Chatbot Research Articles

The Effect of AI Chatbots on Consumer Engagement and Satisfaction

The Effect of AI Chatbots on Consumer Engagement and Satisfaction

Assessing AI chatbots efficacy in ophthalmic triage and referrals: A comparative study

To evaluate the efficacy of AI chatbots (OpenAI ChatGPT GPT-3.5, Google Bard, and WebMD) compared to human ophthalmology trainees in triage and referrals for common ophthalmic conditions. A single-center study was conducted at MKCG Medical College, Berhampur, Odisha, involving six ophthalmology trainees. The performance of AI chatbots was assessed based on diagnostic accuracy and triage categorization. Key performance indicators included the accuracy of the top three suggested diagnoses and concordance in recommendations for investigations and referrals. Physician respondents identified the correct diagnosis among the top three suggestions in 95% of cases. Google Bard achieved 90% accuracy, ChatGPT 85%, and WebMD 20%. High concordance was observed between physician and AI recommendations for investigations and referrals. AI chatbots demonstrate promising potential in supporting triage and referral decisions for ophthalmic conditions. While human expertise remains crucial, AI tools can augment diagnostic accuracy, improve efficiency, and enhance patient care. Future research should focus on refining AI algorithms, integrating clinical data, and exploring real-world implementation strategies.

Understanding anthropomorphic voice-AI chatbot continuance from a human-AI interaction perspective

ABSTRACT Although anthropomorphic AI technologies nowadays are significantly changing the human-AI interaction, the mechanism of how voice-AI chatbots’ anthropomorphism affects users’ continuance behaviour is unclear. To explore the role of human-AI interaction and privacy concerns in the continuance intention of anthropomorphic voice-AI chatbots, a research model, based on parasocial relationship theory, is developed. The research model was then empirically tested against a cross-sectional survey from 473 voice-AI chatbot users and two-wave longitudinal data collected from 271 voice-AI chatbot users. The Structural Equation Modelling (SEM) results indicate that voice-AI chatbots’ anthropomorphism affects continuance intention via both human-AI interaction fluency and human-AI rapport building. In particular, the impact of voice-AI chatbots’ anthropomorphism on voice-AI chatbots’ continuance intention is fully mediated by human-AI interaction fluency and human-AI rapport building. Moreover, the influence of human-AI interaction fluency on voice-AI chatbots’ continuance intention will decrease and the impact of human-AI rapport building on voice-AI chatbots’ continuance intention will increase when users’ privacy concerns are high. The findings provide new insights into AI chatbot research from a human-AI interaction perspective. Developers of voice AI chatbots could focus on anthropomorphism, interaction and user information collection strategies to increase user continuance intention.

Exploring the Effect of Attachment on Technology Addiction to Generative AI Chatbots: A Structural Equation Modeling Analysis

With the advancement of artificial intelligence technology, generative AI chatbots (GAIC) such as ChatGPT, are integrating into various aspects of society and quietly changing people’s lives. However, although there is a plethora of research concerning social media usage and addiction, discussions about chatbots remain far from adequate. Investigating the use and potential technology addiction to generative AI chatbots (TAGAIC) holds both theoretical and practical significance. Drawing on attachment theory, this study aims to investigate the formative factors in TAGAIC. A structural equation model was used to analyze the data collected from a questionnaire survey of 364 GAIC users. Results reveal that technology addiction is positively influenced by emotional attachment but not by functional attachment. Besides, perceived anthropomorphism and perceived empathy have a positive effect on emotional attachment, while system quality and information quality have a positive effect on functional attachment. Limitations of the study were discussed.

Abstract 4120680: Language AI Chat BOTs as Pioneers in Cardiovascular Diagnosis? A Retrospective Analysis on Real-Time Patients in a Tertiary Care Centre

GPT-3 and GPT-4, show promise in assisting healthcare professionals with clinical questions. Their performance in real-time clinical scenarios, particularly with cardiovascular symptoms, remains underexplored. This study aims to evaluate their precision and reliability compared to physicians. Hypothesis: We hypothesize that board-certified emergency department attendings at Maimonides Medical Center exhibit higher accuracy and reliability than GPT-3.5 and GPT-4 in generating differential diagnoses based on history and physical examination for patients presenting to the emergency department. Methods: Patient data from Maimonides Medical Center's emergency department, collected from January 1, 2023, to April 30, 2023, was analyzed. Demographics, symptoms, medical history, and discharge diagnoses recorded by emergency room attendings were examined. ChatGPT-3.5 and GPT-4 generated differential diagnoses, which were compared with those by attending physicians. Accuracy was determined by comparing each rater's diagnoses with discharge diagnosis, calculating the proportion of correctly identified cases. Precision was assessed using Cohen's kappa coefficient and Intraclass Correlation Coefficient to measure agreement between raters. Results: Patient demographics showed mean age of 49.12 years, with 57.3% males and 42.7% females. Chief complaints included fever/sepsis (24.7%), gastrointestinal issues (17.7%), and cardiovascular problems (16.4%). Diagnostic accuracy against discharge diagnoses was highest for Chat GPT-4 (85.5%), followed by Chat GPT-3.5 (84.6%) and ED attendings (83%). Cohen’s kappa demonstrated moderate agreement (0.7) between AI models, with lower agreement observed for ED attendings. Stratified analysis revealed higher accuracy for gastrointestinal complaints with Chat GPT-4 (87.5%) and cardiovascular complaints with Chat GPT-3.5 (81.34%). Conclusion: Our study demonstrates that Chat GPT-4 and GPT-3.5 exhibit comparable diagnostic accuracy to attendings, highlighting their potential to aid decision-making in dynamic clinical settings. The stratified analysis revealed comparable reliability and precision of the AI chat bots for cardiovascular complaints which represents a significant proportion of the high risk patients presenting to the emergency department. This study contributes to integrating AI models into medical practice, enhancing efficiency and effectiveness in clinical decision-making

Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures.

Patient education materials (PEMs) are crucial for improving patient adherence and outcomes; however, they may not beaccessibledue to high reading levels. Our study used seven readability measures to compare the readability of SpanishPEMsfromthe Society of Pediatric Dermatology (SPD) with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. Our results showed that when prompted to produce material at a 6th grade level, both AI ChatBots generated significantly improved readability scores when compared to the SPD handouts. These findings suggest that AI-generated PEMs couldbetter meet readability standards and potentially improve patient outcomes, although further studies are needed to confirm this.

Exploring EFL Students' Creativity and Engagement with AI Chatbot in Web-Based English Course

This research explores the role of AI-based chatbot tools in promoting creativity and engagement among English as a Foreign Language (EFL) students in web-based English material development course. A qualitative research design was employed, involving in-depth interviews and observation checklists. The research was conducted at the English Education Faculty in Parangtambung of Universitas Negeri Makassar, with data collection spanning from May 12, 2024, to June 9, 2024. Participants included five undergraduate students from the 6th semester based on their familiarity with AI chatbots. The subjects of this research were undergraduate students enrolled in the English web-based material development course. Specifically, five students with prior experience or familiarity with AI chatbots were chosen to provide detailed insights through interviews and observations. The findings indicate that AI chatbots significantly encourage creativity among EFL students by providing a safe space for language experimentation and immediate feedback. Students reported increased engagement and enjoyment in their learning process. The research concludes that while AI chatbots can effectively foster creativity and engagement, careful integration and continuous monitoring are necessary to maximize their educational benefits.

فاعلية توظيف العلاقات العامة لربورتات الدردشة في التعريف بالخدمات في المؤسسات الحكومية

The study aimed to describe the effectiveness of employing public relations for chat bots in defining services in government institutions, by knowing the manifestations of public relations practitioners' interest in knowing information about chat bots and employing them in defining services in government institutions, services and areas of use, as well as the benefits achieved and the obstacles they face in this field, reaching their suggestions and their evaluation of the impact of these applications on their professional competence, and the future of public relations in light of chat bots. The study used the descriptive approach, and among the tools, the interview was used, and a questionnaire on the reality of public relations practitioners' use of chat bots in defining services in government institutions. The study sample consisted of (240) employees in the Corporate Communications and Public Relations Department in government institutions. As for the results of the study, it showed great interest from public relations practitioners in government institutions in chat bots and their use in managing corporate communication, as their aspects of use varied between crisis management and meeting the needs of the public. These technologies have contributed to improving the professional efficiency of most of the respondents by enhancing daily performance, speed of accomplishment, planning and decision-making. However, users faced challenges such as the difficulty of robots understanding customer feelings and fear of losing jobs. They suggested linking promotion to their ability to use them and provide training and technical support. Despite the benefits of robots, there are still concerns about the future of the public relations profession in light of this technology.

Beyond Automation: A Rigorous Testing Framework for Reliable AI Chatbots in Life Insurance

AI-driven chatbots are transforming customer service in Life Insurance, offering policyholders instant support, streamlined inquiries, and personalized recommendations. However, deploying these chatbots effectively requires addressing unique challenges, including interpreting complex insurance terminology, ensuring regulatory compliance, and maintaining data security and user privacy. This research introduces a detailed testing framework specifically tailored to Life Insurance chatbots, covering functional, non-functional, AI-specific, and compliance testing to guarantee accuracy, reliability, and adherence to legal standards. The framework emphasizes essential practices for verifying natural language understanding, sensitive data handling, and empathetic responses for users navigating critical life events. This research highlights the critical role of targeted testing to deliver robust, user-centered chatbot solutions in the Life Insurance sector.

A fine-tuning enhanced RAG system with quantized influence measure as AI judge

This study presents an innovative enhancement to retrieval-augmented generation (RAG) systems by seamlessly integrating fine-tuned large language models (LLMs) with vector databases. This integration capitalizes on the combined strengths of structured data retrieval and the nuanced comprehension provided by advanced LLMs. Central to our approach are the LoRA and QLoRA methodologies, which stand at the forefront of model refinement through parameter-efficient fine-tuning and memory optimization. A novel feature of our research is the incorporation of user feedback directly into the training process, ensuring the model’s continuous adaptation to user expectations and thus, improving its performance and applicability. Additionally, we introduce a Quantized Influence Measure (QIM) as an innovative “AI Judge” mechanism to enhance the precision of result selection, further refining the system’s accuracy. Accompanied by an executive diagram and a detailed algorithm for fine-tuning QLoRA, our work provides a comprehensive framework for implementing these advancements within chatbot technologies. This research contributes significant insights into LLM optimization for specific uses and heralds new directions for further development in retrieval-augmented models. Through extensive experimentation and analysis, our findings lay a robust foundation for future advancements in chatbot technology and retrieval systems, marking a significant step forward in the creation of more sophisticated, precise, and user-centric conversational AI systems. We make the dataset, the data processing package huggify-data, the model, and the app publicly available for the community.

Response to comment on: can AI Chatbots accurately answer patient questions regarding vasectomies?

Response to comment on: can AI Chatbots accurately answer patient questions regarding vasectomies?

Utilising Artificial Intelligence (AI) in Vocabulary Learning by EFL Omani Students: The Effect of Age, Gender, and Level of Study

AI tools have enabled FLLs to navigate their journeys of learning languages. This poses several questions, particularly about the most common AI tools used in learning vocabulary and the attitudes of EFL Omani students toward using AI tools in learning English. A mixed-method research design was utilised, and the sampling included 236 respondents studying in the Sultanate of Oman. An SPSS version 29 was employed in analysing the quantitative data, whereas the qualitative data were analysed thematically. The qualitative data revealed that EFL Omani students depended heavily on Google Translation (44%) as the highest AI tool, followed by the Dictionary Application (32%), ChatGPT (22%), Chat Bot (17.40%), and Duolingo (15.70%). In addition, translating the meaning of a new word occupied the highest learning strategy (frequency: 141), followed by learning new vocabulary (frequency: 134), translating the meaning of a full sentence (frequency: 91), and learning the correct pronunciation of strange words (frequency: 90), whereas learning grammar, enhancing writing and reading skills had the lowest frequency. The quantitative data showed that the overall mean is (3.67), which reveals a high frequency of use of AI tools in learning vocabulary. The lowest mean (3.45) is associated with trusting the new vocabulary recommended by AI tools, whereas the positive effectiveness of AI tools has the highest mean (3.92). However, it was found that age, gender, and level of study do not affect EFL Omani students’ use of AI tools to learn vocabulary.

AI Chatbots: Fast Tracking Sustainability Report Analysis for Enhanced Decision Making

AI Chatbots: Fast Tracking Sustainability Report Analysis for Enhanced Decision Making

Letter to the Editor: Enhancing the Readability of Online Patient-Facing Content Using AI Chatbots.

Letter to the Editor: Enhancing the Readability of Online Patient-Facing Content Using AI Chatbots.

Authors' Reply to the Letter to the Editor by Wu Re: Enhancing the Readability of Online Patient-Facing Content Using AI Chatbots.

Authors' Reply to the Letter to the Editor by Wu Re: Enhancing the Readability of Online Patient-Facing Content Using AI Chatbots.

Do AI chatbots impact motivation? Insights from a preliminary longitudinal study

Do AI chatbots impact motivation? Insights from a preliminary longitudinal study

AI chatbots and political learning

ABSTRACT We preregistered and experimentally tested six hypotheses assessing AI chatbot effects on political learning. We randomly assigned subjects to the AI chatbot ChatGPT, Google Search, or a control group with no stimulus or outside assistance in two nationally representative survey experiments on U.S. crime rates and vaccine safety. Subjects using AI chatbots learn more than those with no Internet resources and are confident in what they learn. However, none of these attributes from AI usage were substantively different from those using Google searches in both samples and topics. Directly engaging with ChatGPT increases subjects’ comprehension of their researched content compared to Google. These findings suggest that AI chatbots help promote political learning but are not necessarily more impactful than traditional Internet searches. The impact of AI evolution on shaping political discourse and public knowledge warrants further exploration.

Our journey with an AI assistant offering narrative therapy on WhatsApp

ABSTRACT This study examines the integration of AI chatbots into social work, with a focus on democratising AI for use in Narrative Therapy (NT). As AI becomes more accessible, social work encounters new opportunities to utilise these tools in practice, but also faces significant challenges. The study explores the feasibility of using customisable GPT platforms to develop an AI chatbot for NT sessions and assesses its performance. An autoethnographic case study was conducted using a chatbot configured with the OpenAI Assistant API to perform NT on WhatsApp. Findings suggest that while practitioners can configure the chatbot to guide users through NT stages and adapt to interactions, there are still limitations, such as the need for basic coding and technical knowledge for deployment and enhancement. The study highlights both the potential and the challenges of AI in social work, advocating for enhanced training, interdisciplinary collaboration, and the cautious development of AI-assisted practices.

The impact of different conversational generative AI chatbots on EFL learners: An analysis of willingness to communicate, foreign language speaking anxiety, and self-perceived communicative competence

Based on the Interaction Hypothesis, the study investigates the impact of different conversational Generative Artificial Intelligence (GenAI) chatbots on English as a Foreign Language (EFL) learners’ willingness to communicate (WTC), foreign language speaking anxiety (FLSA), self-perceived communicative competence (SPCC) and speaking performance. Three groups of Chinese undergraduate students were recruited: a control group (CG, N = 33) and two experimental groups (EG1, N = 33; EG2, N = 33). The CG interacted with the teacher and classmates during the speaking class. In contrast, EG1 interacted with a text- and voice-based conversational GenAI chatbot called Typebot, while EG2 engaged with a conversational GenAI chatbot that featured both text and voice interaction along with human-like avatars named D-ID Agent. Quantitative analysis using multilevel modelling revealed that EG2 showed significant improvements in WTC and SPCC and a notable reduction in FLSA levels compared to CG. However, the pre- and post-speaking test results showed no significant differences in speaking performance across the groups. Qualitative data from semi-structured interviews supported these findings, highlighting the immersive learning experience and emotional support provided by the human-like avatars. These results suggest that visually embodied GenAI chatbots can effectively enhance the emotional experience during the language learning. The study provides practical insights for language educators on integrating GenAI technologies in language teaching, emphasising the benefits of human-like avatars in fostering a more engaging and supportive learning environment.

Chat-Bot Based Ticketing System Using Dialogflow and Llama LLM

MyMuseum represents an innovative solution designed to revolutionize the ticket booking process for museums across India, with the primary goal of enhancing engagement and increasing visitor numbers. At its core, the system incorporates a sophisticated conversational AI chatbot that streamlines the ticket booking process, eliminating the hassles traditionally associated with museum visits. This chatbot is complemented by an additional AI-powered feature that interactively presents exhibit information, creating a comprehensive and engaging user experience. The platform boasts a user-friendly interface accessible around the clock, significantly simplifying ticket booking procedures while offering real-time navigation support and comprehensive guides for all participating museums. Key features of MyMuseum include automated event management, a system for notifications and alerts, virtual tour capabilities, and interactive content delivery, collectively forming a holistic solution for museum visitors. This report provides an in-depth examination of the underlying technology stack, which encompasses the ticket-booking chatbot, a Large Language Model (LLM) for disseminating information about museums, their artifacts, and exhibits, integrated payment gateways, and robust backend services. It highlights the myriad benefits of the system, including enhanced automation, scalability, and the generation of data-driven insights. Beyond improving convenience for visitors, MyMuseum is projected to drive increased museum attendance, boost revenue through strategic upselling opportunities, and play a crucial role in enhancing cultural awareness and education. Furthermore, it promises to alleviate operational burdens on museum staff by providing real-time analytics and improving overall efficiency and sustainability in museum operations