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Rangasamy, Sangeetha, Aishwarya Nagarathinam, Aarthy Chellasamy, and Elangovan N. "Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (October 7, 2023): 440–50. http://dx.doi.org/10.17762/ijritcc.v11i10s.7652.
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Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India.
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Suh, Jeehae. "A Study on the Conformity of Chatbot Builder as a Korean Speech Practice Tool." Korean Society of Culture and Convergence 45, no. 1 (January 31, 2023): 61–70. http://dx.doi.org/10.33645/cnc.2023.01.45.01.61.
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The purpose of this study is to verify whether chatbots made with chatbot builders are suitable as a Korean speaking practice tool. Chatbot builders, which can be easily produced as chatbots without separate coding knowledge and can design conversations meaningful for learning, have recently been in the spotlight as a learning tool. In this study, chatbots were created using dialog flows, and conversation patterns with chatbots shared by study participants were analyzed. As a result of the analysis, it was found that 35% of all conversations were not successfully completed. Such a conversation failure was found to be due to the inaccuracy of chatbot's recognition of Korean learner pronunciation, error in handling learner utterance intention, and inaccuracy in handling learner error sentences. In this regard, in order for chatbot builder to be used as a Korean language learning tool now, learner's proficiency or academic achievement should be considered for smooth processing of chatbot's learner utterance. In addition, this study is meaningful in that it verified the suitability of chatbot builders as a learning tool not covered in previous studies.
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Cui, Yichao, Yu-Jen Lee, Jack Jamieson, Naomi Yamashita, and Yi-Chieh Lee. "Exploring Effects of Chatbot's Interpretation and Self-disclosure on Mental Illness Stigma." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (April 17, 2024): 1–33. http://dx.doi.org/10.1145/3637329.
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Chatbots are increasingly being used in mental healthcare - e.g., for assessing mental-health conditions and providing digital counseling - and have been found to have considerable potential for facilitating people's behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants' statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants' comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots' conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants' stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot's self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users' perceptions of the chatbots themselves, and how chatbots' interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.
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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 (April 30, 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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Bortko, Kamil, Kacper Fornalczyk, Jarosław Jankowski, Piotr Sulikowski, and Karina Dziedziak. "Impact of changes in chatbot’s facial expressions on user attention and reaction time." PLOS ONE 18, no. 7 (July 27, 2023): e0288122. http://dx.doi.org/10.1371/journal.pone.0288122.
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Communication within online platforms supported by chatbots requires algorithms, language processing methods, and an effective visual representation. These are crucial elements for increasing user engagement and making communication more akin to natural conversation. Chatbots compete with other graphic elements within websites or applications, and thus attracting a user’s attention is a challenge even before the actual conversation begins. A chatbot may remain unnoticed even with sophisticated techniques at play. Drawing attention to the chatbot area localized within the periphery area can be carried out with the use of various visual characteristics. The presented study analyzed the impact of changes in a chatbot’s emotional expressions on user reaction. The aim of this study was to observe, based on user reaction times, whether changes in a chatbot’s emotional expressions make it more noticeable. The results showed that users are more sensitive to positive emotions within chatbots, as positive facial expressions were noticed more quickly than negative ones.
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Biro, Joshua, Courtney Linder, and David Neyens. "The Effects of a Health Care Chatbot’s Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study." JMIR Human Factors 10 (February 1, 2023): e41017. http://dx.doi.org/10.2196/41017.
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Background The rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information. Objective Our objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot. Methods A 2×3 between-subjects study was performed with 2 independent variables: a chatbot’s complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot’s persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot. Results Analysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine. Conclusions Given their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots’ persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.
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Septiyanti, Nisa Dwi, Muhammad Irfan Luthfi, and Darmawansah Darmawansah. "Effect of Chatbot-Assisted Learning on Students’ Learning Motivation and Its Pedagogical Approaches." Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika 10, no. 1 (April 30, 2024): 69–77. http://dx.doi.org/10.23917/khif.v10i1.4246.
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Abstract- The use of chatbots in the learning process has been increasingly investigated and applied. While many studies have discussed the chatbot's ability to motivate students' interest in learning, few have examined whether students' perception of learning affects the effectiveness of chatbots and the pedagogical approach taken by chatbots as conversational agents during the learning process. There is a need for new analysis to capture the effects of Chatbot-Assisted Learning (Chatbot-AL) and student-chatbot conversations. In an eight-week semester, 48 first-year undergraduate students participated in a chatbot-assisted learning environment integrated into an engineering course. Data were collected through questionnaires on students' learning motivation and discourse in chatbot conversations. Statistical non-parametric analysis and Epistemic Network Analysis (ENA) were used to explore the research questions. The results showed that students with high learning perception had better learning motivation using chatbot-AL than students with low learning perception. Additionally, most of the questions asked by students were aimed at receiving emotional support through casual conversation with the chatbot. Finally, the implications, limitations, and conclusions were discussed.
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Dennis, Alan R., Antino Kim, Mohammad Rahimi, and Sezgin Ayabakan. "User reactions to COVID-19 screening chatbots from reputable providers." Journal of the American Medical Informatics Association 27, no. 11 (July 6, 2020): 1727–31. http://dx.doi.org/10.1093/jamia/ocaa167.
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Abstract Objectives The objective was to understand how people respond to coronavirus disease 2019 (COVID-19) screening chatbots. Materials and Methods We conducted an online experiment with 371 participants who viewed a COVID-19 screening session between a hotline agent (chatbot or human) and a user with mild or severe symptoms. Results The primary factor driving user response to screening hotlines (human or chatbot) is perceptions of the agent’s ability. When ability is the same, users view chatbots no differently or more positively than human agents. The primary factor driving perceptions of ability is the user’s trust in the hotline provider, with a slight negative bias against chatbots’ ability. Asian individuals perceived higher ability and benevolence than did White individuals. Conclusions Ensuring that COVID-19 screening chatbots provide high-quality service is critical but not sufficient for widespread adoption. The key is to emphasize the chatbot’s ability and assure users that it delivers the same quality as human agents.
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Sabna, Eka. "CHATBOT SEBAGAI GURU VIRTUAL UNTUK MATA KULIAH DATA MINING." Jurnal Ilmu Komputer 11, no. 2 (November 4, 2022): 110–15. http://dx.doi.org/10.33060/jik/2022/vol11.iss2.276.
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Abstract
A chatbot is an application (service) that interacts with users through text conversations. Chatbots work to replace the role of humans in serving conversations through messaging applications. The chatbot that is built will be virtual assisting that will help students learn at home. Chatbots can only answer questions based on patterns that have been stored in the chatbot's knowledge base. Chatbots are automated conversational agents that interact with users using natural human language that can help anytime and anywhere. This chatbot is applied as a Virtual Teacher who can provide information and learning materials to students in Data Mining courses.
Keywords: C4.5, NBC, GPA, performance, student
Abstrak
Chatbot merupakan aplikasi (layanan) yang berinteraksi dengan pengguna melalui percakapan teks. Chatbot bekerja untuk menggantikan peranan manusia dalam melayani pembicaraan melalui aplikasi pesan. chatbot yang dibangun akan menjadi virtual assisting yang akan membantu Mahasiswa dalam belajar dirumah. Chatbot hanya dapat menjawab pertanyaan berdasarkan pola yang telah disimpan di dalam knowledge base chatbot. Chatbot adalah agen percakapan otomatis yang berinteraksi dengan pengguna menggunakan bahasa alami manusia yang dapat membantu kapan saja dan dimana saja. Chatbot ini di aplikasikan sebagai Guru Virtual yang dapat memberikan informasi dan materi pembelajaran terhadap mahasiswa dalam matakuliah Data Mining.
Kata Kunci : Chatbot, Data Mining, Virtual Teacher, Student, Learning
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Le, Nhat, A. B. Siddique, Fuad Jamour, Samet Oymak, and Vagelis Hristidis. "Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems." International Journal of Semantic Computing 15, no. 04 (December 2021): 419–39. http://dx.doi.org/10.1142/s1793351x21400109.
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Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.
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Abu-Haifa, Mohammad, Bara'a Etawi, Huthaifa Alkhatatbeh, and Ayman Ababneh. "Comparative Analysis of ChatGPT, GPT-4, and Microsoft Copilot Chatbots for GRE Test." International Journal of Learning, Teaching and Educational Research 23, no. 6 (June 30, 2024): 327–47. http://dx.doi.org/10.26803/ijlter.23.6.15.
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This paper presents an analysis of how well three artificial intelligence chatbots: Copilot, ChatGPT, and GPT-4, perform when answering questions from standardized tests, mainly the Graduate Record Examination (GRE). A total of 137 questions with different forms of quantitative reasoning and 157 questions with verbal categories were used to assess the chatbot’s capabilities. This paper presents the performance of each chatbot across various skills and styles tested in the exam. The proficiency of the chatbots in addressing image-based questions is also explored, and the uncertainty level of each chatbot is illustrated. The results show varying degrees of success among the chatbots. ChatGPT primarily makes arithmetic errors, whereas the highest percentage of errors made by Copilot and GPT-4 are conceptual. However, GPT-4 exhibited the highest success rates, particularly in tasks involving complex language understanding and image-based questions. Results highlight the ability of these chatbots in helping examinees to pass the GRE with a high score, which encourages the use of them in test preparation. The results also show the importance of preventing access to similar chatbots when tests are conducted online, such as during the COVID-19 pandemic, to ensure a fair environment for all test takers competing for higher education opportunities.
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Patrusheva, L. S. "CHATBOT TECHNOLOGY IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE AT A BASIC LEVEL: FROM DEVELOPMENT EXPIERENCE." Bulletin of Udmurt University. Series History and Philology 32, no. 4 (August 26, 2022): 848–53. http://dx.doi.org/10.35634/2412-9534-2022-32-4-848-853.
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The article is concerned with study of chatbots in the practice of teaching Russian as a foreign language. The practice of chatbots for teaching Russian as a foreign language is a new element in the development of linguodidactics. Researchers agree that chatbots will become an indispensable partner in learning foreign languages. In the field of teaching Russian as a foreign language, chatbots are just beginning to be created. Teachers make first attempts to introduce them into the educational process. Since teaching Russian as a foreign language today is generally aimed at developing communicative skills, it seems possible to introduce chatbot technologies into the teaching process. Сhatbots can help to drill speech patterns, conversation skills in social networks, to help overcome the psychological barrier in the communication in Russian. The article presents a series of chatbots "S chatbotom po doroge" which gives conversation practice for elementary language level students. Chatbots compliment a textbook of Russian as a foreign language "Doroga v Rossiyu".
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Nze, Stella Udoka. "AI-Powered Chatbots." Global Journal of Human Resource Management 12, no. 6 (June 15, 2024): 34–45. http://dx.doi.org/10.37745/gjhrm.2013/vol12n63445.
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Artificial Intelligence (AI)-powered chatbots have emerged as a transformative technology, fundamentally changing how businesses and organizations engage with their customers by providing real-time, personalized communication. These chatbots, driven by sophisticated algorithms, utilize Natural Language Processing (NLP) and Machine Learning (ML) to understand, interpret, and respond to human language in a manner that is contextually appropriate and relevant. As a result, AI-powered chatbots enhance both user experience and operational efficiency by automating routine interactions, reducing response times, and providing consistent, high-quality service. The integration of AI chatbots spans multiple sectors, including customer service, healthcare, education, and e-commerce. In customer service, chatbots are deployed to manage high volumes of inquiries, troubleshoot issues, and offer personalized assistance around the clock, thereby freeing human agents to focus on more complex tasks. In healthcare, AI-powered chatbots are utilized to facilitate patient engagement by providing initial diagnoses, managing appointment schedules, offering medication reminders, and delivering health information. Educational institutions employ these chatbots to interact with students, answer frequently asked questions, facilitate administrative processes, and support learning through personalized tutoring. Meanwhile, in e-commerce, chatbots serve as virtual shopping assistants, offering product recommendations, guiding users through their purchasing journey, and resolving post-purchase concerns. This paper delves into the development and deployment methodologies of AI-powered chatbots, examining the various approaches and technologies used to build robust and efficient chatbot systems. The discussion highlights key components such as NLP, ML, reinforcement learning, and deep learning techniques that contribute to the chatbot’s ability to understand user intent, handle natural language conversations, and learn from past interactions to improve future responses. Additionally, the paper analyzes chatbot architecture, including front-end interfaces, dialogue management systems, and backend integration, to provide a comprehensive understanding of the chatbot ecosystem. The literature review presented in this paper synthesizes findings from recent studies and publications, identifying the current trends, advancements, and challenges in implementing AI chatbots across different domains. It evaluates the effectiveness of chatbots in achieving key performance indicators such as customer satisfaction, response accuracy, operational efficiency, and cost savings. The review also highlights areas where AI chatbots have proven to be most effective and identifies potential limitations, including data privacy concerns, integration challenges with existing legacy systems, and the limitations of current NLP models in understanding context, sarcasm, or nuanced language. This paper further discusses the benefits and challenges associated with deploying AI-powered chatbots. Benefits such as 24/7 availability, scalability, reduced operational costs, and enhanced customer engagement are explored in detail, demonstrating how chatbots can deliver substantial value to organizations. Conversely, the paper also addresses challenges such as ensuring data security and privacy, overcoming natural language understanding (NLU) limitations, mitigating biases in AI models, and managing customer expectations when interacting with non-human agents. In addition, this paper provides a forward-looking perspective on the potential future developments of AI chatbots. It explores emerging trends such as multimodal chatbots that integrate voice, text, and visual inputs; advancements in emotion recognition to enable more empathetic and human-like interactions; and the rise of explainable AI, where chatbots can provide transparency in their decision-making processes.To illustrate these concepts, the paper includes diagrams that depict the architecture of AI chatbot systems, the flow of natural language processing, and the integration of various components such as databases, machine learning models, and user interfaces. These visual aids provide a clearer understanding of the technical and functional aspects of chatbot development and deployment. Overall, this paper aims to provide a comprehensive analysis of AI-powered chatbots, detailing their applications, benefits, challenges, and future potential. It serves as a guide for businesses, researchers, and technology developers interested in leveraging AI chatbots to enhance communication, streamline operations, and create a more engaging user experience. By critically examining both the opportunities and the limitations, this research offers valuable insights into the strategic implementation of AI chatbots across diverse industries.
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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 (September 30, 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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Chen, David, Ryan S. Huang, Jane Jomy, Philip Wong, Michael Yan, Jennifer Croke, Daniel Tong, Andrew Hope, Lawson Eng, and Srinivas Raman. "Performance of Multimodal Artificial Intelligence Chatbots Evaluated on Clinical Oncology Cases." JAMA Network Open 7, no. 10 (October 23, 2024): e2437711. http://dx.doi.org/10.1001/jamanetworkopen.2024.37711.
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ImportanceMultimodal artificial intelligence (AI) chatbots can process complex medical image and text-based information that may improve their accuracy as a clinical diagnostic and management tool compared with unimodal, text-only AI chatbots. However, the difference in medical accuracy of multimodal and text-only chatbots in addressing questions about clinical oncology cases remains to be tested.ObjectiveTo evaluate the utility of prompt engineering (zero-shot chain-of-thought) and compare the competency of multimodal and unimodal AI chatbots to generate medically accurate responses to questions about clinical oncology cases.Design, Setting, and ParticipantsThis cross-sectional study benchmarked the medical accuracy of multiple-choice and free-text responses generated by AI chatbots in response to 79 questions about clinical oncology cases with images.ExposuresA unique set of 79 clinical oncology cases from JAMA Network Learning accessed on April 2, 2024, was posed to 10 AI chatbots.Main Outcomes and MeasuresThe primary outcome was medical accuracy evaluated by the number of correct responses by each AI chatbot. Multiple-choice responses were marked as correct based on the ground-truth, correct answer. Free-text responses were rated by a team of oncology specialists in duplicate and marked as correct based on consensus or resolved by a review of a third oncology specialist.ResultsThis study evaluated 10 chatbots, including 3 multimodal and 7 unimodal chatbots. On the multiple-choice evaluation, the top-performing chatbot was chatbot 10 (57 of 79 [72.15%]), followed by the multimodal chatbot 2 (56 of 79 [70.89%]) and chatbot 5 (54 of 79 [68.35%]). On the free-text evaluation, the top-performing chatbots were chatbot 5, chatbot 7, and the multimodal chatbot 2 (30 of 79 [37.97%]), followed by chatbot 10 (29 of 79 [36.71%]) and chatbot 8 and the multimodal chatbot 3 (25 of 79 [31.65%]). The accuracy of multimodal chatbots decreased when tested on cases with multiple images compared with questions with single images. Nine out of 10 chatbots, including all 3 multimodal chatbots, demonstrated decreased accuracy of their free-text responses compared with multiple-choice responses to questions about cancer cases.Conclusions and RelevanceIn this cross-sectional study of chatbot accuracy tested on clinical oncology cases, multimodal chatbots were not consistently more accurate than unimodal chatbots. These results suggest that further research is required to optimize multimodal chatbots to make more use of information from images to improve oncology-specific medical accuracy and reliability.
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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 (May 31, 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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Agarwal, Vineet, and Anjali Shukla. "Chatbot for Interview." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (July 30, 2022): 46–49. http://dx.doi.org/10.35940/ijrte.b7092.0711222.
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The advent of virtual assistants has made communicating with computers a reality. Chatbots are virtual assistant tools designed to simplify the communication between humans and computers. A chatbot will answer your queries and execute a certain computation if required. Chatbots can be developed using Natural Language Processing (NLP) and Deep Learning. Natural Language Process technique like Naïve bayes can be used. Chatbot can be implemented for a fun purpose like chit-chat; these are called Conversational chatbots. Chatbots designed to answer any questions is known as horizontal chatbots and the specific task-oriented chatbots are known as vertical chatbots (also known as Closed Domain Chatbots). In this paper, we will be discussing a task-oriented chatbot to help recruitment team in the technical round of interview process.
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Fang, Jiyang. "Analysis on Chatbot Performance based on Attention Mechanism." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 151–56. http://dx.doi.org/10.54097/hset.v39i.6517.
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The chatbot is a way to imitate the dialogue between people through natural language, enabling human beings to communicate with machines more naturally. The chatbot is a prevalent natural language processing task (NLP) because it has broad application prospects in real life. This is also a complex task involving many natural language processing tasks that must be studied. The chatbot is an intelligent dialogue system that can simulate human dialogue to achieve online guidance and support. The main work of this paper is to summarize the chatbot's academic background and research status and introduce the Cornell Movie-Dialogs Corpus dataset. The methods of artificial intelligence and natural language processing are outlined. Two attention mechanisms used to improve neural machine translation (NMT) are discussed. Finally, this paper tests the performance of chatbots under the influence of N_ITERATION and data scale summarizes the relevant optimization strategies and makes a prospect for the future of chatbots. The main work of this paper is to test the performance of the proposed method under different experimental Settings, including dialog templates, adjusting the amount of training data, and to adjust the number of iterations. The results show that the chatbot's vocabulary changes with N_ITERATION and that increasing the data in the training dataset improves the chatbot's understanding.
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Lapeña, José Florencio. "The Updated World Association of Medical Editors (WAME) Recommendations on Chatbots and Generative AI in Relation to Scholarly Publications and International Committee of Medical Journal Editors (ICMJE) Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals (May 2023)." Philippine Journal of Otolaryngology Head and Neck Surgery 38, no. 1 (June 4, 2023): 4. http://dx.doi.org/10.32412/pjohns.v38i1.2127.
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On January 20, 2023, the World Association of Medical Editors published a policy statement on Chatbots, ChatGPT, and Scholarly Manuscripts: WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications.1 There were four recommendations, namely: 1. Chatbots cannot be authors; 2. Authors should be transparent when chatbots are used and provide information about how they were used; 3. Authors are responsible for the work performed by a chatbot in their paper (including the accuracy of what is presented, and the absence of plagiarism) and for appropriate attribution of all sources (including for material produced by the chatbot); and 4. Editors need appropriate tools to help them detect content generated or altered by AI and these tools must be available regardless of their ability to pay.1 This statement was spurred in part by some journals beginning to publish papers in which chatbots such as ChatGPT were listed as co-authors.2
First, only humans can be authors. Chatbots cannot be authors because they cannot meet authorship requirements “as they cannot understand the role of authors or take responsibility for the paper.”1 In particular, they cannot meet the third and fourth ICMJE criteria for authorship, namely “Final approval of the version to be published” and “Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.”1,3 Moreover, “a chatbot cannot understand a conflict of interest statement, or have the legal standing to sign (such a) statement,” nor can they “hold copyright.”1 Because authors submitting a manuscript must ensure that all those named as authors meet ICMJE authorship criteria, chatbots clearly should not be included as authors.1
Second, authors should acknowledge the sources of their materials. When chatbots are used, authors “should declare this fact and provide full technical specifications of the chatbot used (name, version, model, source) and method of application in the paper they are submitting (query structure, syntax),” “consistent with the ICMJE recommendation of acknowledging writing assistance.”1,4
Third, authors must take public responsibility for their work; “Human authors of articles written with the help of a chatbot are responsible for the contributions made by chatbots, including their accuracy,” and “must be able to assert that there is no plagiarism in their paper, including in text produced by the chatbot.”1Consequently, authors must “ensure … appropriate attribution of all quoted material, including full citations,” “seek and cite the sources that support,” as well as oppose (since chatbots can be designed to omit counterviews), the chatbot’s statements.1
Fourth, to facilitate all this, medical journal editors (who “use manuscript evaluation approaches from the 20th century”) “need appropriate (digital) tools … that will help them evaluate … 21st century … content (generated or altered by AI) efficiently and accurately.”1
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Ardiansyah, Rizky, Dianthy Marya, and Atik Novianti. "Penggunaan metode string matching pada sistem informasi mahasiswa Polinema dengan chatbot." JURNAL ELTEK 21, no. 1 (April 30, 2023): 28–35. http://dx.doi.org/10.33795/eltek.v21i1.381.
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Pelayanan kampus yang baik sangat penting untuk menunjang proses belajar mengajar di perguruan tinggi. Salah satu cara untuk meningkatkan pelayanan adalah dengan menggunakan chatbot. Chatbot dapat memberikan pelayanan yang cepat dan efisien kepada mahasiswa, serta membantu dalam mendapatkan informasi yang dibutuhkan. Chatbot dapat memberikan pelayanan yang efektif dan efisien dalam menyediakan informasi dan melakukan transaksi. Penggunaan chatbot juga mengurangi beban kerja petugas administrasi dan memungkinkan mereka untuk fokus pada tugas yang lebih kompleks. Meskipun demikian, perlu perbaikan dalam aspek keamanan data dan privasi. Penelitian ini bertujuan untuk menguji aplikasi chatbot pelayanan administrasi dengan menggunakan metode string matching. Pengujian dilakukan melalui beberapa tahap, termasuk pengujian fungsi, interaksi, kompatibilitas, keamanan, dan performa chatbot. Hasil pengujian menunjukkan bahwa chatbot memiliki tingkat akurasi sebesar 90% dengan metode string matching. Mayoritas mahasiswa merasa puas dengan kecepatan dan akurasi respon chatbot. Selain itu, aplikasi ini juga dianggap mudah digunakan oleh mahasiswa.
ABSTRACTCampus services are crucial for supporting the teaching and learning process in higher education institutions. One effective approach to enhance these services is through the implementation of chatbots. Chatbots provide prompt and efficient assistance to students, enabling them to easily access the necessary information. They offer an efficient and effective means of delivering services by providing accurate information and facilitating transactions. Furthermore, the utilization of chatbots alleviates the burden on administrative staff, allowing them to focus on more complex tasks. However, it is important to address and improve data security and privacy aspects. This research aims to evaluate an administrative service chatbot using the string matching method. The evaluation process encompasses assessing the chatbot's functionality, interaction capabilities, compatibility, security measures, and overall performance. The results demonstrate an impressive 90% accuracy rate achieved through the implementation of the string matching method. The majority of students’s express satisfaction with the chatbot's promptness and accuracy in providing responses. Additionally, the application is highly regarded for its user-friendly interface, as reported by the students.
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Varitimiadis, Savvas, Konstantinos Kotis, Dimitra Pittou, and Georgios Konstantakis. "Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums." Applied Sciences 11, no. 19 (October 1, 2021): 9160. http://dx.doi.org/10.3390/app11199160.
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Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.
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Sholahuddin, Muhammad Rizqi, and Firas Atqiya. "Sistem Tanya Jawab Konsultasi Shalat Berbasis RASA Natural Language Understanding (NLU)." Jurnal Pendidikan Multimedia (Edsence) 3, no. 2 (December 27, 2021): 93–102. http://dx.doi.org/10.17509/edsence.v3i2.38732.
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A chatbot is an intelligent system that provides users with direct interaction with machines via written media. This paper describes how to use chatbots to ask questions about prayer procedures. A Muslim sometimes has questions about the procedure for praying when he finds a difference between the procedures performed by other Muslims. In this case, the use of chatbots is to provide an explanation. This chatbot was developed using a deep learning model, especially LSTM, that was integrated with the RASA framework. LSTM (Long Short Term Memory) can efficiently save some of the needed memory while also removing some of the unnecessary memory. The Telegram platform was chosen for the chatbot's implementation. The results showed that the chatbot telegram prayer consultation with DIET Classifier and RASA was able to recognize questions and provide answers in the form of text and images, with 96 percent accuracy.
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Ma’rup, Muhammad, Tobirin, and Ali Rokhman. "Utilization of Artificial Intelligence (AI) Chatbots in Improving Public Services: A Meta-Analysis Study." Open Access Indonesia Journal of Social Sciences 7, no. 4 (June 5, 2024): 1610–18. http://dx.doi.org/10.37275/oaijss.v7i4.255.
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AI chatbots have emerged as a transformative tool in public service delivery. This study aims to conduct a systematic review and meta-analysis of existing literature to assess the effectiveness of AI chatbots in improving efficiency, response time and user satisfaction in various public service contexts. A comprehensive literature search was conducted on the Scopus database, limiting studies published between 2018 and 2024. Inclusion criteria included quantitative studies that evaluated the impact of AI chatbots on at least one of three outcome variables: efficiency, response time, or user satisfaction. Data were extracted and effect sizes (in this case Standardized Mean Difference - SMD) were calculated for each study. Moderator analysis was conducted to investigate the influence of the type of public service, the complexity of the chatbot's tasks, the type of AI, and the level of human interaction on the effectiveness of the chatbot. Meta-analysis of 30 studies (N = 9,380) shows that AI chatbots have a significant positive effect on the efficiency of public services (SMD = 0.35, 95% CI [0.25, 0.45]), reducing response time (SMD = -0.40, 95% CI [-0.50, -0.30]), and increased user satisfaction (SMD = 0.50, 95% CI [0.40, 0.60]). Moderator analysis revealed that AI chatbots were more effective in healthcare and for simple tasks. Machine learning-based chatbots also show higher effectiveness than rule-based chatbots. In conclusion, AI chatbots offer significant potential to improve various aspects of public services. However, their effectiveness varies depending on the implementation context. These findings provide valuable empirical evidence for policymakers and practitioners to effectively design and implement AI chatbots in public services.
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Kuhail, Mohammad Amin, Justin Thomas, Salwa Alramlawi, Syed Jawad Hussain Shah, and Erik Thornquist. "Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior." Informatics 9, no. 4 (October 10, 2022): 81. http://dx.doi.org/10.3390/informatics9040081.
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Chatbots with personality have been shown to affect engagement and user subjective satisfaction. Yet, the design of most chatbots focuses on functionality and accuracy rather than an interpersonal communication style. Existing studies on personality-imbued chatbots have mostly assessed the effect of chatbot personality on user preference and satisfaction. However, the influence of chatbot personality on behavioral qualities, such as users’ trust, engagement, and perceived authenticity of the chatbots, is largely unexplored. To bridge this gap, this study contributes: (1) A detailed design of a personality-imbued chatbot used in academic advising. (2) Empirical findings of an experiment with students who interacted with three different versions of the chatbot. Each version, vetted by psychology experts, represents one of the three dominant traits, agreeableness, conscientiousness, and extraversion. The experiment focused on the effect of chatbot personality on trust, authenticity, engagement, and intention to use the chatbot. Furthermore, we assessed whether gender plays a role in students’ perception of the personality-imbued chatbots. Our findings show a positive impact of chatbot personality on perceived chatbot authenticity and intended engagement, while student gender does not play a significant role in the students’ perception of chatbots.
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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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Minh Giam, Nguyen, Nguyen Thi Hoai Nam, and Nguyen Van Doc. "The Process Of Building A Virtual Teacher According To Self-Regulated Learning In Teaching Maths In Primary Schools." International Journal of Scientific Research and Management (IJSRM) 12, no. 02 (February 3, 2024): 3178–84. http://dx.doi.org/10.18535/ijsrm/v12i02.el02.
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Chatbot is a program that interacts with users via textual or auditory. Nowadays, Chatbots are widely used in jobs such as virtual assistants for customer care, product introduction, sales, etc. Chatbots have also been applied in many different aspects of the field of education thanks to the benefits it brings including high personalization, high interactivity, speed, accuracy, etc. A popular application of Chatbots in education is using Chatbots to teach. This article analyzes the benefits that Chatbots bring to the field of education, thereby providing a process for designing a teaching Chatbot for a specific lesson in the direction of self-regulated learning to help teachers and educators have orientation in using Chatbot in teaching.
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Mustafa, Sara Hassan, Elsir Abdelmutaal Mohammed, Ahmed Mustafa Salih, Kanagarajan Palani, Maha Mohamed Omer Albushra, Salma Taha Makkawi, and Amgad Hassan Mustafa. "A Qualitative Exploration of Acceptance of a Conversational Chatbot as a Tool for Mental Health Support among University Students in Sudan." International Journal of Medical Sciences and Nursing Research 4, no. 1 (March 31, 2024): 16–23. http://dx.doi.org/10.55349/ijmsnr.2024411623.
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Background: Sudan’s political and economic challenges have increased mental health issues among university students, but access to mental healthcare is limited. Digital health interventions, such as chatbots, could provide a potential solution to inadequate care. This study aimed to evaluate the level of acceptance of a mental health chatbot prototype among university students in Khartoum, Sudan. Materials and Methods: This qualitative study investigated the perspectives of university students regarding a mental health chatbot prototype designed specifically for this research and deployed on Telegram. Twenty participants aged 18+, owning smartphones, and not receiving mental health treatment tested the prototype. Data was collected through individual, face-to-face, in-depth, semi-structured interviews. The data was analysed using both deductive and inductive content analysis methods. Results: Most of the participants acknowledged the importance of mental health but felt that it was an overlooked issue in Sudan. Participants considered the chatbot to be a unique and innovative concept, offering valuable features. They viewed the chatbot as a user-friendly and accessible tool, with advantages such as convenience, anonymity, and accessibility, and potential cost and time savings. However, most participants agreed that the chatbot has many limitations and should not be seen as a substitute for seeing a doctor or therapist. Conclusion: The mental health chatbot was viewed positively by participants in the study. Chatbots can be promising tools for providing accessible and confidential mental health support for university students in countries like Sudan. Long-term studies are required to assess chatbot’s mental health benefits and risks. Keywords: mental health, chatbots, university students, Sudan, young adults
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Lv, Rulan, and Siyi Chen. "The Impact of Chatbot Communication Style and Service Remedies on Consumer Adoption Intention." Frontiers in Business, Economics and Management 14, no. 1 (March 21, 2024): 161–66. http://dx.doi.org/10.54097/e6vj0q02.
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The development of chatbots is very rapid, so far, the research on chatbots mainly focuses on the field of service success, ignoring the fact that chatbot service failure (i.e., making mistakes) is a kind of norm, how to better design chatbots so as to minimize the loss brought by service failure has become a problem that needs to be solved urgently by AI producers nowadays. Based on the chatbot service failure scenario, this paper investigates the different impacts of chatbots adopting two communication styles, social and task, on consumers' adoption intention, and finds that consumers' adoption intention of social (vs. task) chatbots is lower after service failure, in which uniqueness neglect plays a mediating role. In addition, different matching effects between three chatbot service remedies (humor vs. apology vs. compensation) and communication style (social vs. oriented) were explored. Social-oriented chatbot service failure, using humor as a service remedy was superior to compensation. Task-oriented chatbot service failure and the use of compensation as a means of service remediation is superior to humor. Apologizing is less effective than humor and compensation in both social and task-oriented chatbot service failure scenarios. The conclusions of this paper enrich the research on chatbots in the area of service failure and provide effective suggestions for merchants to design and apply chatbots.
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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 (May 15, 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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Li, Jinjie, Lianren Wu, Jiayin Qi, Yuxin Zhang, Zhiyan Wu, and Shuaibo Hu. "Determinants Affecting Consumer Trust in Communication With AI Chatbots." Journal of Organizational and End User Computing 35, no. 1 (August 11, 2023): 1–24. http://dx.doi.org/10.4018/joeuc.328089.
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This paper summarized the factors that influence consumers' trust in AI chatbots and divided it into chatbot-related factors (expertise, anthropomorphism, responsiveness, and ease of use), company-related factors (perceived risk, brand trust, human support), and consumer-related factors (privacy concerns). This research attempts to explore the mechanism of human-AI chatbots trust formation and answer the question of how to promote consumers' trust in AI chatbots. The results found that the chatbot-related factors (expertise, responsiveness, and anthropomorphism) positively affect consumers' trust in chatbots. The company-related factor (brand trust) positively affects consumers' trust in chatbots, and perceived risk negatively affect consumers' trust in chatbots. Privacy concerns have a moderating effect on company-related factors. This study helps deepen the understanding of human-AI chatbots communication trust, constructs a basic model of human-AI chatbots trust, and provides insights for e-commerce enterprises to improve chatbots and enhance consumer trust.
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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 (February 21, 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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Amelia, Amelia, Fani Sartika, Suryani Murad, and Nazmatul Ufra. "Determinants of Customer Satisfaction in Chatbot Use." Almana : Jurnal Manajemen dan Bisnis 8, no. 1 (April 30, 2024): 46–55. http://dx.doi.org/10.36555/almana.v8i1.2345.
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Chatbots are becoming increasingly popular in business due to their ability to deliver immediate responses to customers. However, there is limited empirical evidence focusing on customers’ perspectives. This study aims to investigate the determinants of customer satisfaction in chatbot use as well as provide an overview of chatbot users in Indonesia. The research uses quantitative methods and descriptive types of research, with the number of samples being 150 chatbot users. All data collected met the criteria for validity and reliability and were analyzed descriptively and quantitatively through multiple linear regression methods. The research showed that most of the respondents were male chatbot users, aged 18–29 years, had last used chatbots between 1–3 months ago, and used chatbots to convey their complaints/problems. It is also known that banking chatbots are the most frequently used chatbots. Additionally, perceived usefulness and perceived ease of use are the determinants of customer satisfaction in chatbot use. Of these two factors, perceived usefulness has a dominant influence on chatbot consumer (user) satisfaction in Indonesia.
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Akdemir, Doğan Mert, and Zeki Atıl Bulut. "Business and Customer-Based Chatbot Activities: The Role of Customer Satisfaction in Online Purchase Intention and Intention to Reuse Chatbots." Journal of Theoretical and Applied Electronic Commerce Research 19, no. 4 (October 28, 2024): 2961–79. http://dx.doi.org/10.3390/jtaer19040142.
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In the online shopping context, brands aim to achieve a high level of profit by providing better customer satisfaction by using various artificial intelligence tools. They try creating a satisfactory customer experience by creating a system that provides never-ending customer support by using dialog-based chatbots, especially in the field of customer service. However, there is a lack of research investigating the impact of business and customer-based chatbot activities together on online purchase intention and the intention to reuse chatbots. This research considers the use of chatbots as a marketing tool from both customer and business perspectives and aims to determine the factors that affect the customers’ intention to purchase online and reuse chatbots. Accordingly, the impact on customer satisfaction with chatbot usage, which is based on chatbots’ communication quality and customers’ motivations to use chatbots, on online purchase intention and intention to reuse chatbots was examined. Through an online questionnaire with two hundred and ten participants, employing structural equation modeling, we revealed that customer satisfaction with chatbot usage has a greater impact on the intention to reuse chatbots than on online purchase intentions. In addition, chatbot communication quality has a greater impact on customer satisfaction with chatbot usage than customers’ motivation to use chatbots. To solidify these findings, confirmatory factor analysis, along with reliability and validity assessments, were implemented within the analytical framework to provide robust support for the study’s hypotheses. These findings not only provide empirical evidence and implications for companies in online shopping but also extend the understanding of AI tools in marketing, highlighting their subtle impact on consumer decision-making in the dynamic digital marketplace.
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Saransh, Saransh. "SOCIAL COMPANION CHATBOT FOR HUMAN COMMUNICATION USING ML AND NLP." International Journal of Engineering Applied Sciences and Technology 8, no. 1 (May 1, 2023): 321–24. http://dx.doi.org/10.33564/ijeast.2023.v08i01.048.
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The advent of chatbot technology has led to a significant shift in the humans communicate with machines. Chatbots powered by Machine Learning (ML) and Natural Language Processing (NLP) can interact with humans naturally and conversationally. This chatbot's primary objective is to provide companionship to individuals who may feel lonely or isolated. The chatbot prompts customers to express their feelings and provides a personalized response based on whether the customer's feelings are positive or negative. The chatbot's development involved designing a userfriendly interface and integrating natural language processing techniques to enable more human-like conversations. If the customer's response matches one of the feelings in the respective list, the chatbot responds with empathy and requests the customer to describe their feelings in more detail. Overall, the chatbot enhances customer support by providing personalized communication with customers.
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Mariano, Pamella A. de L., Ana Carolina R. de Souza, Guilherme C. Guerino, Ana Paula Chaves, and Natasha M. C. Valentim. "A Systematic Mapping Study about Technologies for Hedonic Aspects Evaluation in Text-based Chatbots." Journal on Interactive Systems 15, no. 1 (August 18, 2024): 875–96. http://dx.doi.org/10.5753/jis.2024.4350.
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Many studies present and evaluate daily-use systems ranging from information to conversational systems. Chatbots, either text-based or voice-based, have attracted the attention of researchers. In particular, User eXperience (UX) has been pointed out as one of the chatbot's leading aspects of evaluation involving pragmatic and hedonic aspects. Pragmatic aspects deal with the usability and efficiency of the system, while hedonic aspects consider aspects related to the originality, innovation, beauty of the system, and the user's psychological well-being. Even with existing research on usability evaluation and human-computer interaction within conversational systems, there is a clear shortfall in studies specifically addressing the hedonic aspects of user experience in chatbots. Therefore, this paper presents a Systematic Mapping Study that investigates various UX evaluation technologies (questionnaires, methods, techniques, and models, among others), focusing on the hedonic aspect of chatbots. We focused on studies with chatbots that are activated by text, although they may be able to display click interactions, videos, and images in addition to the text modality. We discovered 69 technologies to evaluate hedonic aspects of UX in chatbots, and the most frequent aspect found is the General UX . Our study provides relevant data on the research topic, addressing the specific characteristics of human-chatbot interaction, such as identity and social interaction. Moreover, we highlight gaps in the hedonic aspect evaluation in chatbots, such as a few works investigating the assessment of user emotional state.
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Rieke, Tim, and Helena Martins. "The relationship between motives for using a Chatbot and satisfaction with Chatbot characteristics: An exploratory study." SHS Web of Conferences 160 (2023): 01007. http://dx.doi.org/10.1051/shsconf/202316001007.
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Even though there is a growing number of studies focusing on Chatbots and artificial conversations, research lacks studies analyzing Chatbot characteristics and motives for using this technology. This displays a critical gap in the literature that the present study addresses. This work thus attempts to analyze the relationship between motives for using a Chatbot and satisfaction with Chatbot characteristics. Two questionnaires were developed, one to assess satisfaction with Chatbot characteristics, according to the Kano model and another to assessing motives for using Chatbots, based on previous qualitative research. Survey research was directed to the Portuguese population (N=258) and statistical analysis indicated that motives for using Chatbots do not seem to have a clear relationship with satisfaction with Chatbot characteristics. Furthermore, results suggest that equipping Chatbots with human-like characteristics, seems to be indifferent to Portuguese Millennials; instead, speed and accessibility of Chatbots seem to be valued, especially when using this technology for convenience purposes. A discussion on the possible implications for theory and practice on this topic is presented, and clues for future research are suggested.
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Linus, Kevin Destiny, Samson Isaac, and Amina Bala Ja'afaru. "An Improved Conversational Chatbot Marketing System Case on FedEx." Kasu Journal of Computer Science 1, no. 2 (June 30, 2024): 316–39. http://dx.doi.org/10.47514/kjcs/2024.1.2.0012.
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This paper presents an innovative approach to enhancing conversational chatbot marketing systems. In a digital world, customer engagement and service personalization are paramount, and chatbots play a crucial role in this transformation. Fast forwarding to today's digital fast-paced landscape, enhancing customer interaction and service efficiency using chatbots is paramount for global logistics and courier delivery servicing companies like Federal Express FedEx. This study presents the development and integration of an improved conversational chatbot marketing system using the Rasa architecture framework. The aim of this research is to improve an existing chat bot conversational marketing system (case-on FedEx). The system will be able to give in response more accurately with respect to the user input on the messaging platform. With the vital objectives of integrating the system using RASA a machine learning framework for building conversational AI based Chatbot system. The system should be able to provide links to website home page, FAQs page and logs that the admin can see questions not answer and admin will provide the answer respectively, covering many marketing related areas. Rasa framework was chosen for its robust capabilities in building context-aware and intelligent chatbots.. The results indicate a significant reduction in response times, increased customer satisfaction, and higher engagement rates. The chatbot's ability to handle repetitive tasks and common queries allowed human agents to focus on more complex issues, thereby improving overall efficiency. This case study demonstrates the potential of advanced conversational AI systems in revolutionizing customer service and marketing strategies in the logistics industry, setting a new benchmark for digital transformation initiatives. The future work of this research will focus on further refining the chatbot's capabilities, expanding its scope to include predictive analytics, and exploring its application in other operational areas within FedEx
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Kadasah, Effah Abdullah. "Artificial Intelligence Powered Chatbot for Business." International Journal of Information Technology and Business 4, no. 2 (May 1, 2023): 61–66. http://dx.doi.org/10.24246/ijiteb.422023.61-66.
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Text has become an essential interaction manner between people. The use of chatbots improved quickly in business area including marketing, customer service and e-commerce. Users value chatbots because they are fast, intuitive and convenient. This paper discussed about the artificial intelligence technology that used to develop and implement chatbots which can the organization used to benefit in their businesses. A chatbot is a computer program that can interact with a human by using natural language. The main three areas in business that using chatbot the most are marketing, customer service and e-commerce fields. The roles of chatbot in mentioned areas has been discussed in this paper. AI powered chatbots transform business by reducing costs, increasing revenue and enhancing the customer experience. The benefits and limitation of chatbots have been also discussed in this paper.
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Entenberg, Guido A., Malenka Areas, Andrés J. Roussos, Ana Laura Maglio, Jillian Thrall, Milagros Escoredo, and Eduardo L. Bunge. "Using an Artificial Intelligence Based Chatbot to Provide Parent Training: Results from a Feasibility Study." Social Sciences 10, no. 11 (November 5, 2021): 426. http://dx.doi.org/10.3390/socsci10110426.
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Online parenting training programs have shown to be effective. However, no studies on parent training programs delivered through chatbots have been reported yet. Aim. This study aims to assess the feasibility of delivering parenting skills through a chatbot. Methods. A sample of 33 parents completed a pilot feasibility study. Engagement, knowledge, net-promoters score and qualitative responses were analyzed. Results. A total of 78.8% of the sample completed the intervention. On average, participants remembered 3.7 skills out of the 5 presented and reported that they would recommend the chatbot to other parents (net promoter score was 7.44; SD = 2.31 out of 10). Overall, parents sent a mean of 54.24 (SD = 13.5) messages to the chatbot, and the mean number of words per message was 3. Main themes parents discussed with the chatbot included issues regarding their child’s habits, handling disruptive behaviors, interpersonal development, and emotional difficulties. Parents generally commented on the usefulness of the intervention and suggested improvements to the chatbot’s communication style. Conclusions. Overall, users completed the intervention, engaged with the bot, and would recommend the intervention to others. This suggests parenting skills could be delivered via chatbots.
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Xu, Ying, Nora Bradford, and Radhika Garg. "Transparency Enhances Positive Perceptions of Social Artificial Intelligence." Human Behavior and Emerging Technologies 2023 (September 6, 2023): 1–15. http://dx.doi.org/10.1155/2023/5550418.
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Social chatbots are aimed at building emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts that transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversations between a hypothetical chatbot and a user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to the postobservation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, though these effects were small. Moreover, transparency appeared to have a larger effect on increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users.
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El Azhari, Khadija, Imane Hilal, Najima Daoudi, and Rachida Ajhoun. "SMART Chatbots in the E-learning Domain: A Systematic Literature Review." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 15 (August 9, 2023): 4–37. http://dx.doi.org/10.3991/ijim.v17i15.40315.
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Integrating Artificial Intelligence (AI) technologies implied significant growth in variousdomains. Furthermore, many companies integrate AI technologies into their products toenhance the quality of their services. Chatbots are among the AI technologies widely used inseveral areas, especially E-learning. Chatbots support learners in their learning processes byhelping them to find the appropriate answers to their questions. We aim to conduct a systematicliterature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive andmassive tasks. This article surveys the literature over the period 2016–2022 on the use of AIchatbots in the E-learning domain as they automatically answer learners’ questions. Thus, weidentify, collect, and synthesize multiple research studies on the application of AI chatbots in theE-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in(1) Developing our research questions and (2) Automatically implementing a solution based onPython language to analyze selected papers, highlighting research gaps, and opening new windowsto guide our future works. Our study shows that chatbots effectively interact with learners.However, there are some drawbacks: (1) Educational chatbots are still limited in their localKnowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus,Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update itsKB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich thechatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services suchas speech recognition and sentiment analysis to boost the user experience and make learningeasier. In our SLR, we discuss these limitations and propose some solutions to fill the gap.
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Hong, Hyeonmi, and Sunghee Shin. "Effects of the use of a conversational artificial intelligence chatbot on medical students’ patient-centered communication skill development in a metaverse environment." Journal of Medicine and Life Science 21, no. 3 (September 30, 2024): 92–101. http://dx.doi.org/10.22730/jmls.2024.21.3.92.
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This study investigated how the use of a conversational artificial intelligence (AI) chatbot improved medical students' patient-centered communication (PCC) skills and how it affected their motivation to learn using innovative interactive tools such as AI chatbots throughout their careers. This study adopted a onegroup post-test-only design to investigate the impact of AI chatbot-based learning on medical students' PCC skills, their learning motivation with AI chatbots, and their perception towards the use of AI chatbots in their learning. After a series of classroom activities, including metaverse exploration, AI chatbot-based learning activities, and classroom discussions, 43 medical students completed three surveys that measured their motivation to learn using AI tools for medical education, their perception towards the use of AI chatbots in their learning, and their self-assessment of their PCC skills. Our findings revealed significant correlations among learning motivation, PCC scores, and perception variables. Notably, the perception towards AI chatbot-based learning and AI chatbot learning motivation showed a very strong positive correlation (r=0.72), indicating that motivated students were more likely to perceive chatbots as beneficial educational tools. Additionally, a moderate correlation between motivation and self-assessed PCC skills (r=0.54) indicated that students motivated to use AI chatbots tended to rate their PCC skills more favorably. Similarly, a positive relationship (r=0.68) between students' perceptions of chatbot usage and their self-assessed PCC skills indicated that enhancing students' perceptions of AI tools could lead to better educational outcomes.
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Nehme, Mayssam, Franck Schneider, Esther Amruthalingam, Elio Schnarrenberger, Raphaël Trëmeaud, and Idris Guessous. "Chatbots in medicine: certification process and applied use case." Swiss Medical Weekly 154, no. 10 (October 25, 2024): 3954. http://dx.doi.org/10.57187/s.3954.
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Chatbots are computer programs designed to engage in natural language conversations in an easy and understandable way. Their use has been accelerated recently with the advent of large language models. However, their application in medicine and healthcare has been limited due to concerns over data privacy, ther risk of providing medical diagnoses, and ensuring regulatory and legal compliance. Medicine and healthcare could benefit from chatbots if their scope is carefully defined and if they are used appropriately and monitored long-term. The confIAnce chatbot, developed at the Geneva University Hospitals and the University of Geneva, is an informational tool aimed at providing simplified information to the general public about primary care and chronic diseases. In this paper, we describe the certification and regulatory aspects applicable to chatbots in healthcare, particularly in primary care medicine. We use the confIAnce chatbot as a case study to explore the definition and classification of a medical device and its application to chatbots, considering the applicable Swiss regulations and the European Union AI Act. Chatbots can be classified anywhere from non-medical devices (informational tools that do not handle patient data or provide recommendations for treatment or diagnosis) to Class III medical devices (high-risk tools capable of predicting potentially fatal events and enabling a pre-emptive medical intervention). Key considerations in the definition and certification process include defining the chatbot’s scope, ensuring compliance with regulations, maintaining security and safety, and continuously evaluating performance, risks, and utility. A lexicon of relevant terms related to artificial intelligence in healthcare, medical devices, and regulatory frameworks is also presented in this paper. Chatbots hold potential for both patients and healthcare professionals, provided that their scope of practice is clearly defined, and that they comply with regulatory requirements. This review aims to provide transparency by outlining the steps required for certification and regulatory compliance, making it valuable for healthcare professionals, scientists, developers, and patients.
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Christanti, Viny, Jesslyn Jesslyn, and Fundroo Orlando. "Implementasi Chatbot Pelajaran Sekolah Dasar Dengan Pandorabots." CICES 9, no. 2 (August 18, 2023): 203–13. http://dx.doi.org/10.33050/cices.v9i2.2703.
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Chatbot is a virtual conversation that can receive input in the form of voice or writing. A chatbot can be a generative or retrieval chatbot. The creation of the two chatbots provides an opportunity for users to write text freely. Sometimes giving freedom to type sentences freely can result in answers that are not to the user's liking. There are several ways that users can be directed to write text in the chat column. This study aims to develop a chatbot application that can be used as a medium for questioning and answering subject matter for elementary school students in a structured manner. Chatbots developed based on AIML (Artificial Intelligence Markup Language) use Pandorabots as a platform to process text input from users so that chatbots can direct users to type according to the patterns and knowledge contained in the chatbot. There are two kinds of chatbots created in this study, namely text-based interaction and button-based interaction chatbots. The chatbot test uses 20 data samples in the form of questions and answers from Elementary School subject matter grades 1 to grade 6. Chatbots using AIML can only answer questions that are relevant and according to patterns in AIML. To optimize the performance of text chatbots, we recommend using large amounts of data. Whereas Chatbots with UI Buttons should use less data to work optimally.
Keywords— AIML, button-based, chatbot, Pandorabots, elementary school, text-based
Chatbot merupakan sebuah percakapan virtual yang dapat menerima input berupa suara atau tulisan. Sebuah chatbot dapat merupakan chatbot generative atau retrieval. Pembuatan kedua chatbot tersebut memberikan kesempatan untuk pengguna menulis teks dengan bebas. Terkadang memberikan kebebasan mengetik kalimat dengan bebas dapat menghasilkan jawaban yang tidak sesuai dengan keinginan pengguna. Ada beberapa cara agar pengguna dapat diarahkan untuk menuliskan teks dalam kolom chat. Penelitian ini bertujuan untuk mengembangkan aplikasi chatbot yang dapat digunakan sebagai media tanya-jawab materi pelajaran untuk siswa Sekolah Dasar secara terstruktur. Chatbot yang dikembangkan berbasis AIML (Artificial Intelligence Markup Language) menggunakan Pandorabots sebagai platform untuk memproses input teks dari pengguna sehingga chatbot dapat mengarahkan pengguna untuk mengetikan sesuai pola dan pengetahuan yang ada didalam chatbot. Terdapat dua macam chatbot yang dibuat pada penelitian ini yaitu text-based interaction dan button-based interaction chatbot. Pengujian chatbot menggunakan 20 sampel data berupa soal dan jawaban dari materi pelajaran Sekolah Dasar kelas 1 sampai dengan kelas 6. Chatbot dengan menggunakan AIML hanya dapat menjawab pertanyaan yang relevan dan sesuai dengan pola pada AIML. Untuk mengoptimalkan kinerja Chatbot text, sebaiknya data yang digunakan berjumlah besar. Sedangkan Chatbot dengan UI Button sebaiknya menggunakan data yang sedikit untuk dapat bekerja secara optimal.
Kata Kunci—AIML, button-based, chatbot, Pandorabots, sekolah dasar, text-based
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Tsai, Wan-Hsiu Sunny, Yu Liu, and Ching-Hua Chuan. "How chatbots' social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue." Journal of Research in Interactive Marketing 15, no. 3 (June 18, 2021): 460–82. http://dx.doi.org/10.1108/jrim-12-2019-0200.
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PurposeThis study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue.Design/methodology/approachThis study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand.FindingsThe influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators.Originality/valueThis study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.
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Ramadhan, Farhan Fathur, Andri Sahata Sitanggang, Julian Chandra Wibawa, and Nizar Rabbi Radliya. "Implementation of Digital Marketing Strategy with Chatbot Technology." International Journal of Artificial Intelligence Research 7, no. 2 (November 12, 2023): 132. http://dx.doi.org/10.29099/ijair.v7i2.1006.
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Chatbots are a rapidly growing technology in the field of digital marketing. They are computer programs designed to simulate conversation with human users. Chatbots can be integrated into websites, mobile apps, and messaging platforms to provide instant customer service and support, as well as personalized recommendations and promotions. By using natural language processing (NLP) and machine learning (ML) techniques, chatbots can understand and respond to user input in a human-like manner. They can also be programmed to respond to specific keywords, trigger events, and customer behavior. Chatbot implementation in digital marketing can help companies to improve customer engagement, increase sales and reduce costs. However, the key to successful chatbot implementation is to ensure that the chatbot is designed to meet the specific needs of the target audience and that it is integrated into the overall marketing strategy. This thesis explains the beneficial role of chatbots and shows how chatbots can be integrated into digital marketing strategies.
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Nagargoje, Prof Vivek. "J.A.R.V.I.S Organizational Virtual Assistant." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4664–67. http://dx.doi.org/10.22214/ijraset.2021.35066.
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Chatbots, or conversational interfaces as they are also known, present a new way for individuals to interact with computer systems. Traditionally, to get a question answered by a software program involved using a search engine, or filling out a form. A chatbot allows a user to simply ask questions in the same manner that they would address a human. The most well known chatbots currently are voice chatbots: Alexa and Siri. However, chatbots are currently being adopted at a high rate on computer chat platforms. The technology at the core of the rise of the chatbot is natural language processing (“NLP”). A simple chatbot can be created by loading an FAQ (frequently asked questions) into chatbot software. The functionality of the chatbot can be improved by integrating it into the organization’s enterprise software, allowing more personal questions to be answered, like“When is the meet?”, or “What is the schedule of my day?”. A chatbot can be used as an “assistant” to a live agent, increasing the agent’s efficiency.
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Janssen, Antje, Jens Passlick, Davinia Rodríguez Cardona, and Michael H. Breitner. "Virtual Assistance in Any Context." Business & Information Systems Engineering 62, no. 3 (April 6, 2020): 211–25. http://dx.doi.org/10.1007/s12599-020-00644-1.
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Abstract Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains. Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge.
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Suryanto, Tri Lathif Mardi, Aji Prasetya Wibawa, Hariyono Hariyono, and Andrew Nafalski. "Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems." International Journal of Robotics and Control Systems 3, no. 4 (December 5, 2023): 955–1006. http://dx.doi.org/10.31763/ijrcs.v3i4.1195.
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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.
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Lin, Chien-Chang, Anna Y. Q. Huang, and Stephen J. H. Yang. "A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022)." Sustainability 15, no. 5 (February 22, 2023): 4012. http://dx.doi.org/10.3390/su15054012.
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A conversational chatbot or dialogue system is a computer program designed to simulate conversation with human users, especially over the Internet. These chatbots can be integrated into messaging apps, mobile apps, or websites, and are designed to engage in natural language conversations with users. There are also many applications in which chatbots are used for educational support to improve students’ performance during the learning cycle. The recent success of ChatGPT also encourages researchers to explore more possibilities in the field of chatbot applications. One of the main benefits of conversational chatbots is their ability to provide an instant and automated response, which can be leveraged in many application areas. Chatbots can handle a wide range of inquiries and tasks, such as answering frequently asked questions, booking appointments, or making recommendations. Modern conversational chatbots use artificial intelligence (AI) techniques, such as natural language processing (NLP) and artificial neural networks, to understand and respond to users’ input. In this study, we will explore the objectives of why chatbot systems were built and what key methodologies and datasets were leveraged to build a chatbot. Finally, the achievement of the objectives will be discussed, as well as the associated challenges and future chatbot development trends.