of moderators in transitioning from Generative AI (GenAI) chatbot customer experience to customer satisfaction in digital marketing. As digital marketing continues to grow with technological advancements, GenAI chatbots such as ChatGPT, Copilot, and Gemini have become essential tools for enhancing customer engagement and providing personalized experiences. Despite extensive research on the technical aspects of chatbots, there is limited understanding of how perceived personalization, relevance, and usefulness of GenAI chatbots impact customer satisfaction significantly when moderated by variables like familiarity with technology and organization type. A conceptual model is developed, and data is collected through a survey of 346 consumers who interact with GenAI chatbots. The data is analyzed using moderated regression analysis to test the proposed hypotheses. The findings of this study will enhance the understanding of factors influencing customer experience and provide practical insights for businesses aiming to improve customer satisfaction through effective GenAI chatbot integration. This research contributes to the existing literature on digital marketing and offers actionable recommendations for customizing GenAI chatbots to meet the needs of different organizational types.
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Sarode, Prof Vaishali, Bhakti Joshi, Tejaswini Savakare, and Harshada Warule. "A Real Time Chatbot Using Python." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7385–89. http://dx.doi.org/10.22214/ijraset.2023.53453.
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Abstract: Real-time chatbots developed using Python have emerged as powerful tools for enhancing customer support, improving user experiences, and streamlining business processes. Leveraging Python's extensive libraries and frameworks, such as NLTK, Flask, and telebot, developers can build intelligent and scalable chatbot systems.This paper provides an overview of the key components and techniques involved in developing a real-time chatbot using Python. It explores the process of requirement gathering, use case definition, conversation flow design, and performance optimization. Integration with backend services, error handling, validation, and user experience (UX) design are also discussed. The utilization of natural language understanding (NLU) algorithms and techniques allows chatbots to interpret and comprehend user intents, providing accurate and context-aware responses. Integration with REST APIs, Flask, and telebot facilitates seamless communication and interaction between the chatbot and users. Furthermore, the paper highlights the importance of security, privacy, and ethical considerations in chatbot systems. It emphasizes the significance of continuous testing, feedback iteration, and user-centric design principles to refine the chatbot's performance and enhance the user experience. Looking ahead, future work in real-time chatbot development using Python includes advancements in natural language processing (NLP), personalized user experiences, multi-modal capabilities, and the integration of voice assistants. Ethical considerations and explainable AI techniques will also be critical for building trustworthy and responsible chatbot systems. In conclusion, real-time chatbots developed using Python offer immense potential for transforming customer support, automating processes, and delivering personalized and efficient services. With ongoing advancements in NLP, AI, and user interface design, the future of real-time chatbots holds exciting possibilities for enhanced user interactions and seamless automation.
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Wang, Kaicheng. "From ELIZA to ChatGPT: A brief history of chatbots and their evolution." Applied and Computational Engineering 39, no. 1 (2024): 57–62. http://dx.doi.org/10.54254/2755-2721/39/20230579.
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Over the years, chatbots have grown to be used in a variety of industries. From their humble beginnings to their current prominence, chatbots have come a long way. From the earliest chatbot ELIZA in the 1960s to todays popular Chatgpt, chatbot language models, codes, and databases have improved greatly with the advancement of artificial intelligence technology.This paper introduces the development of chatbots through literature review and theoretical analysis. It also analyzes and summarizes the advantages and challenges of chatbots according to the current status of chatbot applications and social needs. Personalized interaction will be an important development direction for chatbots, because providing personalized responses through user data analysis can provide users with a personalized experience, thus increasing user engagement and satisfaction.
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Chaves, Ana Paula, Jesse Egbert, Toby Hocking, Eck Doerry, and Marco Aurelio Gerosa. "Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots." ACM Transactions on Computer-Human Interaction 29, no. 2 (2022): 1–38. http://dx.doi.org/10.1145/3487193.
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Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.
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Pratiwie S, Nenda. "Chatbots in the Workplace: An Interpretive Phenomenological Study of Workers on the Use of ChatGPT." International Journal of Social Service and Research 3, no. 9 (2023): 2296–305. http://dx.doi.org/10.46799/ijssr.v3i9.537.
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The development of AI technology, such as advanced chatbots, provides many variations of assistance to humans. This research discusses the use of chatbots, particularly ChatGPT, in the workplace based on interviews with three informants. Using interpretive phenomenological methods, the study seeks to find out the experience of using ChatGPT to help complete work tasks. The researcher found that ChatGPT can personalize responses based on user prompts. The speed of providing feedback is the Media Richness Theory's dimension that dominates the results of this study. The superiority of ChatGPT is recognized in terms of its speed in providing feedback when quick resolution of work tasks is needed. However, the research also highlights the weakness of ChatGPT in terms of its limited knowledge storage. Further research is expected to explore experiences related to the use of Chatbot technology with other models to enhance understanding of communication technology industries based on AI.
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Made Natya Pradnya Iswari, Made Hery Santosa, and Luh Indrayani. "Transforming EFL Instruction: A Systematic Review of Chatbot Integration in English Language Learning." Jurnal Pendidikan Bahasa Inggris undiksha 12, no. 3 (2024): 302–9. https://doi.org/10.23887/jpbi.v12i3.92081.
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There are important areas that have not been fully investigated. The use of chatbots to improve students' language learning abilities presents various advantages and obstacles. This study explores the integration of Chatbots in English language instruction for EFL learners, focusing on their impact on learning implementation experiences, advantages, and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, the research reviewed articles from 2019 to 2024 to ensure a comprehensive analysis. A total of 998 publications were identified through Google Scholar, with search terms such as "chatbot," "AI chatbot," and "Chatbot implementation among EFL learners in English language learning." After an extensive screening process, including duplicate removal, inclusion, and exclusion criteria, 10 empirical, open-access studies were selected for review. The findings reveal that ChatGPT significantly enhances writing, grammar, and vocabulary skills by providing immediate and personalized feedback. It also fosters engagement and motivation while supporting cultural exploration and intercultural competence. However, limitations include reduced efficacy in developing speaking and listening skills, technical challenges, and ethical concerns. The study concludes that while ChatGPT is a valuable tool, it should complement, not replace, traditional teaching methods to ensure a balanced and effective learning experience.
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Younis, Muhanad T., Nadia Mahmood Hussien, Yasmin Makki Mohialden, Komeil Raisian, Prabhishek Singh, and Kapil Joshi. "Enhancement of ChatGPT using API Wrappers Techniques." Al-Mustansiriyah Journal of Science 34, no. 2 (2023): 82–86. http://dx.doi.org/10.23851/mjs.v34i2.1350.
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This study looks at how API (Application Programming Interface) wrapper technology can make it easier to use complex functions by putting together a lot of API calls. These packages have non-real-time interfaces that are hard to use. ChatGPT is a chatbot-specific GPT-3 language paradigm. It lets developers create chatbots that respond intelligently to natural language user input, creating a more engaging user experience. This article shows that ChatGPT, Python, and API wrapper technology may be used to develop a smart chatbot. We show how to use the OpenAI API library to add ChatGPT to Python programs. This makes it easier for developers to make chatbots that sound and act more like real people when they talk. Our contribution to this field is showing that it is possible to make smart chatbots with ChatGPT and API wrapper technology. To reach this goal, we use a system that combines the OpenAI API with ChatGPT and Python. This gives us valuable information about how to make smart chatbots. The efficiency of the system has been tested many times while applying it to different environments, and the results are satisfactory.
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Birenbaum, Menucha. "The Chatbots’ Challenge to Education: Disruption or Destruction?" Education Sciences 13, no. 7 (2023): 711. http://dx.doi.org/10.3390/educsci13070711.
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The article addresses the positive and negative implications of the growing spread of chatbots based on large language models (LLMs) on instruction, learning, and assessment in education. It is based on extensive conversations with ChatGPT regarding pedagogy-related issues and relevant documents. Discussed are the challenges of chatbots like ChatGPT to educators—on the one hand, their potential to advance deep learning and the roles of the instructor and the school context in causing it to happen. On the other hand, it underscores the pedagogical drawbacks of improper usage of such chatbots and the instructional practices and school contexts that could escalate learning. Three school-culture components, namely classroom learning, teacher professional learning, and school leadership, are the essential aspects of pedagogical approaches that, in a particular constellation, could enhance and, in another, impede a chatbot’s potential to advance deep learning. The underlying theoretical framework is assessment-driven, contrasting assessment for learning (AfL) and assessment for grading, distinguishing assessment cultures from testing cultures. Patterns of chatbot usage that align with the principles of each culture are discussed. A sample of quotes from the conversations with ChatGPT is presented to support the insights gained from the chatting experience and the conclusions drawn.
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Kim, Trudy S., Catherine T. Yu, Chandler Hinson, et al. "ChatGPT Virtual Assistant for Breast Reconstruction: Assessing Preferences for a Traditional Chatbot versus a Human AI VideoBot." Plastic and Reconstructive Surgery - Global Open 12, no. 10 (2024): e6202. http://dx.doi.org/10.1097/gox.0000000000006202.
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Background: Recent advancements in artificial intelligence (AI) have reshaped telehealth, with AI chatbots like Chat Generative Pretrained Transformer (ChatGPT) showing promise in various medical applications. ChatGPT is capable of offering basic patient education on procedures in plastic and reconstructive surgery (PRS), yet the preference between human AI VideoBots and traditional chatbots in plastic and reconstructive surgery remains unexplored. Methods: We developed a VideoBot by integrating ChatGPT with Synthesia, a human AI avatar video platform. The VideoBot was then integrated into Tolstoy to create an interactive experience that answered four of the most asked questions related to breast reconstruction. We used Zapier to develop a ChatGPT-integrated chatbot. A 16-item survey adapted from the 2005 validated measurement of online trust by Corritore et al was distributed online to female participants via Amazon Mechanical Turk. Results: A total of 396 responses were gathered. Participants were 18 to 64 years old. Perceptions of truthfulness, believability, content expertise, ease of use, and safety were similar between the VideoBot and chatbot. Most participants preferred the VideoBot compared with the traditional chatbot (63.5% versus 28.1%), as they found it more captivating than the text-based chatbot. Of the participants, 77% would have preferred to see someone who they identified with in terms of gender and race. Conclusions: Both the VideoBot and text-based chatbot show comparable effectiveness, usability, and trust. Nonetheless, the VideoBot’s human-like qualities enhance interactivity. Future research should explore the impact of race and gender concordance in telehealth to provide a more personalized experience for patients.
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Cuddapah Anitha. "Applications of Natural Language Processing in Chatbot." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 1099–106. https://doi.org/10.52783/jisem.v10i33s.5936.
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NLP has revolutionized chatbots into essential instruments for both enterprises and people. NLP improves the chatbot's capacity to emulate human-like interactions by utilizing techniques such as intent identification, sentiment analysis, and contextual comprehension. Natural Language Processing (NLP) has transformed chatbot interactions by allowing them to efficiently process, comprehend, and produce human language. This study examines many uses of natural language processing in chatbots, encompassing customer service and healthcare. It analyzes the technological progress in NLP that has improved chatbot capabilities and user experience. The study additionally delineates prevalent problems and constraints linked to NLP methodologies in chatbot systems. The study's findings underscore the enhancements in efficiency, cost-effectiveness, and customer pleasure attributable to NLP-powered chatbots. The study finishes with thoughts into future trends and prospective breakthroughs in NLP technology for conversational AI.
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Satiti, Laras Hayu, Endang Fauziati, and Endang Seytaningsih. "AI Chatbot as an Effective English Teaching Partner for University Students." International Journal of Educational Research & Social Sciences 5, no. 3 (2024): 463–69. https://doi.org/10.51601/ijersc.v5i3.820.
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This study investigated the effectiveness of an AI chatbot for improving English language skills among Indonesian university students. In order to address the research question, the researchers conducted an analysis of responses to a 20-item questionnaire distributed via Google Form among a sample of 95 university students. The survey was conducted to evaluate student perceptions of the chatbot's usability, language accuracy, feedback quality, and overall strengths. The findings revealed high student satisfaction with all aspects of the chatbot. Students agreed that the chatbot was user-friendly, understood their questions, provided helpful feedback, and offered valuable learning opportunities. These results align with previous research on the effectiveness of chatbots in enhancing English language proficiency. The study suggests that AI chatbots can be a beneficial tool for both independent language practice and supplementing classroom instruction. The chatbot's ability to personalise learning experiences and adapt to individual needs contributes to improved student engagement and language acquisition. This research adds to the growing body of evidence supporting the use of AI chatbots in computer-assisted language learning (CALL). Future research could investigate the effectiveness of the chatbot for learners with varying proficiency levels.
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Santoso, Gerald, Johan Setiawan, and Agus Sulaiman. "Development of OpenAI API Based Chatbot to Improve User Interaction on the JBMS Website." G-Tech: Jurnal Teknologi Terapan 7, no. 4 (2023): 1606–15. http://dx.doi.org/10.33379/gtech.v7i4.3301.
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This study presents an innovative chatbot, powered by OpenAI API, designed to enhance the user experience on the Journal of Business, Management, and Social Studies (JBMS) website. Chatbots have gained prominence for improving online interactions and information retrieval. The chatbot's development followed a structured prototype methodology, including Requirement Gathering, Prototype Building, Requirement Refinement, Customer Evaluation, and Design and Implementation. User Acceptance Testing (UAT) scored an average of 4.14, signifying high user satisfaction. UAT results showed positive user experiences and satisfaction with the chatbot. Integration of OpenAI API improved information extraction from journal articles and personalized article recommendations. Stakeholder feedback from JBMS's CEO, students, and UMN lecturers affirmed high satisfaction levels. Future research will refine the chatbot's features to align better with user needs, solidifying its role as an innovative tool for information retrieval within JBMS, and enhancing user service.
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Panda, Subhajit, and Navkiran Kaur. "Exploring the viability of ChatGPT as an alternative to traditional chatbot systems in library and information centers." Library Hi Tech News 40, no. 3 (2023): 22–25. https://doi.org/10.1108/LHTN-02-2023-0032.
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Purpose The purpose of this study is to explore the feasibility of using ChatGPT-based chatbot systems as an alternative to traditional knowledge base-based chatbot systems in library and information centers (LICs). Design/methodology/approach The study used a qualitative research approach to examine the viability of ChatGPT-based chatbot systems in LICs. The researchers conducted an extensive literature review and analyzed data collected from interviews with experts in the field of library and information science. Findings The findings of the study reveal that ChatGPT-based chatbot systems are a viable alternative to traditional knowledge base-based chatbot systems in LICs. ChatGPT has the potential to provide more accurate and personalized responses to user queries, improve the user experience and reduce the workload of library staff. However, there are some limitations to the use of ChatGPT in LICs, such as the need for substantial training data and the risk of perpetuating biases. Originality/value To the best of the authors’ knowledge, this study is one of the first to explore the potential of ChatGPT-based chatbot systems in the context of LICs. The research provides insights into the advantages and limitations of ChatGPT-based chatbots and highlights the possibilities for future use of this technology in the information service industry.
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Dinh, Hoa, and Thien Khai Tran. "EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach." Applied Sciences 13, no. 22 (2023): 12446. http://dx.doi.org/10.3390/app132212446.
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The digital transformation has created an environment that fosters the development of effective chatbots. Through the fusion of artificial intelligence and data, these chatbots have the capability to provide automated services, optimize customer experiences, and reduce workloads for employees. These chatbots can offer 24/7 support, answer questions, perform transactions, and provide rapid information, contributing significantly to the sustainable development processes of businesses and organizations. ChatGPT has already been applied in various fields. However, to ensure that there is a chatbot providing accurate and useful information in a narrow domain, it is necessary to build, train, and fine-tune the model based on specific data. In this paper, we introduce EduChat, a chatbot system for university-related questions. EduChat is an effective artificial intelligence application designed by combining rule-based methods, an innovative improved random forest machine learning approach, and ChatGPT to automatically answer common questions related to universities, academic programs, admission procedures, student life, and other related topics. This chatbot system helps provide quick and easy information to users, thereby reducing the time spent searching for information directly from source documents or contacting support staff. The experiments have yielded positive results.
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Lee, Chaewook, and Namin Shin. "A Case Study on Middle School Students' Learning Experience in Free English Conversation with Generative AI Chatbots." Korean Association For Learner-Centered Curriculum And Instruction 25, no. 12 (2025): 1–19. https://doi.org/10.22251/jlcci.2025.25.12.1.
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Objectives This study explored the pedagogical potential of generative, voice-enabled, conversational, and goal-oriented chatbots for English teaching and learning by analyzing the free conversation experiences between a generative AI chatbot and middle school students. Methods Video-recorded free conversations between a generative English-speaking chatbot developed by Company Y and three middle school students were transcribed and analyzed. Based on the research questions, both qualitative and quantitative analyses were conducted, including text analysis, readability assessment, lexical analysis, token count, and sentiment analysis. Results The findings indicated that the generative AI-based chatbot outperformed traditional rule-based chat bots in terms of encouraging active verbal output, providing personalized conversations, offering adaptive sup port, facilitating emotional interactions, and enhancing learner motivation. Quantitative analysis particularly sup ported the chatbot’s effectiveness in personalized conversation and emotional interaction. Conclusions The results suggest that generative AI-powered chatbots can serve as valuable tools to supplement one-on-one tutoring in English speaking instruction.
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Subramaniam, Nantha Kumar. "Enabling Learning of Programming through Educational Chatbot." International Journal of Research and Innovation in Applied Science IX, no. XII (2025): 470–77. https://doi.org/10.51584/ijrias.2024.912042.
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This research investigates the integration of AI-powered chatbots as transformative tools to enhance the teaching and learning of programming, specifically targeting Java programming in an open and distance learning (ODL) environment. The chatbot was conceptualized and developed using principles from constructivist, problem-based, and active learning theories to support a learner-centric approach. Designed to offer multimodal content, real-time feedback, and interactive problem-solving exercises, the chatbot aims to reduce cognitive load and facilitate a deeper understanding of complex programming concepts. The implementation involved deploying the chatbot in an online Java programming course over one semester, engaging 32 ODL learners in a blended learning mode. Learners utilized the chatbot for interactive, one-on-one tutoring sessions, benefiting from multimodal materials such as animations, visualizations, and interactive exercises. These resources supported learners in addressing challenging concepts and completing programming tasks, enhancing their engagement and comprehension. Evaluation was conducted through a structured survey using a 4-point Likert scale, capturing learner perceptions on the chatbot’s effectiveness. The findings revealed high levels of satisfaction, with learners highlighting the chatbot’s ability to provide learning support and its unique learning experience. The survey further underscored the chatbot’s role in alleviating cognitive load and bridging the gap between abstract programming theories and their practical applications. The study demonstrates the potential of AI-powered chatbots to transform programming education.
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Meshram, Anurag, Amol Patil, Saurabh Mulik, Aniruddh Pawar, and Prof Sheetal Nirve. "Text Generation Model Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 23–25. https://doi.org/10.22214/ijraset.2025.66190.
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Abstract: The rapid growth of artificial intelligence (AI) and natural language processing (NLP) has given rise to conversational AI solutions known as chatbots. This project aims to explore and develop a chatbot using the OpenAI API. The chatbot is designed to offer users an interactive, responsive, and context-aware conversation experience. This report presents a detailed overview of the development process, design considerations, implementation, evaluation, and potential improvements. Chatbots have gained prominence for improving online interactions and information retrieval. The chatbot's development followed a structured prototype methodology, including Requirement Gathering, Prototype Building, Requirement Refinement, Customer Evaluation, and Design and Implementation. User Acceptance Testing (UAT) scored an average of 4.14, signifying high user satisfaction. This work also delves into the ethical considerations and real- world applications of AI chatbots.
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Joshi, Kalpesh. "AI Mental Health Therapist Chatbot." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 308–11. http://dx.doi.org/10.22214/ijraset.2023.56393.
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Chatbots have become very popular these days as the technology is growing with a very high rate. Due to advancements in the technology chatbots have made our lives easier as we can get to know about many things at our finger tips. So, there are many chatbots available which do the work related to particular things. One such chatbot is ChatGPT, Bard etc. AI chatbots provide a more human like experience with the help of natural language processing and leverage semantics to understand the context of what a person says. Thinking of it we have created a AI Mental Health Therapist Chatbot to provide a medical recommendations according to the problem the user might be facing. It will be able to provide medical support in minimal cost and also recommend the treatment required to the user. This can be a type of advancement in the field of AI which can gain popularity among people. The best AI chatbots can unlock incredible efficiency and also the breadth of AI chatbots available today is incredible.
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Kar, Parijat, and Ridhima Kar. "Convergence of Chatbot Personalities Using Reinforcement Learning and Text Generation." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 327–33. https://doi.org/10.1609/aaaiss.v5i1.35608.
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Chatbots have become indispensable tools in customer support and mental health industries, facilitating efficient communication and problem-solving. However, the success of these interactions often hinges on the alignment between the chatbot's personality and the user's preferences. This study investigates the significance of personality matching in chatbot conversations, exploring the impact of MBTI personality types on user engagement and satisfaction. We delve into various strategies for adopting appropriate tone and persona in one-to-one and group chatbot interactions. By understanding the nuances of personality and tailoring chatbot responses accordingly, we aim to optimize the effectiveness of these virtual agents and enhance the overall user experience.
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Journal, IJSREM. "CUSTOMER SUPPORT CHATBOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem27984.
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Abstract—The food business has seen a dramatic transition in recent years toward digital platforms as more and more customers choose to order food through online channels. Businesses are using creative solutions, like customer care chatbots, to handle the growing demand and expedite the purchase process. The creation and application of a customer support chatbot designed especially for food orders is the main goal of this study. This study's main goals are to integrate a sophisticated chatbot system into the meal ordering process to improve operational efficiency and the overall user experience. The chatbot's purpose is to converse with consumers in natural language, providing a smooth, customized experience that resembles a human conversation. By utilizing sophisticated natural language processing (NLP), The chatbot's objectives are to handle orders, comprehend customer preferences, and deliver pertinent information. Keywords—Ease of user experience, Enhanced customer service, Voice recognition, user feedback, Time efficiency.
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Sari, Hasrini, and Rachmasari Adinda. "Examining Customer Experience in Using a Chatbot." International Journal of Asian Business and Information Management 14, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijabim.322438.
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While the use of artificial intelligence and chatbots is increasing, studies of customer experiences in using chatbots remain rare. This article explores the experience of customers before, during, and after their interaction with a chatbot to determine what aspects draw their attention when interacting with the chatbot. It identifies potential problems customers face when using a chatbot from different points of view, specifically those of a chatbot developer, a chatbot buyer, and a customer. This study focuses on VX, a chatbot from a leading telecommunications company in Indonesia, and uses interviews of fourteen customers, identified personas, and individual customer journeys to analyze interactions. The personas revealed five types of needs: two basic ones for information and transactions, and three others for security, entertainment, and complaints.
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Buraphadeja, Vasa, and Donyawan Chantokul. "Supporting Student Engagement and Academic Guidance through a Chatbot in Blended Learning." International Journal on E-Learning 24, no. 3 (2024): 241–53. https://doi.org/10.70725/031289kgvqkw.
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This study examines the effectiveness of a custom-designed chatbot in a blended learning environment, focusing on its adherence to instructional guidelines and its impact on student engagement. The chatbot was designed to prompt students with questions about concepts related to various academic majors, encourage elaboration on their responses, and provide tailored recommendations based on students' strengths. Results indicate that while the chatbot effectively engaged students in discussions, as students who received recommendations were likely to follow up on their topics of interest, it struggled to cover all topics of academic majors equally. The chatbot’s efforts to encourage more detailed responses occasionally led to disengagement, with some students halting midway. These findings contribute to the growing research on chatbots as practical tools in real-world educational settings. Future research should focus on streamlining chatbot tasks, integrating student preferences to enhance engagement, and designing cohesive learning experiences that connect in-class and online interactions to improve the chatbot’s effectiveness in academic advising and career guidance.
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Wiliam, Andi, Sasmoko Sasmoko, Harjanto Prabowo, Mohammad Hamsal, Elfindah Princes, and Yasinta Indrianti. "ANALYSIS OF E-SERVICE CHATBOT AND SATISFACTION OF BANKING CUSTOMERS IN INDONESIA." Asia Proceedings of Social Sciences 4, no. 3 (2019): 72–75. http://dx.doi.org/10.31580/apss.v4i3.860.
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This research was conducted to qualitatively analyze the actual opportunities of banking e-service chatbot to satisfy Indonesian banking customers and also to explore qualitatively what factors were expected to affect customer satisfaction in its implementation through the experience review of other companies as users of e-service chatbots. In this study, the authors used literature studies on the experiences of various companies when using e-service chatbots to satisfy their customers, which then contextualized into the banking industries in Indonesia. The results of the study found that the e-service chatbots had the opportunity to increase the satisfaction of banking customers in Indonesia. This opportunity occured when the customer had fully experienced digital literacy and they experienced the readiness to change a stage of banking services. The results of the study also found that the opportunities for the Indonesian banking industry that have been fully implemented since the beginning of their governance were arranged electronically, so that their customers experienced good readiness which ultimately resulted in good customer satisfaction. The implication of this research is the importance of banking governance to seriously carry out digital transformation as an excellent service through chatbots.
 
 Keywords: Chatbot, E-Service, E-Satisfactions, E-Retention
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Kayalı, Bünyami, Mehmet Yavuz, Şener Balat, and Mücahit Çalışan. "Investigation of student experiences with ChatGPT-supported online learning applications in higher education." Australasian Journal of Educational Technology 39, no. 5 (2023): 20–39. http://dx.doi.org/10.14742/ajet.8915.
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The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot’s functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use. Implications for practice or policy Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities. ChatGPT can promote and enhance students' personalised learning experiences. ChatGPT can be used in all higher education courses. Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT. Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.
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Harbola, Aditya. "Design and Implementation of an AI Chatbot for Customer Service." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1295–303. http://dx.doi.org/10.17762/msea.v70i2.2321.
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 A company's customer service is very important to its success. It can help boost revenue and retain customers. As digital technology has increased the demand for 24-hour support, businesses are now turning to AI chatbots to provide better and more personalized service. Artificial intelligence (AI) chatbots can help businesses improve the customer experience and reduce the workload of their customer service agents. The paper presents the development and implementation of a chatbot utilizing NLP and AI techniques. It aims to provide efficient and personalized responses to customers' inquiries. The research process involved gathering and analyzing data, developing the chatbot's framework, and carrying out the study. Its architecture and framework were built with the help of NLP and AI. This feature allows the chatbot to respond to users' natural language queries. Its features were also designed to help customers navigate through various tasks and provide recommendations. The chatbot was well-received by its users and was able to provide effective and efficient customer service. The findings of the study indicate that the potential of AI and NLP in enhancing the experience of customers is immense.
 
 
 
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Nicolescu, Luminița, and Monica Teodora Tudorache. "Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review." Electronics 11, no. 10 (2022): 1579. http://dx.doi.org/10.3390/electronics11101579.
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Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.
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Vujko, Aleksandra, Zoran Papović, and Drago Cvijanović. "Artificial intelligence and its influence on sustainable travel agencies business: The role of chatbots in creating tourist arrangements." Ekonomika 71, no. 1 (2025): 1–13. https://doi.org/10.5937/ekonomika2501001v.
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Among the innovations that have garnered significant attention is the use of chatbots, which serve as automated agents designed to facilitate communication and streamline processes for both travel agencies and their clientele. This paper will explore how chatbots contribute to the sustainability of agency businesses by enhancing operational efficiency, improving customer experience, and embodying modernity and innovation. The research included 243 travelers who used ChatGPT for their trips, and factor analysis and Structural Equation Modeling (SEM) were used to analyze the data. The result was that chatbot users gather their positive experiences around three key factors: Simplicity of travel, Frequency of travel and Organization of travel, which confirmed the main hypothesis H that the deployment of chatbots not only aligns agency businesses with contemporary trends but also significantly enhances their operational efficiency, customer satisfaction, and overall market presence. One of the main limiting factors for the application of chatbots is the low prevalence of software that has such functions. The adoption of chatbots serves as a clear indicator of modernity and innovation within agency businesses, positioning them as forward-thinking entities in an ever-evolving market.
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Holland, Alexis M., William R. Lorenz, Jack C. Cavanagh, et al. "Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models." JAMA Network Open 7, no. 8 (2024): e2425373. http://dx.doi.org/10.1001/jamanetworkopen.2024.25373.
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ImportanceArtificial intelligence (AI) has permeated academia, especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model. However, little has been reported on its use in medical research.ObjectiveTo assess a chatbot’s capability to generate and grade medical research abstracts.Design, Setting, and ParticipantsIn this cross-sectional study, ChatGPT versions 3.5 and 4.0 (referred to as chatbot 1 and chatbot 2) were coached to generate 10 abstracts by providing background literature, prompts, analyzed data for each topic, and 10 previously presented, unassociated abstracts to serve as models. The study was conducted between August 2023 and February 2024 (including data analysis).ExposureAbstract versions utilizing the same topic and data were written by a surgical trainee or a senior physician or generated by chatbot 1 and chatbot 2 for comparison. The 10 training abstracts were written by 8 surgical residents or fellows, edited by the same senior surgeon, at a high-volume hospital in the Southeastern US with an emphasis on outcomes-based research. Abstract comparison was then based on 10 abstracts written by 5 surgical trainees within the first 6 months of their research year, edited by the same senior author.Main Outcomes and MeasuresThe primary outcome measurements were the abstract grades using 10- and 20-point scales and ranks (first to fourth). Abstract versions by chatbot 1, chatbot 2, junior residents, and the senior author were compared and judged by blinded surgeon-reviewers as well as both chatbot models. Five academic attending surgeons from Denmark, the UK, and the US, with extensive experience in surgical organizations, research, and abstract evaluation served as reviewers.ResultsSurgeon-reviewers were unable to differentiate between abstract versions. Each reviewer ranked an AI-generated version first at least once. Abstracts demonstrated no difference in their median (IQR) 10-point scores (resident, 7.0 [6.0-8.0]; senior author, 7.0 [6.0-8.0]; chatbot 1, 7.0 [6.0-8.0]; chatbot 2, 7.0 [6.0-8.0]; P = .61), 20-point scores (resident, 14.0 [12.0-7.0]; senior author, 15.0 [13.0-17.0]; chatbot 1, 14.0 [12.0-16.0]; chatbot 2, 14.0 [13.0-16.0]; P = .50), or rank (resident, 3.0 [1.0-4.0]; senior author, 2.0 [1.0-4.0]; chatbot 1, 3.0 [2.0-4.0]; chatbot 2, 2.0 [1.0-3.0]; P = .14). The abstract grades given by chatbot 1 were comparable to the surgeon-reviewers’ grades. However, chatbot 2 graded more favorably than the surgeon-reviewers and chatbot 1. Median (IQR) chatbot 2-reviewer grades were higher than surgeon-reviewer grades of all 4 abstract versions (resident, 14.0 [12.0-17.0] vs 16.9 [16.0-17.5]; P = .02; senior author, 15.0 [13.0-17.0] vs 17.0 [16.5-18.0]; P = .03; chatbot 1, 14.0 [12.0-16.0] vs 17.8 [17.5-18.5]; P = .002; chatbot 2, 14.0 [13.0-16.0] vs 16.8 [14.5-18.0]; P = .04). When comparing the grades of the 2 chatbots, chatbot 2 gave higher median (IQR) grades for abstracts than chatbot 1 (resident, 14.0 [13.0-15.0] vs 16.9 [16.0-17.5]; P = .003; senior author, 13.5 [13.0-15.5] vs 17.0 [16.5-18.0]; P = .004; chatbot 1, 14.5 [13.0-15.0] vs 17.8 [17.5-18.5]; P = .003; chatbot 2, 14.0 [13.0-15.0] vs 16.8 [14.5-18.0]; P = .01).Conclusions and RelevanceIn this cross-sectional study, trained chatbots generated convincing medical abstracts, undifferentiable from resident or senior author drafts. Chatbot 1 graded abstracts similarly to surgeon-reviewers, while chatbot 2 was less stringent. These findings may assist surgeon-scientists in successfully implementing AI in medical research.
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Seraquive, Jomar Elizabeth Guzman, Patricio Álvarez-Muñoz, Kerly Palacios-Zamora, Dennis Alfredo Peralta Gamboa, and Marco Faytong-Haro. "Unveiling the core constructs: A statistical approach to evaluating user experience with Chatbots in higher education (A case study from a university in Ecuador)." Journal of Infrastructure, Policy and Development 8, no. 10 (2024): 6381. http://dx.doi.org/10.24294/jipd.v8i10.6381.
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Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.
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Zhang, Jingwen, Yoo Jung Oh, Patrick Lange, Zhou Yu, and Yoshimi Fukuoka. "Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint." Journal of Medical Internet Research 22, no. 9 (2020): e22845. http://dx.doi.org/10.2196/22845.
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Background Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. Objective The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. Methods We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Results Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. Conclusions As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.
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Ali, Shahmir H., Fardin Rahman, Aakanksha Kuwar, et al. "Rapid, Tailored Dietary and Health Education Through A Social Media Chatbot Microintervention: Development and Usability Study With Practical Recommendations." JMIR Formative Research 8 (December 9, 2024): e52032. https://doi.org/10.2196/52032.
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Background There is an urgent need to innovate methods of health education, which can often be resource- and time-intensive. Microinterventions have shown promise as a platform for rapid, tailored resource dissemination yet have been underexplored as a method of standardized health or dietary education; social media chatbots display unique potential as a modality for accessible, efficient, and affordable educational microinterventions. Objective This study aims to provide public health professionals with practical recommendations on the use of social media chatbots for health education by (1) documenting the development of a novel social media chatbot intervention aimed at improving dietary attitudes and self-efficacy among South Asian American young adults and (2) describing the applied experiences of implementing the chatbot, along with user experience and engagement data. Methods In 2023, the “Roti” chatbot was developed on Facebook and Instagram to administer a 4-lesson tailored dietary health curriculum, informed by formative research and the Theory of Planned Behavior, to 18- to 29-year-old South Asian American participants (recruited through social media from across the United States). Each lesson (10-15 minutes) consisted of 40-50 prescripted interactive texts with the chatbot (including multiple-choice and open-response questions). A preintervention survey determined which lesson(s) were suggested to participants based on their unique needs, followed by a postintervention survey informed by the Theory of Planned Behavior to assess changes in attitudes, self-efficacy, and user experiences (User Experience Questionnaire). This study uses a cross-sectional design to examine postintervention user experiences, engagement, challenges encountered, and solutions developed during the chatbot implementation. Results Data from 168 participants of the intervention (n=92, 54.8% Facebook; n=76, 45.2% Instagram) were analyzed (mean age 24.5, SD 3.1 years; n=129, 76.8% female). Participants completed an average of 2.6 lessons (13.9 minutes per lesson) and answered an average of 75% of questions asked by the chatbot. Most reported a positive chatbot experience (User Experience Questionnaire: 1.34; 81/116, 69.8% positive), with pragmatic quality (ease of use) being higher than hedonic quality (how interesting it felt; 88/116, 75.9% vs 64/116, 55.2% positive evaluation); younger participants reported greater hedonic quality (P=.04). On a scale out of 10 (highest agreement), participants reported that the chatbot was relevant (8.53), that they learned something new (8.24), and that the chatbot was helpful (8.28). Qualitative data revealed an appreciation for the cheerful, interactive messaging of the chatbot and outlined areas of improvement for the length, timing, and scope of text content. Quick replies, checkpoints, online forums, and self-administered troubleshooting were some solutions developed to meet the challenges experienced. Conclusions The implementation of a standardized, tailored health education curriculum through an interactive social media chatbot displayed strong feasibility. Lessons learned from challenges encountered and user input provide a tangible roadmap for future exploration of such chatbots for accessible, engaging health interventions.
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Long, Ju, Juntao Yuan, and Hsun-Ming Lee. "How to Program a Chatbot – An Introductory Project and Student Perceptions." Issues in Informing Science and Information Technology 16 (2019): 001–31. http://dx.doi.org/10.28945/4282.
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Aim/Purpose: One of the most fascinating developments in computer user interfaces in recent years is the rise of “chatbots”. Yet extent information system (IS) curriculum lacks teaching resources on chatbots programming.. Background: To better prepare students for this new technological development and to enhance the IS curriculum, we introduce a project that teaches students how to program simple chatbots, including a transactional chatbot and a conversational chatbot. Methodology: We demonstrated a project that teaches students how to program two types of simple chatbots: a transactional chatbot and a conversational chatbot. We also conducted a survey to examine students’ perceptions on their learning experience. Findings: Our survey on students’ perception of the project finds that learning chatbots is deemed very useful because chatbot programming projects have enabled the students to understand the subject better. We also found that social influence has positively motivated the students to learn chatbot programming. Though most of the students have no prior experiences programming chatbots, their self-efficacy towards chatbot programming remained high after working through the programming project. Despite the difficult tasks, over 71% of respondents agree to various degrees that chatbot programming is fun. Though most students agree that chatbot programming is not easy to learn, more than 70% of respondents indicated that they will use or learn chatbots in the near future. The overwhelmingly positive responses are impressive given that this is the first time for the students to program and learn chatbots. Recommendations for Practitioners: In this article, we introduced a step by step project on teaching chatbot programming in an information systems class. Following the project instructions, students can get their first intelligent chatbots up and running in a few hours using Slack. This article describes the project in detail as well as students’ perceptions. Recommendations for Researchers: We used UTAUT model to measure students’ perception of the projects. This study could be of value to researchers studying students’ technology learning and adoption behaviors. Impact on Society: To our best knowledge, pedagogical resources that teach IS students how to program chatbots, especially the introductory level materials, are limited. We hope this teaching case could be of value for IS educators when introducing IS students to the wonderful field of chatbot programming. Future Research: For future work, we plan to expand the teaching resources to cover more advanced chatbot programming projects, such as on how to make chatbot more human-like.
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Amiot, Clélie, François Charoy, and Jérôme Dinet. "Chatbots in Collaborative Settings and their Impact on Virtual Teamwork." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1–18. https://doi.org/10.1145/3710945.
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Chatbots have emerged as a powerful tool for collaborative teamwork. In this paper, we investigate the influence of chatbots on virtual teamwork within the context of a collaborative online activity. To assess chatbots' impact on group dynamics and performance, we designed a novel collaborative activity with an associated online platform and a custom chatbot assistant. We recruited 72 participants divided into four-person teams, with the teams split into four conditions depending on the chatbot's assistance strategy (none, by private chat, by group chat, or both). We found that chatbot assistance improved participants' performance and reduced response times to information requests during the collaborative activity. This improvement was more pronounced for teams in the group and both chats conditions, while participants in the private chat condition reported significantly higher perceived effort. Furthermore, we observed a significant correlation between the perceived quality of the chatbot's communication skills and the cognitive workload and time pressure experienced by the participants. Our findings contribute to the field of Computer-Supported Cooperative Work by proposing a new design of experimental activity to study virtual collaboration, providing insights that can inform the design of future collaboration tools and adding to our broader understanding of human-AI synergy in group settings.
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Sahu, Sneha. "The Role of AI Chatbots in Enhancing Online Customer Experiences." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04280.
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Abstract This research paper explores the transformative role of AI-powered chatbots in reshaping online customer experiences. With the rise of e-commerce and digital services, businesses are increasingly adopting AI chatbots to offer instant support, personalized interactions, and round-the-clock availability. The study investigates how chatbot integration improves customer satisfaction, operational efficiency, and brand engagement. Using both primary and secondary data, the research analyzes key success factors, challenges, and future directions in chatbot technology. The findings suggest that AI chatbots are not merely tools for automation but strategic assets for delivering seamless, human-like customer service in the digital age. Keywords: AI Chatbots, Customer Experience, Customer Satisfaction, Digital Transformation, Customer Support, Natural Language Processing (NLP), Automation, E-commerce
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Mishchenko, E. V., M. I. Yarantseva, D. N. Verzhykovskyi, and S. A. Ghazaryan. "Methods for assessing the economic efficiency of implementing AI–based chatbots for customer experience management." Entrepreneur’s Guide 18, no. 2 (2025): 22–31. https://doi.org/10.24182/2073-9885-2025-18-2-22-31.
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This study focuses on identifying the place, role, and specific features of enhancing the economic efficiency of implementing and using chatbots in customer experience management. The purpose of the study is to summarize the theoretical aspects of chatbot use in managing customer experience and to analyze the methods for assessing the economic efficiency of investments in such technologies. The paper presents the concept and characteristics of chatbots and clarifies the variety of chatbot types available to businesses, highlighting the relevance of developing efficiency assessment methods. The strengths and weaknesses of chatbot use, along with opportunities and threats in customer experience management, are identified. The specific features of AI–based chatbots in customer experience management are formulated. The functional differences and the need for chatbot intellectualization are emphasized. Various methodologies for evaluating the effectiveness of chatbot implementation are summarized and compared. The paper conceptually justifies the necessity of considering not only economic efficiency but also the functional and goal–oriented alignment of the technology, which broadens the understanding of comparative analysis approaches for different chatbot configurations and other digital marketing tools.
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Saah, Paul, Charles Mbohwa, and Nelson Sizwe Madonsela. "How consumers' attitude towards chatbots affects their experience and behavioural intentions: The case of South Africa." Asian Development Policy Review 12, no. 1 (2024): 26–38. http://dx.doi.org/10.55493/5008.v12i1.4960.
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The purpose of this study is to determine how consumers' attitudes towards chatbots affect their experiences and behavioural intentions. In the contemporary business environment, a lot of enterprises and outstanding brands have resorted to using modern technology in their business operations, such as chatbots, to offer 24-hour customer care. The quantitative research approach was adopted for this study with a correlational survey model to collect and analyse numerical data to ascertain how consumers' attitudes towards chatbots affect their experiences and behavioural intentions. The population of this study comprises customers of renowned brands in South Africa. The sample size was 211 consumers with at least five years’ experience of using chatbots in South Africa who were selected using a nonprobability convenience sampling method. The Statistical Package for Social Sciences (SPSS) and SmartPLS3 were used to analyse the data. The study's findings show a strong predictive correlation between customer contacts and behavioural intent towards chatbots and perceived usefulness, ease of use, enjoyment, and seeming risk. The significance of these aspects in influencing customer attitudes and behaviours while interacting with chatbot technologies is highlighted by these findings. Understanding and optimizing perceived usefulness, ease of use, fun, and apparent risk in chatbot design is crucial for shaping positive consumer interactions and influencing behavioral intent towards chatbots. Therefore, it is highly recommended that renowned brands and companies looking to optimise their chatbot systems endeavour to improve customer experiences and encourage long-term behavioural intentions among South African users.
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Reddy, Mr P. Rajashekar. "Enhancing User Experience: A Study on Dialogflow Chatbot Implementation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29070.
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In recent years, chatbots have become essential for customer service, task automation, and information dissemination. Google's Dialogflow platform offers intuitive tools for building conversational agents. This paper provides an overview of developing smart chatbots with Dialogflow, emphasizing natural language understanding. It covers Dialogflow's architecture, including intents, entities, and fulfillment, and discusses training techniques for chatbot optimization. Additionally, it explores advanced features like external service integration and multilingual support. Overall, it serves as a practical guide for developers and organizations interested in leveraging Dialogflow to create personalized chatbot experiences. Keywords : User input, Conversation flow, Dialog management, User experience (UX)
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Minić, Siniša, and Nemanja Deretić. "Application of artificial intelligence in education: Chatbot ChatGPT." Obrazovanje i vaspitanje 19, no. 22 (2024): 61–72. https://doi.org/10.5937/obrvas19-54678.
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The rapid advancement of artificial intelligence (AI) and natural language processing has led to the development of increasingly sophisticated and versatile language models. AI models are capable of generating new data based on patterns and structures learned from existing data, and they can create multimedia content. These generative AI models rely on unsupervised machine learning techniques and neural networks to analyze, understand, and generate content that closely resembles human-generated output. Among these, ChatGPT, an AI chatbot developed by OpenAI, has emerged as a powerful tool with diverse applications across various fields. In recent years, the scientific and academic communities have devoted significant attention to researching and developing the ChatGPT chatbot. As of November 2024, over 212,000 articles have been published on ChatGPT across national and international journals and conferences, according to Google Scholar. The growing development of the ChatGPT chatbot is poised to significantly reshape the education sector and raises concerns within the academic community regarding how future generations should be taught. For instance, this technology can be used to efficiently write articles or essays in seconds, potentially reducing the need for human involvement. This paper provides a literature review on the potential applications of the ChatGPT chatbot in education, as well as the key challenges and risks associated with its use. The integration of chatbots like ChatGPT and other AI models in education offers numerous opportunities to enhance the learning experience for students and support teachers. However, to fully harness their educational potential, it is crucial to approach the use of these models with caution, critically assessing their limitations and potential biases.
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He, Linwei, Erkan Basar, Emiel Krahmer, Reinout Wiers, and Marjolijn Antheunis. "Effectiveness and User Experience of a Smoking Cessation Chatbot: Mixed Methods Study Comparing Motivational Interviewing and Confrontational Counseling." Journal of Medical Internet Research 26 (August 6, 2024): e53134. http://dx.doi.org/10.2196/53134.
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Background Cigarette smoking poses a major public health risk. Chatbots may serve as an accessible and useful tool to promote cessation due to their high accessibility and potential in facilitating long-term personalized interactions. To increase effectiveness and acceptability, there remains a need to identify and evaluate counseling strategies for these chatbots, an aspect that has not been comprehensively addressed in previous research. Objective This study aims to identify effective counseling strategies for such chatbots to support smoking cessation. In addition, we sought to gain insights into smokers’ expectations of and experiences with the chatbot. Methods This mixed methods study incorporated a web-based experiment and semistructured interviews. Smokers (N=229) interacted with either a motivational interviewing (MI)–style (n=112, 48.9%) or a confrontational counseling–style (n=117, 51.1%) chatbot. Both cessation-related (ie, intention to quit and self-efficacy) and user experience–related outcomes (ie, engagement, therapeutic alliance, perceived empathy, and interaction satisfaction) were assessed. Semistructured interviews were conducted with 16 participants, 8 (50%) from each condition, and data were analyzed using thematic analysis. Results Results from a multivariate ANOVA showed that participants had a significantly higher overall rating for the MI (vs confrontational counseling) chatbot. Follow-up discriminant analysis revealed that the better perception of the MI chatbot was mostly explained by the user experience–related outcomes, with cessation-related outcomes playing a lesser role. Exploratory analyses indicated that smokers in both conditions reported increased intention to quit and self-efficacy after the chatbot interaction. Interview findings illustrated several constructs (eg, affective attitude and engagement) explaining people’s previous expectations and timely and retrospective experience with the chatbot. Conclusions The results confirmed that chatbots are a promising tool in motivating smoking cessation and the use of MI can improve user experience. We did not find extra support for MI to motivate cessation and have discussed possible reasons. Smokers expressed both relational and instrumental needs in the quitting process. Implications for future research and practice are discussed.
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Noh, Yeo-Gyeong, and Jin-Hyuk Hong. "Designing Reenacted Chatbots to Enhance Museum Experience." Applied Sciences 11, no. 16 (2021): 7420. http://dx.doi.org/10.3390/app11167420.
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The increased availability of chatbots has drawn attention and interest to the study of what answers they provide and how they provide them. Chatbots have become a common sight in museums but are limited to answering only simple and basic questions. Based on the observed potential of chatbots for history education in museums, we investigate how chatbots impact history education and improve the overall experience according to their appearance and language style. For this, we built three models, designed by factors on embodiment and reflection, and 60 sets of answer–questions, designed for the National Museum of Korea. We conducted a study with a total of 34 participants and carried out a variety of analyses covering individual learning styles, museum experience scales, gaze data, in-depth interviews and observations from researchers. We present various results and lessons regarding the effect of embodiment and reflection on the museum experience. Our findings show how people with different learning styles connect with chatbot models and how visitors’ behavior in the museum changes depending on the chatbot model. Specifically, the chatbot model equipped with embodiment and reflection shows its superiority in enhancing the museum experience, in general.
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Alsadoun, Abdullah Ali, and Asem Nasser Alnasser. "Role of artificial intelligence chatbot marketing in enhancing customer satisfaction and loyalty in digital shopping experiences." International Journal of Innovative Research and Scientific Studies 8, no. 1 (2025): 1902–9. https://doi.org/10.53894/ijirss.v8i1.4826.
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This study aims to explore the impact of AI chatbot marketing on customer satisfaction and loyalty within the context of online shopping in Saudi Arabia. It seeks to understand how the usability and responsiveness of AI chatbots influence customer perceptions and behaviors, ultimately contributing to enhanced satisfaction and loyalty. A cross-sectional research design was employed, utilizing a structured questionnaire to collect data from 271 customers with prior experience in online shopping. Convenience sampling was used to gather responses, and the questionnaire included dimensions related to the usability and responsiveness of AI chatbots. Structural equation modeling (SEM) was applied to analyze the data and assess the relationships between AI chatbot marketing, customer satisfaction, and customer loyalty. The results reveal that AI chatbot marketing significantly enhances customer satisfaction, which in turn positively influences customer loyalty. Additionally, customer satisfaction was found to mediate the relationship between AI chatbot marketing and customer loyalty. These findings highlight the critical role of AI chatbots in shaping customer experiences and fostering long-term loyalty. The study concludes that AI chatbot marketing is a powerful tool for improving customer satisfaction and loyalty in the online shopping environment. By investing in advanced AI technologies and optimizing chatbot functionalities, businesses can create more engaging and satisfying customer interactions. The findings suggest that businesses should prioritize the development and implementation of AI chatbots to enhance customer experiences. By focusing on usability and responsiveness, companies can drive higher levels of satisfaction and loyalty, thereby gaining a sustainable competitive advantage in the digital marketplace. These insights are particularly relevant for online retailers in Saudi Arabia and other regions aiming to capitalize on the growing trend of AI-driven customer engagement.
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Martínez Puertas, Sergio, María Dolores Illescas Manzano, Cristina Segovia López, and Paulo Ribeiro-Cardoso. "Purchase intentions in a chatbot environment: An examination of the effects of customer experience." Oeconomia Copernicana 15, no. 1 (2024): 145–94. http://dx.doi.org/10.24136/oc.2914.
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Research background: Chatbots represent valuable technological tools that allow companies to improve customer experiences, meet their expectations in real time, and provide them with personalized assistance. They have contributed to the transformation of conventional customer service models into online solutions, offering accessibility and efficiency through their integration across various digital platforms. Nevertheless, the existing literature is limited in terms of exploring the potential of chatbots in business communication and studying their impact on the customer's response. Purpose of the article: The main objective of this study is to examine how consumers perceive chatbots as customer service devices. In particular, the paper aims to analyze the influence of the dimensions of “Information”, “Entertainment”, “Media Appeal”, “Social Presence” and “Risk for Privacy” on the “Customer Experience” and the latter on the “Purchase Intention”, under the consideration of the Uses and Gratifications Theory. Moderations due to Chatbot Usage Frequency for some of the relationships proposed are also analyzed. Methods: An empirical study was performed through a questionnaire to Spanish consumers. The statistical data analysis was conducted with R software through the lavaan package. To test the hypotheses from the conceptual model a structural equation modelling approach was adopted. Findings & value added: The results obtained identify the main characteristics of chatbots that can support brands to effectively develop their virtual assistants in order to manage their relational communication strategies and enhance their value proposal through the online customer journey. Findings demonstrate the contribution that chatbot dimensions make to the online consumer experience and its impact on the purchase intention, with the consideration of the moderating effect exercised by the user's level of experience (novice vs. experienced) with the use of chatbots. Regarding managerial implications, this research offers recommendations for e-commerce professionals to manage chatbots more effectively. The “Entertainment” and “Social Presence” dimensions can be operationalized at a visual (e.g., appearance of the avatar and text box, use of designs aligned with the website) and textual level (e.g., style and tone of voice, use of expressions typical of the target audience) to generate a feeling of proximity with the chatbot and facilitate its adoption. “Media Appeal” requires that the chatbot be easy to use, effective, and accessible, to facilitate its usability. Finally, mitigation of “Privacy Risk” concerns should be achieved by presenting an appropriate privacy policy and requesting permission for the use of customers’ private information.
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YILDIZ DURAK, Hatice, and Aytuğ ONAN. "An Examination of Studies on the Use of Chatbot Technology in the Field of Education." International Conference on Applied Engineering and Natural Sciences 1, no. 1 (2023): 121–24. http://dx.doi.org/10.59287/icaens.978.
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Today, it is observed that technologies are rapidly advancing and being utilized in educational environments. Among these technologies, chatbots, which have gained significant popularity with ChatGPT, play a crucial role in enhancing learning experiences. Chatbots offer numerous opportunities to support learning performance, increase engagement, and provide extracurricular feedback and assistance to students. These chatbots are capable of interacting with users in a natural language format, enabling seamless communication. They are designed to follow predefined patterns, answer questions, and provide guidance within their defined scope. This article aims to examine the existing research on the utilization of chatbots in educational environments to shed light on the significance of their implementation. To achieve this objective, 19 articles and papers related to the field were analyzed using Google Scholar, incorporating keywords such as 'chatbot+education' and 'chatbot in education'. The articles and papers were subjected to a descriptive analysis, covering sections such as the subject, purpose, methodology, results, discussion, and recommendations. Based on the review, it was observed that many studies focused on the usability and product design of chatbots in education. The samples used in these studies predominantly comprised university-level participants, with limited applications targeting larger student populations. It is anticipated that the data and results presented in this study will serve as a guide to identify existing applications and research gaps in the utilization of chatbots in education.
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Tamara, Christania Armagita Julia, Willem J. F. Alfa Tumbuan, and Emilia Margareth Gunawan. "CHATBOTS IN E-COMMERCE: A STUDY OF GEN Z CUSTOMER EXPERIENCE AND ENGAGEMENT – FRIEND OR FOE?" Jurnal EMBA : Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi 11, no. 3 (2023): 161–75. http://dx.doi.org/10.35794/emba.v11i3.49501.
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With chatbots becoming more common in e-commerce, concerns have arisen about how Gen Z users perceive these virtual assistants. This study intends to investigate how Gen Z perceives chatbots in general and how they impact customer engagement and the e-commerce experience. Using semi-structured online interviews, a qualitative study methodology was used to collect information from 15 Generation Z individuals who had interacted with chatbots on e-commerce platforms in Manado City, Indonesia. In-depth knowledge of the perspectives, experiences, and complex actions of Gen Z customers around chatbots in e-commerce is made possible by the use of thematic analysis, which identifies patterns and themes in the data that has been collected. According to research, chatbots are often viewed as allies by Gen Z customers as they offer convenience, quick replies, and around-the-clock accessibility. However, there are problems with their ability to understand difficult questions and provide customized solutions. The study's conclusions emphasize the significance of elements like information accuracy, personalization, and interface usability in determining how Gen Z clients interact and interact with chatbots. By identifying areas for improvement and adjusting chatbot interactions to meet the expectations of Gen Z clients, e-commerce organizations may optimize customer care initiatives and improve customer experiences. The recommendations include enhancing chatbot functionality and design, personalizing interactions to users' interests, encouraging openness and trust, providing smooth human handoffs, and implementing multi-channel support. Keywords: chatbots, e-commerce, customer experience, customer engagement
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Pala, Cherry Ann, Danica Lagrimas, and Martin L. Nobis, Jr. "Exploration of Chatbots in Mathematics Education for Innovative Learning Process." Asian Journal of Advanced Research and Reports 19, no. 5 (2025): 178–94. https://doi.org/10.9734/ajarr/2025/v19i51010.
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Aims: This study aimed to explore the potential of chatbots in mathematics education by introducing them as an innovative learning tool for Bachelor of Secondary Education Math students. Specifically, the study seeks to provide a comprehensive understanding of how chatbots can be integrated into the mathematics learning process. Ultimately, this study aims to enhance the knowledge and understanding of chatbots’ capabilities and their impact on educational environments, thereby contributing to improved educational outcomes in mathematics. Study Design: This study employed a mixed-methods research approach. This method is well-suited for this research as it combines both qualitative and quantitative methods to provide a comprehensive understanding of the experiences faced by mathematics students in integrating chatbots into their learning process. Place and Duration of Study: The study was conducted at the University of Eastern Philippines, Laoang Campus, Laoang, Northern Samar, during the school year 2024-2025. Methodology: The researchers included Bachelor of Secondary Education (BSED) Mathematics students from the University of Eastern Philippines, Laoang Campus, during the school year 2024–2025, who have actively used chatbots in their mathematics education.30 Participants were selected through purposive sampling. Data collection involved semi-structured one-on-one interviews and survey questionnaires. Thematic analysis was applied to qualitative data from interviews, while descriptive statistics (frequency and percentage) were used to analyze quantitative survey data on chatbot usage. Results: Out of 30 mathematics students, Cici was reported as the most frequently used chatbot among mathematics students, followed by ChatGPT and Copilot. Other chatbots like Meta AI, Perplexity, and Math AI were also noted, with less usage reported for Gemini, Question AI, Gauth Math, Sizzling, and Quillbot. It also revealed that chatbots assisted students with problem-solving in mathematics, enhanced conceptual understanding, ensured accuracy and validity, expanded mathematical knowledge, improved accessibility and convenience, boosted student confidence and engagement, and provided a student-friendly and time-saving learning experience, issues like dependency on chatbots, subscriptions or paywall issues, lack of details or clarity, inaccurate or unreliable answers, and internet connectivity issues were commonly mentioned. In response, students recommended strategies such as cross-verification of answers, avoiding over-reliance on chatbots, using multiple chatbots, clarifying or specifying questions, improving access and free tools, responsible use, and student discipline. Conclusion: Chatbots like Cici, ChatGPT, and Copilot play a growing role in mathematics education, aiding students in problem-solving and conceptual understanding. While they enhance accessibility and engagement, challenges such as dependency and accuracy concerns exist. Students advocate for responsible usage, cross-checking answers, and refining queries to ensure AI tools support independent learning rather than replace it.
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Arif, Ariobimo, Made Sudarma, and I. Wayan Shandyasa. "ANALISIS KEPUASAN PENGGUNA DENGAN SISTEM CHATBOT PEMESANAN MAKANAN DAN MINUMAN VIA TELEGRAM DI RESTORAN." Jurnal SPEKTRUM 11, no. 1 (2024): 116. http://dx.doi.org/10.24843/spektrum.2024.v11.i01.p13.
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In the era of technological advancement, the utilization of Chatbots for food and beverage menu ordering through the Telegram Bot platform emerges as an intriguing alternative to enhance customer experiences in the Food and Beverage industry. This research aims to analyze user satisfaction with the Chatbot implementation using the Heuristic method. In this implementation, the Chatbot is supported by Natural Language Processing (NLP) with a focus on customer satisfaction analysis. The Heuristic method serves as the primary evaluation tool to measure aspects such as service speed, user-friendliness, and information clarity. Analysis results reveal positive user responses to the Chatbot, indicating a high level of satisfaction in system usage. These findings support the notion that employing Chatbots with the Heuristic method can deliver a satisfying experience for customers in food and beverage menu ordering. Practical implications of this research can assist restaurants in enhancing service and efficiency through Chatbot implementation that considers user satisfaction aspects measured by the Heuristic method.
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Kharis, M., Sandra Schön, Edy Hidayat, Rizky Ardiansyah, and Martin Ebner. "Development of a Chatbot App for Interactive German Grammar Learning." International Journal of Emerging Technologies in Learning (iJET) 17, no. 14 (2022): 52–63. http://dx.doi.org/10.3991/ijet.v17i14.31323.
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This study aims to develop the Gramabot application as a means of online learning during the pandemic and to examine its effectiveness as a medium for German grammar learning. The design-based research uses learning experience design methodology. Beside an introduction of the state of the art of chatbots for language learning, the paper describes the concept of the chatbot application and its evaluation. Prior experiences with chatbots, implementation experiences and evaluations insights were obtained from 36 Indonesian students with beginner level German language skills. The data were described using qualitative and simple descriptive statistical analysis. The results of the analysis show that most of the respondents are familiar with several chatbot applications, the use of chatbots is not as a learning medium, but as a means of asking questions about products. In addition, String Matching is a suitable method used to develop Gramabot. Based on the results of the evaluation, it can be concluded that Gramabot can help students understand basic level grammatical material.
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Alrawad, Mahmaod, Latifa Attieh, Bayar Ibrahim, and Mujtaba Ramadan. "Bridging AI and human interaction: How trust and social presence affects chatbot acceptance." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 385–97. https://doi.org/10.53894/ijirss.v8i2.5168.
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This study examines the factors driving chatbot adoption among customers. Integrating the Technology Acceptance Model with social presence, perceived performance, and trust, the research investigates how these variables influence customers' attitudes and intentions to use chatbots. The findings confirm the importance of conversational commerce, particularly for millennials, and support the Social Presence Theory, highlighting the positive impact of human-like interactions on trust and attitudes towards chatbots. Key constructs such as trust, perceived usefulness, ease of use, and behavioral intention are validated as influential factors in consumer decisions regarding chatbot technology. The study offers practical implications for businesses seeking to enhance chatbot experiences by focusing on usability, usefulness, and performance. Future research should consider a broader population, explore additional variables like ethical considerations, cross-cultural differences, and personalization, and investigate potential moderating factors such as personality traits and prior technology experience.
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Iyamuremye, Aloys, and Kizito Ndihokubwayo. "Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT." Educational Journal of Artificial Intelligence and Machine Learning 1, no. 1 (2024): 1–13. http://dx.doi.org/10.58197/prbl/9hk37296.
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This research explored the impact of using ChatGPT, a chatbot-assisted artificial intelligence (AI), on students’ interest and conceptual understanding of atomic structure and chemical bonding. A total of 92 senior and two secondary students, including 61 male and 31 female students from Rwanda, were randomly selected to participate in the study. The study used a sequential explanatory research design to collect and analyze data. A pre and post-achievement test was used to collect quantitative before and after using ChatGPT. On the other hand, focus group discussion was used to collect qualitative data to explore students’ interest in using chatbots. Repeated measure ANOVA was used to analyze quantitative data from achievement tests, while thematic analysis was used to analyze qualitative data from focus group discussions. The results revealed that the generated response from the use of chatbot-AI is an effective method to supplement other active methods of teaching and learning atomic structure and chemical bonding. In this regard, the students’ performance increased by 16.6% after the use of chatbot-AI. However, the study did not find a statistically significant difference between male and female students after using ChatGPT. Thematic analysis revealed three overarching themes. In this line, the use of ChatGPT stimulates students’ engagement and interactivity, curiosity and motivation, and enhanced learning experience. Educators and policymakers were recommended to integrate this technology into the curriculum to supplement other active teaching and learning pedagogies.
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Baring Arreza, Mariae Khrisna. "Users Experience On Airline Chatbots." Asia-Pacific Journal of Information Technology and Multimedia 11, no. 01 (2022): 102–14. http://dx.doi.org/10.17576/apjitm-2022-1101-09.
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A chatbot is an advancing technology that has taken the airline industry to a different level of service. The continuous pursuit drives this development to provide efficient and quality services; however, this technology is not void of challenges that may affect customers' experiences. This study utilizes a sequential mixed method approach that explored the Filipino experiences of using airline chatbots. The results revealed neutral users' experiences. The qualitative components revealed the following themes (1) Difficult experience; (2) Limitations of the chatbots; (3) Lack of human connections and; (4) Positive experiences. As a recommendation, further study may be required utilizing different indicators.