Intelligent Conversational Agents Research Articles

Mobile/VR/Robotics/IoT-Based Chatbots and Intelligent Personal Assistants for Social Inclusion

Chatbots are dialogue systems that utilize computational linguistics (CL), including automatic speech recognition (ASR) and natural language processing (NLP). Artificially intelligent conversational agents combine personalization, interoperability, and scalability with the aim of promoting safe information monitoring, management, and retrieval. AI chatbots offer user-friendly engagement for innovative e-learning, care assistance, and multilingual digital content creation, promoting inclusiveness and AI-enhanced communication. They are a breakthrough for future societies and economies as they can offer cost-effective, tailor-made, and instant exchange of knowledge and skills, including on-site assistance, health monitoring, and e-consultation. AI chatbots are constantly improving to the benefit of their users. Their economic and social impact is expected to rise as individuals, especially vulnerable populations, are trained to use them for lifelong learning, decision-making, and problem-solving.

Campus Companion : Creating a Supportive Chat – Assistant for Students

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

Asynchronous Platform Mediated by Intelligent Conversational Agents for Enhancing Digital Connectivity

Asynchronous Platform Mediated by Intelligent Conversational Agents for Enhancing Digital Connectivity

ChatGPT, Metaverses and the Future of Transdisciplinary Communication

The emergence of artificial intelligence (AI) and virtual reality (VR) technologies has propelled human-machine interaction and immersive experiences to unprecedented levels. Current discussions revolve around Trans-disciplinarity, transdisciplinary communication, metaverses2, and a paradigm shift ushered in by ChatGPT, an intelligent conversational agent powered by OpenAI’s GPT-3.5 architecture. Its integration into VR and metaverses for education and transdisciplinary communication is also being explored. The objective is to discuss and reframe ethical issues arising within the global discourse surrounding technology adaptation, and the innate promises, potential, and pitfalls. By examining the technical and ethical implications of such a potential integration, the paper aims to uncover the broader contexts of these advancements. A deeper understanding of the challenges ahead can be achieved by critically examining the risks and opportunities presented by ChatGPT, virtual realities, and metaverses. The paper emphasizes the need for initiative-taking measures to guide the development and deployment of these transformative technologies. Furthermore, the intersection of AI, VR, metaverses, education, transdisciplinarity, and transdisciplinary communication will be investigated and examined.

A Reinforcement Learning Approach for Intelligent Conversational Chatbot For Enhancing Mental Health Therapy

Mental health therapy plays a crucial role in addressing the growing challenges of mental health disorders. However, barriers such as limited accessibility and social stigma often hinder individuals from seeking timely and effective support. Advancement in the field of Artificial Intelligence (AI) and Natural Language Processing (NLP) has enabled the development of Chatbots which can simulate counseling conversations and provide support to those who seek help. However, existing chatbots often lack contextual understanding, limit user responses to multiple-choice questions, and face challenges in generating human-like responses. In this work, we present an Intelligent Conversational Agent using Reinforcement Learning with Human Feedback (RLHF) for mental health therapy support. The proposed system employs reinforcement learning to optimize its responses and adapts its conversational strategies to better understand user's responses, analyze the emotions expressed, and provide personalized therapeutic interventions to enhance Mental Health Therapy.

Scientometric analysis on the use of ChatGPT, artificial intelligence, or intelligent conversational agent in the role of medical training

IntroductionIn recent times, there has been a noticeable surge in the usage of artificial intelligence, including ChatGPT and other types, in the field of health sciences education. In this regard, an exploratory bibliometric study was carried out to examine the utilization of smart conversational agents, ChatGPT, and artificial intelligence bots in medical education. MethodsA retrospective, observational, cross-sectional bibliometric analysis was employed to assess the scientific publications listed in Scopus. This study was conducted on March 11, 2023 in search for information in Scopus. A total of 220 relevant documents were identified that were available in the Scopus database during the period between 2017 and 2022. Elsevier's SciVal software was used. Subsequently, statistical tables and graphs were prepared for presentation in Bibliometrix software. ResultsAmong the authors, Timothy W. Bickmore, from the United States, has the highest number of publications (10) and citations received (172), and an h-index of 45, suggesting a significant influence in the field of study. The subcategory with the highest academic output is Health Informatics with 133 publications, while Geriatrics and Gerontology has the least with only 3. Most of the analyzed publications (44.2%) originated from collaborations within the same country. Notably, the Swiss Federal Institute of Technology Zurich and Imperial College London stood out with 12 publications each that received over 200 citations indicating their significant impact on their respective fields. Despite having the highest number of academic publications (15), Brazil had a relatively low field-weighted citation impact (0.64) and received the lowest number of citations (81). A clustering analysis was performed on a sample of 10 concepts using 2 dimensions. The results indicated that all terms were part of the same cluster. Notably, the terms 'conversational agents', 'chatbots', 'conversational agent', and 'chatbot' were closely related. ConclusionsIt was found that the American Bickmore, Timothy W., led the top-10 researchers, and that the Health Informatics subject area was the most predominant. However, Brazil and Germany were the leading countries in terms of research output that was mainly published in high impact journals (Q1).

From multimodal features to behavioural inferences: A pipeline to model engagement in human-robot interactions.

Modelling the engaging behaviour of humans using multimodal data collected during human-robot interactions has attracted much research interest. Most methods that have been proposed previously predict engaging behaviour directly from multimodal features, and do not incorporate personality inferences or any theories of interpersonal behaviour in human-human interactions. This work investigates whether personality inferences and attributes from interpersonal theories of behaviour (like attitude and emotion) further augment the modelling of engaging behaviour. We present a novel pipeline to model engaging behaviour that incorporates the Big Five personality traits, the Interpersonal Circumplex (IPC), and the Triandis Theory of Interpersonal Behaviour (TIB). We extract first-person vision and physiological features from the MHHRI dataset and predict the Big Five personality traits using a Support Vector Machine. Subsequently, we empirically validate the advantage of incorporating personality in modelling engaging behaviour and present a novel method that effectively uses the IPC to obtain scores for a human's attitude and emotion from their Big Five traits. Finally, our results demonstrate that attitude and emotion are correlates of behaviour even in human-robot interactions, as suggested by the TIB for human-human interactions. Furthermore, incorporating the IPC and the Big Five traits helps generate behavioural inferences that supplement the engaging behaviour prediction, thus enriching the pipeline. Engagement modelling has a wide range of applications in domains like online learning platforms, assistive robotics, and intelligent conversational agents. Practitioners can also use this work in cognitive modelling and psychology to find more complex and subtle relations between humans' behaviour and personality traits, and discover new dynamics of the human psyche. The code will be made available at: https://github.com/soham-joshi/engagement-prediction-mhhri.

An intelligent conversational agent for educating the general public about HIV

The article presents a Spanish conversational agent that focuses on raising awareness about HIV. The agent aims to provide natural communication, personalized information based on user requests, and a centralized source of information about HIV. The core of the agent’s logic is formed by a natural language understanding conversational model, supported by a knowledge base of medical responses and real conversations with users. An empirical study was conducted with 71 users to evaluate the agent’s effectiveness as a sexual health educational tool. The results show that HIV knowledge raised by 18.44% after using the agent. That, and the positive user experience support the agent’s role as a tool for raising HIV prevention and awareness.

Perception and Acceptance of Medical Chatbot Among Undergraduates in Ekiti State University, Nigeria

This study explores the perceptions and acceptance of medical chatbots among undergraduate students at Ekiti State University, Ado-Ekiti, Nigeria. A medical chatbot is an artificially intelligent conversational agent that simulates human-like communication, catering to user inquiries and generating logical responses. These chatbots leverage natural language processing and machine learning to engage in dynamic interactions, retrieve relevant information, and adapt to new data. This research investigates two primary aspects: the perception of undergraduate students towards the use of medical chatbots and the level of acceptance of these chatbots among the same demographic. The study employs a descriptive cross-sectional survey design, involving a sample size of 300 undergraduate students, determined using Taro Yamane's method. The data collection process includes a semi-structured questionnaire, validated by experts in Tests and Measurement and Public Health. The collected data are analyzed using SPSS version 28. The findings reveal an equitable gender distribution among participants, with a slightly higher representation of females. Additionally, a substantial proportion of respondents fall within the 18-25 age bracket, with a significant presence of undergraduates below 18 years old. The study indicates positive perceptions of medical chatbots among undergraduate students, suggesting a favorable view towards their adoption. While the majority of participants exhibit acceptance of medical chatbots, there is skepticism about the precision and reliability of healthcare suggestions provided by these platforms. In conclusion, this study sheds light on the positive perceptions and acceptance of medical chatbots among undergraduate students in Ekiti State University. The findings suggest a potential for integrating this technology into healthcare, education, and research endeavors, while acknowledging the need for further investigation into the underlying factors influencing these perceptions. As the healthcare landscape evolves, chatbots can potentially offer valuable contributions to enhancing health services, especially in scenarios where in-person visits are unnecessary. However, continuous research is essential to ensure their accuracy, trustworthiness, and effectiveness across diverse demographic groups

An Intelligent Conversational Agent for the Legal Domain

An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it.

Semantic and pragmatic precision in conversational AI systems.

For a conversational agent, to display intelligent interactive behavior implies the ability to respond to the user's intentions and expectations with correct, consistent and relevant actions with appropriate form and content in a timely fashion. In this paper, we present a data-driven analytical approach to embed intelligence into a conversational AI agent. The method requires a certain amount of (ideally) authentic conversational data, which is transformed in a meaningful way to support intelligent dialog modeling and the design of intelligent conversational agents. These transformations rely on the ISO 24617-2 dialog act annotation standard, and are specified in the Dialogue Act Markup Language (DiAML), extended with plug-ins for articulate representations of domain-specific semantic content and customized communicative functionality. ISO 24617-2 is shown to enable systematic in-depth interaction analysis and to facilitate the collection of conversational data of sufficient quality and quantity of instances of interaction phenomena. The paper provides the theoretical and methodological background of extending the ISO standard and DiAML specifications for use in interaction analysis and conversational AI agent design. The expert-assisted design methodology is introduced, with example applications in the healthcare domain, and is validated in human-agent conversational data collection experiments.

Lessons Learned from Modeling the Interaction with Conversational Agents

Intelligent conversational agents have become widespread. Inspired by conversations in natural language, they present different degrees of intelligence and autonomy, bringing challenges for Human-Computer Interaction (HCI). One such challenge concerns design languages for modeling user-agent interaction. We focus here on MoLIC, a design-phase dialogue model based on Semiotic Engineering theory devised to represent user-system interactions as conversations. We performed two case studies with MoLIC interaction diagrams representing two conversational agents – the ANA chatbot and Samsung Bixby. We examined how the interactive aspects of these agents could be expressed in MoLIC. Although it was possible to express the general interaction, our results showed limitations related to the language expressiveness or its inadequacy to represent these systems. We identified limitations in the applicability of MoLIC in modeling and pondered on how to extend or adapt it; directing the HCI community to issues and initiatives that can help design and model these technologies.

A System for the Improvement of Educational Assessment Using Intelligent Conversational Agents

A System for the Improvement of Educational Assessment Using Intelligent Conversational Agents

Curb your enthusiasm: Examining the customer experience with Alexa and its marketing outcomes

Interactive Voice Assistants (IVAs) are intelligent conversational agents capable of communicating with users using natural language. Although IVAs are more frequent in our lives, customer experience research with these agents is still in its infancy. This article aims to identify the factors that form the customer experience (CX) with Alexa and assesses its impact on traditional marketing outcomes: satisfaction and recommendation. This research presents a conceptual model of CX with IVAs and an empirical validation of the model using Structural Equation Modelling based on a sample of 580 IVA users. The results confirm that CX is a multidimensional higher-order construct composed of six factors (usefulness, ease of use, trust, privacy concerns, communication skills, and enjoyment). We also highlight the positive impact of experience on satisfaction and recommendation. Finally, we test the enthusiasm moderating role, showing its negative influence on the investigated relationships. Theoretical and practical implications are discussed.

An innovative integrated platform of telerehabilitation for patients with chronic diseases: development and initial implementation

Although physical exercise is recommended to maintain health and to counteract the deleterious effects of chronic diseases, home exercising is rarely performed autonomously by patients. Hence, there is urgency to introduce and validate rehabilitation protocols based on a combination of technological solutions and specific health requirements in order to guarantee continuity and quality of care to patients with chronic diseases [1]. The aim of this presentation is to describe the development and initial implementation of an integrated platform of telerehabilitation, based on information and communication technologies. The platform is the product of a Lombardy Region grant (Regione Lombardia, Programma Operativo Regionale 2014-2020, “Bando Call HUB Ricerca e Innovazione”) to Istituti Clinici Scientifici Maugeri IRCCS. Preliminary results have been obtained in five healthy subjects testing the feasibility of the integrated platform in a lab setting (Fig. 1). The platform includes an @home fully-integrated kit composed of: 1) wearable inertial devices, 2) digitally-controlled resistance band, 3) time-of-flight range camera 4) mini-PC with exergames. The @home kit is connected with: 4) cloud-based software systems for programming and monitoring exercises, 5) intelligent conversational agents (chatbot) and virtual coach on personal smartphones. The exergames are based on Otago- inspired exercises [2], including strength, balance, aerobic and cognition. The hardware of the exergames consist of a mini-PC, to be connected to a home TV set, eight inertial measurement units (IMUs, two for proximal and distal part of each limb), providing both accelerometer and gyroscope assessments, a 3D motion range camera, and an extensible band with programmable resistance, exerted by an electric motor. This arrangement allows to detect and record the movement patterns of the subjects. A Digital Support System (DSS) in cloud allows the remote definition and supervision of exercising programs on a deferred basis, thus reducing operator time. An assisted interaction with subjects is allowed through a chatbot and a smartphone app allowing not only to monitor completed activities and achieved progresses but also to receive reminders and advice for the upcoming planning activities. The game interface and an avatar of the subject, detected by a range camera, are shown on a TV screen. Operating together, IMUs and the range camera can not only track the movement, but also assess it (as speed and joint range of motion). The DSS operates in an asynchronous mode allowing the continuous remote supervision of the rehabilitation activities, with the possibility to modify the rehabilitation program and progressively adapting it to subject’s performance. The chatbot and the smartphone app allow continuous and assiduous first level contact with the subject. The prototype of the platform is innovative as it is based on algorithms both for evaluations and classification of the results data on a deferred basis at variance with the existing telerehabilitation devices. The platform provides also rehabilitation assistance and advice, likely reducing the need of repeated consultations with the physiotherapist. Finally, this intervention technology might take part in the creation of a standardized evaluation and rehabilitation prescription system, leading to better quality of healthcare assistance.

Perspectives on Socially Intelligent Conversational Agents

The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.

A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions.

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Editorial Issue 33.2

This issue contains five papers. In the first paper, Ege Tekgün, Muhtar Çağkan Uludağlı, Hüseyin Akcan, and Burak Erdeniz, from Izmir University of Economics in Turkey assess the influence of virtual avatar anthropomorphism and the synchronicity of the visuo-tactile stimulation on self-location using a virtual reality (VR) full-body illusion (FBI) experiment. During the experiment, half of the 36 participants observed a gender-matched full-body humanoid avatar from a first-person perspective (1PP) and the other half observed a less anthropomorphic full-body cubical avatar from 1PP while they were receiving synchronous and asynchronous visuo-tactile stimulation. Results show a significant main effect of the synchronicity of the visuo-tactile stimulation and avatar body type on self-location but no significant interaction was found between them. Moreover, the results of the self-report questionnaire provide additional evidence showing that participants who received synchronous visuo-tactile stimulation, experienced not only greater changes in the feeling of self-location, but also, increased ownership, and referral of touch. In the second paper, Ke Li, Qian Zhang, Jinyuan Jia, from Tongji University in Shanghai, China, and Hantao Zhao, from Southeast University in Nanjing, all in China discuss the technology of presenting building information modeling (BIM) with an online platform and the difficulty to display large-scale BIM scenes in a flawless manner on mobile browsers, due to network bandwidth and browser performance limitations. The authors propose CEBOW, a Cloud-Edge-Browser Online architecture for visualizing BIM components with online solutions. The method combines transmission scheduling, cache management, and optimal initial loading into a single system architecture. For network transmission testing, BIM scenes are used, and the results show that their method effectively reduces scene loading time and networking delay while improving the visualization effect of large-scale scenes. In the third paper, Yuzhu Dong and Eakta Jain, from University of Florida in Gainesville, and Sophie Jörg, from Clemson University, all in United States discuss how the importance of eyes for virtual characters stems from the intrinsic social cues. They emphasize that the eye animation impacts the perception of an avatar's internal emotional state. They present three large scale experiments that investigate the extent to which viewers can identify if an avatar is scared. The authors find that participants can identify a scared avatar with 75% accuracy using cues in the eyes including pupil size variation, gaze, and blinks. Because eye trackers return pupil diameter in addition to gaze, their experiments inform practitioners that animating the pupil correctly will add expressiveness to a virtual avatar with negligible additional cost. These findings also have implications for creating expressive eyes in intelligent conversational agents and social robots. The fourth paper, by Osman Güler, from TUSAŞ Şehit Hakan Gülşen Vocational and Technical Anatolian High School in Ankara and Serkan Savaş, from Çankırı Karatekin Üniversitesi, both in Turkey present a study showing that Interactive Boards (IBs) have the necessary hardware to run Stereoscopic 3D (S3D) training materials, but the panel has not got an S3D imaging feature. Therefore, only the Anaglyph S3D imaging method can be applied to IBs. Thus, an Anaglyph S3D training material was prepared for the interaction of the skeletal system and interactive 3D material design for IBs with its effects in education was investigated. A Likert-type scale was developed to measure the usability of the training material on IBs and the material was evaluated by 20 experts. The data were analyzed by the SPSS statistical program and the results were interpreted. According to the results, educational material seems to be positive in terms of image characteristics, content, navigation, and ease of use, font sizes were moderate for readability, the feedback process and the help menu were moderately effective. The last paper by Alexandra Sierra, Marie Postma, from Tilburg University in Netherlands and Menno Van Zaanen, from North-West University in Potchefstroom, South Africa investigate whether the uncanny valley effect, which has already been found for the human-like appearance of virtual characters, can also be found for animal-like appearances. They conducted an online study in which six different animal designs were evaluated in terms of the following properties: familiarity, commonality, naturalness, attractiveness, interestingness, and animateness. The study participants differed in age (under 10–60 years) and origin (Europe, Asia, North America, and South America). For the evaluation of the results, the authors ranked the animal-likeness of the character using both expert opinion and participant judgments. They also investigated the effect of movement and morbidity. The results confirm the existence of the uncanny valley effect for virtual animals, especially with respect to familiarity and commonality, for both still and moving images. No uncanny valley effect was detected for interestingness and animateness.

Smart Speakers: The Next Frontier in mHealth.

The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection.

Evaluating Quality of Chatbots and Intelligent Conversational Agents of BCA (VIRA) Line

Background: The use of technology in the financial sector greatly supports its productivity. Including the use of Artificial Intelligence or Artificial Intelligence that makes activities in the financial sector faster and more efficient. PT. Bank Central Asia also implemented Artificial Intelligence technology by launching a chatbot feature called VIRA. Although this vira provides various conveniences this feature has a disadvantage of this feature is that the account can be accessed without using a pin, but only with a card number and OTP code. Aim: This research aims to look at the legal protection of the use of VIRA chatbots in BCA customers' banking activities. Method: This research is included in the type of empirical juridical legal research with library research and vira application observation. Findings: The results showed that BCA customers can only access personal data using VIRA. The main facilities of chatbot VIRA are more general information services and promotions for customers. In addition, the account is not automatically logged out, so when the phone is lost others may be able to access the account of the mobile phone owner through VIRA.