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Joshi, Kalpesh. "AI Mental Health Therapist Chatbot." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 308–11. http://dx.doi.org/10.22214/ijraset.2023.56393.
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Chatbots have become very popular these days as the technology is growing with a very high rate. Due to advancements in the technology chatbots have made our lives easier as we can get to know about many things at our finger tips. So, there are many chatbots available which do the work related to particular things. One such chatbot is ChatGPT, Bard etc. AI chatbots provide a more human like experience with the help of natural language processing and leverage semantics to understand the context of what a person says. Thinking of it we have created a AI Mental Health Therapist Chatbot to provide a medical recommendations according to the problem the user might be facing. It will be able to provide medical support in minimal cost and also recommend the treatment required to the user. This can be a type of advancement in the field of AI which can gain popularity among people. The best AI chatbots can unlock incredible efficiency and also the breadth of AI chatbots available today is incredible.
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Lee, Ju Yoen. "Can an artificial intelligence chatbot be the author of a scholarly article?" Journal of Educational Evaluation for Health Professions 20 (February 27, 2023): 6. http://dx.doi.org/10.3352/jeehp.2022.20.6.
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At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.
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Lee, Ju Yoen. "Can an artificial intelligence chatbot be the author of a scholarly article?" Journal of Educational Evaluation for Health Professions 20 (February 27, 2023): 6. http://dx.doi.org/10.3352/jeehp.2023.20.6.
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At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.
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Lee, Ju Yoen. "Can an artificial intelligence chatbot be the author of a scholarly article?" Science Editing 10, no. 1 (February 16, 2023): 7–12. http://dx.doi.org/10.6087/kcse.292.
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At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.
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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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Temsah, Mohamad-Hani, Fadi Aljamaan, Khalid H. Malki, Khalid Alhasan, Ibraheem Altamimi, Razan Aljarbou, Faisal Bazuhair, et al. "ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations." Healthcare 11, no. 13 (June 21, 2023): 1812. http://dx.doi.org/10.3390/healthcare11131812.
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This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs’ familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.
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Abdallah, Wael, Arezou Harraf, Osama Mosusa, and Abdalmuttaleb Sartawi. "Investigating Factors Impacting Customer Acceptance of Artificial Intelligence Chatbot: Banking Sector of Kuwait." International Journal of Applied Research in Management and Economics 5, no. 4 (January 7, 2023): 45–58. http://dx.doi.org/10.33422/ijarme.v5i4.961.
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The Purpose: This study investigates the role of Artificial Intelligence- chatbot (AI chatbot) quality and AI chatbot users across various banking needs and its impact on customer acceptance of AI chatbots through the mediating role of perceived usefulness and ease of use. Design/methodology/approach – This quantitative study uses a cross-sectional time dimension. The questionnaire of this study was developed using multiple academic sources. Partial least square structural equation modeling was used to analyze the data, and the SmartPLS 4 software was used for the calculation. Finding - The findings indicated a significant positive direct relationship between AI chatbot quality and acceptance of AI chatbot (path coefficient of 0.138 and p-value of 0.022). At the same time, the direct relationship between the AI-chatbot user and the acceptance of the AI chatbot was insignificant (path coefficient = 0.0.096, and p-value = 0.246). While the results of the indirect relationship reveal that perceived usefulness and ease of use partially mediated the relationship between AI chatbot quality and acceptance of AI chatbots. The perceived usefulness and ease of use fully mediated the relationship between AI chatbot users and acceptance of the AI chatbot. Originality/value – The results of this study developed a framework for banking and other customer-oriented businesses in understanding and developing AI chatbots to address customer needs.
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Pandey, Siddhant, Nikhil Chandra Pandey, and Yash Bajaj. "AI Enabled Chatbot." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1761–64. http://dx.doi.org/10.22214/ijraset.2024.60147.
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Abstract: As technology continues its rapid evolution, the integration of Artificial Intelligence (AI) into chatbot systems emerges as a pivotal frontier in reshaping human-computer interaction. This research paper embarks on a comprehensive exploration of the intricate synergy between AI technologies and chatbot development, with a primary focus on elevating user interactions to unprecedented levels of sophistication and intuitiveness. By delving into a nuanced understanding of methodologies, technologies, and challenges, this study aims to provide a holistic perspective on the present state and future potential of AIenabled chatbots. The journey begins with a retrospective analysis of the evolution of chatbots, tracing their trajectory from rulebased systems to the forefront of AI-driven conversational agents. The advent of AI, particularly Natural Language Processing (NLP) and machine learning algorithms, has ushered in a new era, enabling chatbots to not only comprehend but also respond intelligently, mirroring the complexities of human language.
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Kumar, Kartik. "An Educational Chatbot Using AI in Radiotherapy." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 16, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34122.
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The surge in demand for information in cancer centers and hospitals, particularly during the pandemic, overwhelmed the limited manpower available. To address this challenge, there arose a need to develop an educational chatbot tailored for diverse user groups in the field of radiotherapy, including patients and their families, the general public, and radiation staff. Objective: In response to the pressing clinical demands, the primary aim of this endeavor is to delve into the intricacies of designing an educational chatbot for radiotherapy using artificial intelligence.Methods: The chatbot is meticulously crafted using a dialogue tree and layered structure, seamlessly integrated with artificial intelligence functionalities, notably natural language processing (NLP). This adaptable chatbot can be deployed across various platforms, such as IBM Watson Assistant, and embedded in websites or diverse social media channels.Results: Employing a question-and-answer methodology, the chatbot adeptly engages users seeking information on radiotherapy, presenting an approachable and reassuring interface. Recognizing that users, often anxious, may struggle to articulate precise questions, the chatbot facilitates the interaction by offering a curated list of questions. The NLP system augments the chatbot's ability to discern user intent, ensuring the provision of accurate and targeted responses. Notably, the study reveals that functional features, including mathematical operations, are preferred in educational chatbots, necessitating routine updates to furnish fresh content and features.Conclusions: The study culminates in the affirmation that leveraging artificial intelligence facilitates the creation of an educational chatbot capable of disseminating information to users with diverse backgrounds in radiotherapy. Furthermore, the importance of rigorous testing and evaluation, informed by user feedback, is emphasized to iteratively enhance and refine the chatbot's performance. Keywords: AI, machine learning, NLP, chatbot, radiotherapy, IoT, healthcare.
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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.
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Saxena, Aarush. "AI-Based Chatbot." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 941–42. http://dx.doi.org/10.22214/ijraset.2022.47785.
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Abstract: A chatbot is software used to develop interaction between a user/human and a computer/system in natural language, similar to human chats. Chatbots converse with the customer in a discussion following input from a human and a response to the customer. It makes the user believe that he is chatting with a human while chatting with the computer. The chatbot application helps the student to get information about the college admission process and get quick answers from anywhere with an internet connection. This chatbot system reduces the workload of the admissions department by providing students or parents with the information they need and also reduces the workload of the department, which has to constantly answer all the students' questions.
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Vinita, Kumari Pal, Singh Sonali, Sinha Anshita, and Sohail Shekh Mohammad. "Medical chatbot using AI and NLP." i-manager’s Journal on Software Engineering 16, no. 3 (2022): 46. http://dx.doi.org/10.26634/jse.16.3.18551.
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The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.
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Li, Jingquan. "Security Implications of AI Chatbots in Health Care." Journal of Medical Internet Research 25 (November 28, 2023): e47551. http://dx.doi.org/10.2196/47551.
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Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people’s data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.
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Harbola, Aditya. "Design and Implementation of an AI Chatbot for Customer Service." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1295–303. http://dx.doi.org/10.17762/msea.v70i2.2321.
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A company's customer service is very important to its success. It can help boost revenue and retain customers. As digital technology has increased the demand for 24-hour support, businesses are now turning to AI chatbots to provide better and more personalized service. Artificial intelligence (AI) chatbots can help businesses improve the customer experience and reduce the workload of their customer service agents. The paper presents the development and implementation of a chatbot utilizing NLP and AI techniques. It aims to provide efficient and personalized responses to customers' inquiries. The research process involved gathering and analyzing data, developing the chatbot's framework, and carrying out the study. Its architecture and framework were built with the help of NLP and AI. This feature allows the chatbot to respond to users' natural language queries. Its features were also designed to help customers navigate through various tasks and provide recommendations. The chatbot was well-received by its users and was able to provide effective and efficient customer service. The findings of the study indicate that the potential of AI and NLP in enhancing the experience of customers is immense.
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Dinh, Hoa, and Thien Khai Tran. "EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach." Applied Sciences 13, no. 22 (November 17, 2023): 12446. http://dx.doi.org/10.3390/app132212446.
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The digital transformation has created an environment that fosters the development of effective chatbots. Through the fusion of artificial intelligence and data, these chatbots have the capability to provide automated services, optimize customer experiences, and reduce workloads for employees. These chatbots can offer 24/7 support, answer questions, perform transactions, and provide rapid information, contributing significantly to the sustainable development processes of businesses and organizations. ChatGPT has already been applied in various fields. However, to ensure that there is a chatbot providing accurate and useful information in a narrow domain, it is necessary to build, train, and fine-tune the model based on specific data. In this paper, we introduce EduChat, a chatbot system for university-related questions. EduChat is an effective artificial intelligence application designed by combining rule-based methods, an innovative improved random forest machine learning approach, and ChatGPT to automatically answer common questions related to universities, academic programs, admission procedures, student life, and other related topics. This chatbot system helps provide quick and easy information to users, thereby reducing the time spent searching for information directly from source documents or contacting support staff. The experiments have yielded positive results.
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Tjahyana, Lady Joanne. "Exploring AI Chatbot Development for Gen-Z: A Study on First-Time and Experienced Voters in Pemilu." Jurnal Ilmu Komunikasi dan Bisnis 9, no. 2 (April 26, 2024): 224–36. http://dx.doi.org/10.36914/jikb.v9i2.1091.
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Artificial intelligence (AI) chatbots have been utilized as interactive learning platforms for higher education. Gen-Z was a generation that quickly adopted the latest technology. Several Gen-Z students participated in the Pemilu (general elections in Indonesia) for the first time in 2024. Therefore, this research aimed to develop an AI chatbot to give information about Pemilu to Gen-Z voters. The researcher developed the AI chatbot with several steps: data collecting, training, evaluating, refining, and analyzing conversation data. A group of 100 Gen-Z students took part in testing the AI chatbot. The researcher discovered that it was essential to keep refining the AI chatbot by improving the datasets and putting more context in it. Next, the researcher asserted that experienced Gen-Z voters asked more questions, as they have prior knowledge of experiencing the previous elections. Most of the experienced Gen-Z voters asked advanced questions, while most of the first-time Gen-Z voters asked basic questions. Moreover, the AI chatbot was a suitable learning platform for Gen-Z as they used metacognitive strategies to experiment with their questions to get the correct information from the AI chatbot. That action automatically helped the AI chatbot to improve its performance as part of the training step. Abstrak Artificial intelligence (AI) chatbots telah digunakan sebagai platform pembelajaran interaktif pada level pendidikan tinggi. Gen-Z adalah generasi yang cepat mengadopsi teknologi. Beberapa siswa dari kalangan Gen-Z akan berpartisipasi dalam Pemilu untuk pertama kali pada tahun 2024. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan AI chatbot yang dapat memberikan informasi kepada Gen-Z tentang Pemilu. Peneliti mengembangkan AI chatbot melalui beberapa langkah: data collecting, training, evaluating, refining dan analyzing data percakapan. Sebanyak 100 orang mahasiswa Gen-Z berpartisipasi dalam proses testing chatbot. Peneliti menyimpulkan bahwa sangatlah penting untuk tetap melakukan proses refining dengan cara memperbaiki datasets dan menaruh lebih banyak konteks ke dalamnya. Peneliti juga menemukan bahwa experienced voters melontarkan pertanyaan lebih banyak daripada first-time voters. Kemudian, experienced voters juga menanyakan topik yang lebih advanced karena mereka sudah mempunyai pengalaman mengikuti Pemilu, sedangkan first-time voters hanya menanyakan pertanyaan dasar tentang Pemilu. Selain itu, AI chatbots adalah platform yang cocok untuk Gen-Z karena mereka cenderung menggunakan stratgegi metacognitive untuk terus bereksperimen dengan pertanyaan - pertanyaan agar mendapatkan jawaban yang tepat dari chatbot. Hal ini secara otomatis juga akan meningkatkan performa dari chatbot sebagai bagian dari proses training AI chatbot.
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Aiumtrakul, Noppawit, Charat Thongprayoon, Chinnawat Arayangkool, Kristine B. Vo, Chalothorn Wannaphut, Supawadee Suppadungsuk, Pajaree Krisanapan, et al. "Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention." Journal of Personalized Medicine 14, no. 1 (January 18, 2024): 107. http://dx.doi.org/10.3390/jpm14010107.
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Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5–8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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Humphries, Chelsea. "Human Librarian Interviews ChatGPT." Pathfinder: A Canadian Journal for Information Science Students and Early Career Professionals 4, no. 1 (September 8, 2023): 188–96. http://dx.doi.org/10.29173/pathfinder91.
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In an effort to understand how ChatGPT might be used as a scholarly research tool, I conducted an informal, unstructured interview with the AI chatbot. I learned that it generates its responses based on patterns it identifies in its large reservoir of text data; ChatGPT cannot vouch for the accuracy of the information it presents, nor cite its sources. For this reason, instead of being used as an information source in itself, ChatGPT may be better suited as an information assistant, helping researchers design searches for information in other sources in tandem with “human librarians” (as it designates academic librarians like myself). This reflective piece describes and evaluates the AI chatbot using its own generated content and provides a foundation from which librarians can explore the topic of AI chatbots and scholarly research within information literacy classes.
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Ab Razak, Nur Izah, Muhammad Fawwaz Muhammad Yusoff, and Rahmita Wirza O.K. Rahmat. "ChatGPT Review: A Sophisticated Chatbot Models in Medical & Health-related Teaching and Learning." BMSC 19, s12 (November 20, 2023): 98–108. http://dx.doi.org/10.47836/mjmhs.19.s12.12.
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Artificial intelligence (AI) has transformed our interactions with the world, spawning complex apps and gadgets known as intelligent agents. ChatGPT, a chatbot hybrid of AI and human-computer interaction, converse with humans and have a wide range of possible uses. Chatbots have showed potential in the field of medical education and health sciences by aiding learning, offering feedback, and increasing metacognitive thinking among undergraduate and postgraduate students. OpenAI’s ChatGPT, an dvanced language model, has substantially enhanced chatbot capabilities. Chatbots are being used in the medical related field for teaching & learning, mental state categorisation, medication recommendation, health education and awareness. While chatbots have been well accepted by users, further study is needed to fully grasp their use in medical and healthcare settings. This study looked at 32 research on ChatGPT and chatbots in medical-related fields and medical education. Medical education, anatomy, vaccines, internal medicine, psychiatry, dentistry, nursing, and psychology were among the topics discussed in the articles. The study designs ranged from pilot studies to controlled experimental trials. The findings show the exponential growth and potential of ChatGPT and chatbots in healthcare and medical education, as well as the necessity for more research and development in this sector.
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Chow, James C. L., Valerie Wong, Leslie Sanders, and Kay Li. "Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform." Healthcare 11, no. 17 (August 29, 2023): 2417. http://dx.doi.org/10.3390/healthcare11172417.
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Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot’s content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot’s conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.
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Halvankar, Amol. "College Enquiry For Student using AI ChatBot." International Scientific Journal of Engineering and Management 03, no. 03 (March 23, 2024): 1–9. http://dx.doi.org/10.55041/isjem01409.
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A chat bot is a computer program that may initiate conversations between users and other computers. A larger audience can use chatbot technology, which is text-based and safe to use.. Chatbots for university research are developed using AI algorithms that interpret user messages and assess user demands. The aims of the chatbot's responses is to match the user's input while avoiding making oneself physically available to the institution in response to queries. The program responds to the students' inquiries by applying its intelligence. For using this type applications, natural processing language, command line, graphical user interface (GUI), menu driven, form-based, etc. that used in user interfaces TGUI and web-based user interfaces are the most typical types, however sometimes another type of user interface is required. This is where a conversational user interface based on chatbots fits in. One type of bot that has been present on chat systems is the chatbot. The user can interact with them via graphical interfaces, and the trend is in this direction. They often offer a stateful service, meaning that each session's data is saved by the application.
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Hawanti, Santhy, and Khudoiberdieva Munisa Zubaydulloevna. "AI chatbot-based learning: alleviating students' anxiety in english writing classroom." Bulletin of Social Informatics Theory and Application 7, no. 2 (December 5, 2023): 182–92. http://dx.doi.org/10.31763/businta.v7i2.659.
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In the ever-evolving landscape of education, integrating innovative technologies can enhance the learning experience for students. ChatGPT, a cutting-edge language processing tool developed by OpenAI, offers exciting possibilities for teaching writing. This advanced AI model can be a powerful asset in the classroom, providing students with valuable resources and support as they develop their writing skills. Seventy-three college students participated in the quasi-experiment. The findings demonstrate that AI chatbot-based instruction reduces students' anxiety about learning English writing. AI chatbots offer instant feedback, allowing students to correct errors immediately. This quick feedback loop can prevent students from ruminating over their mistakes, thus reducing anxiety. With AI chatbot, students can learn at their own pace. They can take time to understand concepts, practice writing, and receive feedback without feeling rushed. This flexibility can alleviate the pressure of strict deadlines in traditional classroom settings. The findings imply teachers to implement chatbot-based learning in the classroom.
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Patel, Dhruv, NIhal Shetty, Paarth Kapasi, and Ishaan Kangriwala. "College Enquiry Chatbot using Conversational AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 903–15. http://dx.doi.org/10.22214/ijraset.2023.51324.
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Abstract: Chatbots are computer programs that use text or voice-based interfaces to replicate human conversation. They are often used to automate mundane processes, provide customer support, or aid in the retrieval of information. Chatbots are built with a number of strategies that enable them to interpret and respond to user inputs in a more human-like manner. They can be employed in a variety of industries, including e-commerce, healthcare, and banking. We have analysed and compared numerous chatbot strategies in this report to establish the optimal way for our own chatbot project. We reviewed twenty-six papers on chatbot development and assessed the advantages and disadvantages of various strategies. Natural language processing techniques, such as tokenization and named entity recognition, have been shown in our research to be critical for interpreting user inputs. We also discovered that dialogue management methods, such as rule-based and machine learning-based approaches, have an important influence in influencing discussion flow. Furthermore, we discovered that natural language generation techniques, such as template-based and neural network-based methods, are critical in generating effective chatbot responses. We also investigated various services on the market in order to create a functional chatbot for our college. We also emphasized the various applications of chatbots as well as the current hurdles in the industry. Based on these findings, we chose a technique for our own chatbot project that employs advanced natural language processing and machine learning techniques to create more human-like conversations and improve overall user experience.
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Nguyễn Thị Thúy Nga and Mai Ngọc Khôi. "STUDENTS’ USAGE OF CHATBOT IN WRITING ASSIGNMENTS: ISSUES RELATED TO ACADEMIC INTEGRITY." Tạp chí Khoa học Ngoại ngữ, no. 76 (February 26, 2024): 110–29. http://dx.doi.org/10.56844/tckhnn.76.735.
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The rise of artificial intelligence (AI), like ChatGPT chatbot, has generated heated discussions in the education field. While ChatGPT has gained popularity among students for its ability to provide instant and lengthy responses to academic questions, several teachers and researchers have voiced concerns regarding the authenticity and quality of the AI generated or facilitated works. This study investigates students’ attitudes towards using chatbots, their common practices of using chatbots in writing assignments, and the impacts on academic integrity. A questionnaire was issued to 80 first year students enrolling in a Global Citizenship Education course (in which students have Academic Writing module) at an international university in Hanoi. After that, 30 of these participants were interviewed. The preliminary data analysis revealed that while students had overwhelmingly positive attitudes toward the tool, they lacked a thorough understanding of the risks involved in using the chatbot. The study aims to contribute to the conversation of using AI in education as well as provide recommendations for promoting academic integrity while using Chatbots and similar tools. It calls for due attention be paid to developing procedures for or best practices of using Chatbots in the classrooms without compromising authenticity and academic integrity.
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Meng, Jingbo, and Yue (Nancy) Dai. "Emotional Support from AI Chatbots: Should a Supportive Partner Self-Disclose or Not?" Journal of Computer-Mediated Communication 26, no. 4 (May 19, 2021): 207–22. http://dx.doi.org/10.1093/jcmc/zmab005.
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Abstract This study examined how and when a chatbot’s emotional support was effective in reducing people’s stress and worry. It compared emotional support from chatbot versus human partners in terms of its process and conditional effects on stress/worry reduction. In an online experiment, participants discussed a personal stressor with a chatbot or a human partner who provided none, or either one or both of emotional support and reciprocal self-disclosure. The results showed that emotional support from a conversational partner was mediated through perceived supportiveness of the partner to reduce stress and worry among participants, and the link from emotional support to perceived supportiveness was stronger for a human than for a chatbot. A conversational partner’s reciprocal self-disclosure enhanced the positive effect of emotional support on worry reduction. However, when emotional support was absent, a solely self-disclosing chatbot reduced even less stress than a chatbot not providing any response to participants’ stress. Lay Summary In recent years, AI chatbots have increasingly been used to provide empathy and support to people who are experiencing stressful times. This study compared emotional support from a chatbot compared to that of a human who provided support. We were interested in examining which approach could best effectively reduce people’s worry and stress. When either a person or a chatbot was able to engage with a stressed individual and tell that individual about their own experiences, they were able to build rapport. We found that this type of reciprocal self-disclosure was effective in calming the worry of the individual. Interestingly, if a chatbot only reciprocally self-disclosed but offered no emotional support, the outcome was worse than if the chatbot did not respond to people at all. This work will help in the development of supportive chatbots by providing insights into when and what they should self-disclose.
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Le, Xuan Cu. "Inducing AI-powered chatbot use for customer purchase: the role of information value and innovative technology." Journal of Systems and Information Technology 25, no. 2 (June 7, 2023): 219–41. http://dx.doi.org/10.1108/jsit-09-2021-0206.
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Purpose This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention. Design/methodology/approach Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis. Findings Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention. Practical implications The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots. Originality/value The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.
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Kumar, Yulia, Patricia Morreale, Peter Sorial, Justin Delgado, J. Jenny Li, and Patrick Martins. "A Testing Framework for AI Linguistic Systems (testFAILS)." Electronics 12, no. 14 (July 17, 2023): 3095. http://dx.doi.org/10.3390/electronics12143095.
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This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems (AILS), with particular emphasis on the various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, “How should AI be evaluated?” While the Turing test has traditionally been the benchmark for AI evaluation, it is argued that current, publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing-test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Ongoing research has already validated several versions of ChatGPT, and comprehensive testing on the latest models, including ChatGPT-4, Bard, Bing Bot, and the LLaMA and PaLM 2 models, is currently being conducted. The testFAILS framework is designed to be adaptable, ready to evaluate new chatbot versions as they are released. Additionally, available chatbot APIs have been tested and applications have been developed, one of them being AIDoctor, presented in this paper, which utilizes the ChatGPT-4 model and Microsoft Azure AI technologies.
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Aslam, Farhan. "The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations." European Journal of Technology 7, no. 3 (August 15, 2023): 62–72. http://dx.doi.org/10.47672/ejt.1561.
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Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.
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Erfina, Adhitia, and Muhamad Rifki Nurul. "Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language." Data & Metadata 2 (June 7, 2023): 45. http://dx.doi.org/10.56294/dm202345.
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ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.
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Janthakal, Sheetal, G. Mohan Reddy, Stevenson P, K. Shoheb Aqtar, and Amrutha G. S. "AI-Based Chatbot for College Management System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3633–37. http://dx.doi.org/10.22214/ijraset.2023.52059.
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Abstract: The days of solely engaging with a service through a keyboard are over. Users interact with systems more and more through chats and chatbots. A chatbot is a computer program that can converse with humans using Artificial Intelligence in messaging platforms. Every time the chatbot gets input from the user, it saves input and response which helps the chatbot with little initial knowledge to evolve using gathered responses. With increased responses, the precision of the chatbot also gets increases. The ultimate goal of this project is to add a chatbot feature and API for College. This project will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services.
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Chang, Daniel H., Michael Pin-Chuan Lin, Shiva Hajian, and Quincy Q. Wang. "Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization." Sustainability 15, no. 17 (August 27, 2023): 12921. http://dx.doi.org/10.3390/su151712921.
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The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.
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Rathod, Kunal. "Research on Revolutionising Learning with AI-Powered Chatbots and NLP Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 21, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31253.
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The project titled "Revolutionising Learning with AI-Powered Chatbots and NLP Techniques" aims to leverage cutting-edge technology to enhance the learning experience. It features an AI-based chatbot powered by the ChatGPT API, providing learners with a natural and interactive way to seek information and assistance. This chatbot not only responds to text queries but also incorporates voice-to-text and text-to-voice capabilities, akin to Google Assistant, ensuring accessibility for a wider range of users. Furthermore, the project integrates image text recognition, similar to Google Lens, allowing users to extract information from images. For an added dimension, the system enables users to send text commands to the chatbot and receive AI-generated images, making learning more engaging and informative. By combining these features, the project provides a comprehensive and innovative approach to learning by making information accessible through multiple modalities, thus catering to various learning styles and preferences. Whether it's through natural language conversations, voice commands, or image-based queries, this system promises to transform the way users interact with educational content and empower them to explore, learn, and discover in a manner that best suits their individual needs and preferences. Key Words: AI, Chatbots, NLP, ML, Voice-to-Text, Image Text Recognition.
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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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Xia, Zongwen, Ningqin Li, and Xinrui Xu. "A Bibliometric Review of Analyzing the Intellectual Structure of the Knowledge Based on AI Chatbot Application from 2005–2022." Journal of Information Systems Engineering and Management 8, no. 1 (January 31, 2023): 25843. http://dx.doi.org/10.55267/iadt.07.14428.
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This research approaches the problem of artificial intelligence chatbot applications from a new perspective. With the development of innovation, many firms are using artificial intelligence chatbots to manage their business and build relationships with their customers. Thus, this study aims to offer bibliometric assessments of the expanding literature about AI chatbot services. We used the VOS Viewer software to analyze the data based on Scopus from 2005 to 2022. We extracted and examined the data from several AI chatbot service bibliometric reviews. Given the data, we form 571 peer-reviewed papers from the journal. After analyzing the data, the researchers found the most influential work, authors, and co-cited authors on AI chatbots. Similarly, the researchers, based on the author’s co-citation analysis and the intellectual structure, distinguish between “computer science”, “chatbot service”, and “digital health”. Computer science is the most critical discipline regarding AI applications.
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Azevedo, Nádila, Gustavo Aquino, Leonardo Nascimento, Leonardo Camelo, Thiago Figueira, Joel Oliveira, Ingrid Figueiredo, André Printes, Israel Torné, and Carlos Figueiredo. "A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support." Applied Sciences 13, no. 11 (June 2, 2023): 6777. http://dx.doi.org/10.3390/app13116777.
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The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.
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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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Chase, Matthew. "Academic Libraries Can Develop AI Chatbots for Virtual Reference Services with Minimal Technical Knowledge and Limited Resources." Evidence Based Library and Information Practice 19, no. 2 (June 14, 2024): 136–38. http://dx.doi.org/10.18438/eblip30523.
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A Review of: Rodriguez, S., & Mune, C. (2022). Uncoding library chatbots: Deploying a new virtual reference tool at the San Jose State University Library. Reference Services Review, 50(3), 392-405. https://doi.org/10.1108/RSR-05-2022-0020 Objective – To describe the development of an artificial intelligence (AI) chatbot to support virtual reference services at an academic library. Design – Case study. Setting – A public university library in the United States. Subjects – 1,682 chatbot-user interactions. Methods – A university librarian and two graduate student interns researched and developed an AI chatbot to meet virtual reference needs. Developed using chatbot development software, Dialogflow, the chatbot was populated with questions, keywords, and other training phrases entered during user inquiries, text-based responses to inquiries, and intents (i.e., programmed mappings between user inquiries and chatbot responses). The chatbot utilized natural language processing and AI training for basic circulation and reference questions, and included interactive elements and embeddable widgets supported by Kommunicate (i.e., a bot support platform for chat widgets). The chatbot was enabled after live reference hours were over. User interactions with the chatbot were collected across 18 months since its launch. The authors used analytics from Kommunicate and Dialogflow to examine user interactions. Main Results – User interactions increased gradually since the launch of the chatbot. The chatbot logged approximately 44 monthly interactions during the spring 2021 term, which increased to approximately 137 monthly interactions during the spring 2022 term. The authors identified the most common reasons for users to engage the chatbot, using the chatbot’s triggered intents from user inquiries. These reasons included information about hours for the library building and live reference services, finding library resources (e.g., peer-reviewed articles, books), getting help from a librarian, locating databases and research guides, information about borrowing library items (e.g., laptops, books), and reporting issues with library resources. Conclusion – Libraries can successfully develop and train AI chatbots with minimal technical expertise and resources. The authors offered user experience considerations from their experience with the project, including editing library FAQs to be concise and easy to understand, testing and ensuring chatbot text and elements are accessible, and continuous maintenance of chatbot content. Kommunicate, Dialogflow, Google Analytics, and Crazy Egg (i.e., a web usage analytics tool) could not provide more in-depth user data (e.g., user clicks, scroll maps, heat maps), with plans to further explore other usage analysis software to collect the data. The authors noted that only 10% of users engaged the chatbot beyond the initial welcome prompt, requiring more research and user testing on how to facilitate user engagement.
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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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Meng, Jingbo, Minjin (MJ) Rheu, Yue Zhang, Yue Dai, and Wei Peng. "Mediated Social Support for Distress Reduction: AI Chatbots vs. Human." Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (April 14, 2023): 1–25. http://dx.doi.org/10.1145/3579505.
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The emerging uptake of AI chatbots for social support entails systematic comparisons between human and non-human entities as sources of support. In a between-subject experimental study, a human and two types of ostensible chatbots (using a wizard of oz design) had supportive conversations with college students who were experiencing stressful situations during the pandemic. We found that when compared with a less ideal chatbot (i.e., low-contingent chatbot), (1) the human support provider was perceived with more warmth, which directly reduced emotional distress among participants; (2) the ideal chatbot (i.e., high-contingent chatbot) was perceived to be more competent, which activated participants' cognitive reappraisal of their stressful situations and subsequently reduced emotional distress. The human provider and the ideal chatbot did not differ in users' perceived competence or warmth, although the human provider was more effective at activating participants' cognitive reappraisal. This study integrates human communication theories into human-computer interaction work and contributes by positioning and theorizing user perceptions of chatbots in a larger process from support sources with varying communication competence to users' cognitive and emotional responses, and ultimately to the stress outcome. Theoretical and design implications are discussed.
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Hoang, Ngoc Tue, Duong Ngoc Han, and Duc Hanh Le. "Exploring Chatbot AI in improving vocational students’ English pronunciation." AsiaCALL Online Journal 14, no. 2 (December 3, 2023): 140–55. http://dx.doi.org/10.54855/acoj.231429.
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Thanks to technological development, there has been a remarkable leap in the application of artificial intelligence, especially in education. This paper examines the effectiveness of Chatbot Mission Fluent, an AI chatbot, in improving English pronunciation among vocational students in a Hanoi college. Sixty vocational students participate in an A1 English course hosted in a quasi-experimental research design. After the course, participants were interviewed and asked to complete a survey questionnaire to collect their feedback on the AI Chatbot. The experimental group showed notably better English pronunciation than the control group. This research aims to address the knowledge gap regarding the use of AI chatbots as a tool in vocational education. Through this approach, the potential of AI chatbots in improving English pronunciation is carefully explored and emphasized among vocational students. However, this paper also noticed some difficulties in applying and monetarily supporting Missionfluent throughout the process. Overall, this study focuses on the significance of incorporating innovative technologies into language learning programs and highlights the beneficial potential of AI Chatbots’ application in improving vocational students' English pronunciation while acknowledging AI Chatbots’ drawbacks which were discovered in the procedure.
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Kumar, Sheetesh. "Chatbots: A Comprehensive Review of Functionality and Development." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 7, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30316.
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Chatbots are electronic gadgets that respond according to the preferences of the user. It’s widely used by people to get our questions addressed. A chatbot is a piece of software that uses technology to converse with users in their own language. It’s also a sophisticated system that can answer your question right away from anywhere at any time. These days, the technology’s responsive interface is the main reason why so many firms employ it. This paper presents the technology of chatbots and then provides an overview of the results. Chatbot technology is based on artificial intelligence (AI) and natural language processing (NL). We are highlighting the challenges and limitations of the ongoing work and making some suggestions for improvements or additional research. Index Terms—chatbot, chat script, conversational modeling, machine learning, artificial intelligence (AI), natural language processing (NLP), and technology. Chatbots are electronic gadgets that respond according to the preferences of the user. It’s widely used by people to get our questions addressed. A chatbot is a piece of software that uses technology to converse with users in their own language. It’s also a sophisticated system that can answer your question right away from anywhere at any time. These days, the technology’s responsive interface is the main reason why so many firms employ it. This paper presents the technology of chatbots and then provides an overview of the results. Chatbot technology is based on artificial intelligence (AI) and natural language processing (NL). We are highlighting the challenges and limitations of the ongoing work and making some suggestions for improvements or additional research. Index Terms—chatbot, chat script, conversational modeling, machine learning, artificial intelligence (AI), natural language processing (NLP), and technology. Chatbots are electronic gadgets that respond according to the preferences of the user. It’s widely used by people to get our questions addressed. A chatbot is a piece of software that uses technology to converse with users in their own language. It’s also a sophisticated system that can answer your question right away from anywhere at any time. These days, the technology’s responsive interface is the main reason why so many firms employ it. This paper presents the technology of chatbots and then provides an overview of the results. Chatbot technology is based on artificial intelligence (AI) and natural language processing (NL). We are highlighting the challenges and limitations of the ongoing work and making some suggestions for improvements or additional research. Index Terms—chatbot, chat script, conversational modeling, machine learning, artificial intelligence (AI), natural language processing (NLP), and technology.
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Mageira, Kleopatra, Dimitra Pittou, Andreas Papasalouros, Konstantinos Kotis, Paraskevi Zangogianni, and Athanasios Daradoumis. "Educational AI Chatbots for Content and Language Integrated Learning." Applied Sciences 12, no. 7 (March 22, 2022): 3239. http://dx.doi.org/10.3390/app12073239.
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Using advanced artificial intelligence (AI) technology in learning environments is one of the latest challenges for educators and education policymakers. Conversational AI brings new possibilities for alternative and innovative Information and Communication Technologies (ICT) tools, such as ΑΙ chatbots. This paper reports on field experiments with an AI chatbot and provides insights into its contribution to Content and Language Integrated Learning (CLIL). More specifically, this paper presents an experimental use case of an educational AI chatbot called AsasaraBot, designed to teach high school students cultural content in a foreign language, i.e., English or French. The content is related to the Minoan Civilization, emphasizing the characteristic figurine of the Minoan Snake Goddess. The related chatbot-based educational program has been evaluated at public and private language schools in Greece. The findings from these experiments show that the use of AI chatbot technology for interactive ICT-based learning is suitable for learning foreign languages and cultural content at the same time. The AsasaraBot AI chatbot has been designed and implemented in the context of a postgraduate project using open-source and free software.
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Raditya, Andreas Gandhi Raka. "Kajian Teologi Pastoral terhadap Artificial Intelligence dalam Praktek-praktek Religius." Proceedings of The National Conference on Indonesian Philosophy and Theology 2, no. 2 (September 1, 2024): 388–407. http://dx.doi.org/10.24071/snf.v2i2.8508.
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Lately, artificial intelligence has been rapidly advancing and is increasingly being utilized, both by experts in various fields and by the public in common. This article provides a study of the theology of pastoral on the specific use and influence of AI in ChatGPT. By reviewing the utilization of chatbot in various religious practices such as sermon creation, spiritual guidance, and access to religious teachings, this research explores the extent to which chatbot can assist in religious practices. This research includes evaluation of its usage and consideration of its risks associated with the use of AI technology particularly in religious practices. Exploring AI chatbots' potential could help technology as catalysts for transformation of religious practices nowaday. Furthermore, we can direct technology as a tool to enrich faith and the spiritual dimensions of humanity in this digital era.
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Mutyara, M. Reza Aldiansyah, Haryaji Catur Putera Hasman, Alfifto Alfifto, and Mutya Rahmi Darmansyah. "Pengaruh Kompetensi dan Kredibilitas Chatbot AI Terhadap Kepercayaan Penggunaan Chatbot AI Pada Pengguna Lazada Di Kota Medan." Eqien - Jurnal Ekonomi dan Bisnis 13, no. 01 (March 16, 2024): 1–13. http://dx.doi.org/10.34308/eqien.v13i01.1692.
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This research aims: (1) To determine the influence of AI Chatbot on purchase intentions of Lazada users in the city of Medan; (2) To determine the effect of trust on purchase intentions among Lazada users in the city of Medan. (3) To determine the effect of AI Chatbot on purchase intention with trust as a mediating variable among Lazada users in the city of Medan. This research method uses quantitative methods. The research sample was 5 sub-districts represented by 170 respondents. Research results: (1) Chatbot AI has a positive and significant effect on Trust among Lazada users in the city of Medan; (2) Trust has a positive and significant effect on Purchase Intention of Lazada users in the city of Medan. (3) AI chatbot has a positive and significant effect on Purchase Intention of Lazada users in the city of Medan.
Keywords: Influence, Competence, Credibility, Chatboi AI.
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Peng, Mary L., Jeffrey A. Wickersham, Frederick L. Altice, Roman Shrestha, Iskandar Azwa, Xin Zhou, Mohd Akbar Ab Halim, et al. "Formative Evaluation of the Acceptance of HIV Prevention Artificial Intelligence Chatbots By Men Who Have Sex With Men in Malaysia: Focus Group Study." JMIR Formative Research 6, no. 10 (October 6, 2022): e42055. http://dx.doi.org/10.2196/42055.
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Background Mobile technologies are being increasingly developed to support the practice of medicine, nursing, and public health, including HIV testing and prevention. Chatbots using artificial intelligence (AI) are novel mobile health strategies that can promote HIV testing and prevention among men who have sex with men (MSM) in Malaysia, a hard-to-reach population at elevated risk of HIV, yet little is known about the features that are important to this key population. Objective The aim of this study was to identify the barriers to and facilitators of Malaysian MSM’s acceptance of an AI chatbot designed to assist in HIV testing and prevention in relation to its perceived benefits, limitations, and preferred features among potential users. Methods We conducted 5 structured web-based focus group interviews with 31 MSM in Malaysia between July 2021 and September 2021. The interviews were first recorded, transcribed, coded, and thematically analyzed using NVivo (version 9; QSR International). Subsequently, the unified theory of acceptance and use of technology was used to guide data analysis to map emerging themes related to the barriers to and facilitators of chatbot acceptance onto its 4 domains: performance expectancy, effort expectancy, facilitating conditions, and social influence. Results Multiple barriers and facilitators influencing MSM’s acceptance of an AI chatbot were identified for each domain. Performance expectancy (ie, the perceived usefulness of the AI chatbot) was influenced by MSM’s concerns about the AI chatbot’s ability to deliver accurate information, its effectiveness in information dissemination and problem-solving, and its ability to provide emotional support and raise health awareness. Convenience, cost, and technical errors influenced the AI chatbot’s effort expectancy (ie, the perceived ease of use). Efficient linkage to health care professionals and HIV self-testing was reported as a facilitating condition of MSM’s receptiveness to using an AI chatbot to access HIV testing. Participants stated that social influence (ie, sociopolitical climate) factors influencing the acceptance of mobile technology that addressed HIV in Malaysia included privacy concerns, pervasive stigma against homosexuality, and the criminalization of same-sex sexual behaviors. Key design strategies that could enhance MSM’s acceptance of an HIV prevention AI chatbot included an anonymous user setting; embedding the chatbot in MSM-friendly web-based platforms; and providing user-guiding questions and options related to HIV testing, prevention, and treatment. Conclusions This study provides important insights into key features and potential implementation strategies central to designing an AI chatbot as a culturally sensitive digital health tool to prevent stigmatized health conditions in vulnerable and systematically marginalized populations. Such features not only are crucial to designing effective user-centered and culturally situated mobile health interventions for MSM in Malaysia but also illuminate the importance of incorporating social stigma considerations into health technology implementation strategies.
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Hmoud, Mohammad, Hadeel Swaity, Eman Anjass, and Eva María Aguaded-Ramírez. "Rubric Development and Validation for Assessing Tasks' Solving via AI Chatbots." Electronic Journal of e-Learning 22, no. 6 (May 17, 2024): 01–17. http://dx.doi.org/10.34190/ejel.22.6.3292.
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This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential. This investigation involved a rigorous process including expert involvement to ensure content validity, as well as the application of statistical tests for assessing internal consistency and reliability. Factor analysis also revealed two significant domains, "Quality of Content" and "Quality of Expression", which further enhanced the construct validity of the evaluation scale. The results from this investigation robustly affirm the reliability and validity of the developed rubric, thus marking a significant advancement in the sphere of AI chatbot performance evaluation within educational contexts. Nonetheless, the study simultaneously emphasizes the requirement for additional validation research, specifically those entailing a variety of tasks and diverse AI chatbots, to further corroborate these findings. The ramifications of this research are profound, offering both researchers and practitioners engaged in chatbot development and evaluation a comprehensive and validated framework for the assessment of chatbot performance.
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Garcia Valencia, Oscar A., Charat Thongprayoon, Caroline C. Jadlowiec, Shennen A. Mao, Jing Miao, and Wisit Cheungpasitporn. "Enhancing Kidney Transplant Care through the Integration of Chatbot." Healthcare 11, no. 18 (September 12, 2023): 2518. http://dx.doi.org/10.3390/healthcare11182518.
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Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.
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Bryant, Antony. "AI Chatbots: Threat or Opportunity?" Informatics 10, no. 2 (June 12, 2023): 49. http://dx.doi.org/10.3390/informatics10020049.
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Raza, Asif, Mirza Adnan Baig, Mustafa Latif, Muhammad Ali Akhtar, Muhammad Umer Farooq, and Waseemullah. "Enabling Context-based AI in Chatbots for conveying Personalized Interdisciplinary Knowledge to Users." Engineering, Technology & Applied Science Research 13, no. 6 (December 5, 2023): 12231–36. http://dx.doi.org/10.48084/etasr.6313.
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Adaptable chatbots have revolutionized user interactions by dynamically tailoring responses to users' knowledge and explainability preferences in interdisciplinary domains, such as AI in education and medicine. While strides have been made in model explainability, little attention has been paid to making models contextually aware and responsive to users' background knowledge. This study investigated interdisciplinary knowledge learning principles in a different domain, where the chatbot's contextual understanding is enhanced through dynamic knowledge graphs that capture users' past interactions to deliver up-to-date and relevant responses. By incorporating explainability features, chatbot responses become enriched, enabling users to understand the reasoning behind answers. This study proposed a model that showed superior chatbot performance and accurately addressed most queries, outperforming competitor chatbots DBpedia, ODC, and ODA. A 0.7 F-measure showcased its excellence, attributed to dynamic knowledge graphs and explainability checks. This study envisions a new era of conversational AI that not only meets user needs but also fosters a deeper understanding of AI decision-making, making it indispensable in delivering personalized, informed, and contextually aware interactions.
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Iyamuremye, Aloys, and Kizito Ndihokubwayo. "Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT." Educational Journal of Artificial Intelligence and Machine Learning 1, no. 1 (April 1, 2024): 1–13. http://dx.doi.org/10.58197/prbl/9hk37296.
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This research explored the impact of using ChatGPT, a chatbot-assisted artificial intelligence (AI), on students’ interest and conceptual understanding of atomic structure and chemical bonding. A total of 92 senior and two secondary students, including 61 male and 31 female students from Rwanda, were randomly selected to participate in the study. The study used a sequential explanatory research design to collect and analyze data. A pre and post-achievement test was used to collect quantitative before and after using ChatGPT. On the other hand, focus group discussion was used to collect qualitative data to explore students’ interest in using chatbots. Repeated measure ANOVA was used to analyze quantitative data from achievement tests, while thematic analysis was used to analyze qualitative data from focus group discussions. The results revealed that the generated response from the use of chatbot-AI is an effective method to supplement other active methods of teaching and learning atomic structure and chemical bonding. In this regard, the students’ performance increased by 16.6% after the use of chatbot-AI. However, the study did not find a statistically significant difference between male and female students after using ChatGPT. Thematic analysis revealed three overarching themes. In this line, the use of ChatGPT stimulates students’ engagement and interactivity, curiosity and motivation, and enhanced learning experience. Educators and policymakers were recommended to integrate this technology into the curriculum to supplement other active teaching and learning pedagogies.