Chatbot Research Articles

Infinite Potential of AI Chatbots: Enhancing User Experiences and Driving Business Transformation in E-commerce: Case of Palestinian E-Commerce

In the much-evolving digital age landscape, it is difficult to overlook artificial intelligence's enormous role in people. This study entails an empirical examination of the impact of AI-powered chatbots on improving customer experience, customer satisfaction, and potential benefits in the digital era. This study was handled by the use of quantitative data collection methods through an online survey from 221 online users of AI-powered chatbots in Palestine. The collected data was then analyzed using descriptive statistics and structural equation modeling techniques. According to the results, it was shown that service quality significantly influenced the intention to use chatbots by customers or users thus indirectly influencing the net benefits enjoyed by users in the digital era. Similarly, the service quality provided by AI-powered chatbots was found to significantly influence the intention to use them, thus indirectly influencing the net benefits of AI-powered chatbots. Additionally, service quality was found to influence user satisfaction with chatbots, which indirectly influenced the net benefits enjoyed by users/customers in the digital era. On the other hand, user satisfaction significantly and directly influenced the net benefits of AI-powered chatbots in the digital era. In this regard, this study significantly contributed to the AI-powered chatbot's body of knowledge in the Palestinian digital context by identifying the factors that influence the use of chatbots in the digital era from the users’ point of view with the capacity to help transform the Palestinian business landscape

AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning

Teaching and learning in higher education require adaptation following students' inevitable use of AI chatbots. This study contributes to the empirical literature on students' use of AI chatbots and how they influence learning. The aim of this study is to identify how to adapt programming education in higher engineering education. A mixed-methods case study was conducted of a scientific computing course in a Mechanical Engineering Master's program at a Eindhoven University of Technology in the Netherlands. Data consisted of 29 student questionnaires, a semi-structured group interview with three students, a semi-structured interview with the teacher, and 29 students' grades. Results show that students used ChatGPT for error checking and debugging of code, increasing conceptual understanding, generating, and optimizing solution code, explaining code, and solving mathematical problems. While students reported advantages of using ChatGPT, the teacher expressed concerns over declining code quality and student learning. Furthermore, both students and teacher perceived a negative influence from ChatGPT usage on pair programming, and consequently on student collaboration. The findings suggest that learning objectives should be formulated in more detail, to highlight essential programming skills, and be expanded to include the use of AI tools. Complex programming assignments remain appropriate in programming education, but pair programming as a didactic approach should be reconsidered in light of the growing use of AI Chatbots.

Evaluating the Efficacy of Large Language Models in CPT Coding for Craniofacial Surgery: A Comparative Analysis.

The advent of Large Language Models (LLMs) like ChatGPT has introduced significant advancements in various surgical disciplines. These developments have led to an increased interest in the utilization of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy. This observational study evaluated the effectiveness of 5 publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for craniofacial procedures. A consistent query format was employed to test each model, ensuring the inclusion of detailed procedure components where necessary. The responses were classified as correct, partially correct, or incorrect based on their alignment with established CPT coding for the specified procedures. The results indicate that while there is no overall significant association between the type of AI model and the correctness of CPT code identification, there are notable differences in performance for simple and complex CPT codes among the models. Specifically, ChatGPT 4.0 showed higher accuracy for complex codes, whereas Perplexity.AI and Bard were more consistent with simple codes. The use of AI chatbots for CPT coding in craniofacial surgery presents a promising avenue for reducing the administrative burden and associated costs of manual coding. Despite the lower accuracy rates compared with specialized, trained algorithms, the accessibility and minimal training requirements of the AI chatbots make them attractive alternatives. The study also suggests that priming AI models with operative notes may enhance their accuracy, offering a resource-efficient strategy for improving CPT coding in clinical practice. This study highlights the feasibility and potential benefits of integrating LLMs into the CPT coding process for craniofacial surgery. The findings advocate for further refinement and training of AI models to improve their accuracy and practicality, suggesting a future where AI-assisted coding could become a standard component of surgical workflows, aligning with the ongoing digital transformation in health care.

College Chatbot Using RASA

The rapid advancement of Artificial Intelligence, Machine Learning, etc., has brought about significant changes in the technological sphere for decades. One such transformative technology is a Chatbot, a Conversational AI capable of addressing users’ queries. These chatbots are tailored to serve various objectives, one of which is to streamline tasks in education through automation. In line with this, a paper titled “College Chatbot Using RASA” has been written, which can effectively respond to questions based on college and other college-related information. The chatbot has been made accessible via an API utilizing the Streamlit framework. The RASA framework, a key open-source technology, has been harnessed to enable the chatbot. RASA plays a vital role in the chatbot's functionality by combining two main components, Rasa Core and Rasa Natural Language Understanding (NLU). Rasa NLU component is used to deduce intent and extract necessary entities from user input, while Rasa Core generates output by constructing a probabilistic model. The model is evaluated by getting a confusion matrix and prediction confidence distribution. The chatbot, upon testing, showcases the best result of 1 for each accuracy, precision, recall, and F1 score.

Kajian Teologi Pastoral terhadap Artificial Intelligence dalam Praktek-praktek Religius

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.

170 Collective creativity; promoting physical activity. Outlining a Co-design process for the development of Digital Physical Activity promotion initiatives for Autistic Adults

Abstract Purpose Autistic students at higher education institutions in Ireland have reported feelings of isolation, depression, and disengagement, all of which contribute to health disparities in this population. It is critical to address these issues, given the recent significant increase in the number of Autistic youths enrolling in higher education. To inform the development of acceptable and effective physical activity promotion programmes, we utilized a co-design process with the aim of co-creating a digital health intervention to promote physical activity among Autistic university students. Methods A co-design process [Design Thinking] was utilized for the development of the intervention. First, to empathize with participants, semi-structured interviews were held with Autistic adults. Interviews were recorded, transcribed verbatim, and analysed via reflexive thematic analysis. Actionable intervention targets were defined, and potential solutions ideated. The findings from the interviews informed the exploration and refinement of intervention ideas during co-design workshops. Workshops featured a multidisciplinary development team, including Autistic adults and experts in technology, physical activity promotion, and digital health. Results Three central themes were developed during analysis of the interview data: (1) “Psychological Factors” describing participants’ experiences and needs relating to routines, motivation, and self-consciousness/anxiety; (2), “E-Health Ideas and Needs” encapsulated the participants’ preferences and suggestions for digital health intervention components; and (3) “Supportive Spaces” encompassing the participants’ preferences relating to physical and social environments for physical activity. Individual interviews findings informed the ideation of digital health solutions for refinement in co-design workshops; these included interventions focusing on enhancing e-communication about physical activity opportunities on campus, the use of AI Chatbots for physical activity promotion; and the use of online social support groups. Conclusions Findings suggest Autistic adults face a variety of interrelated barriers to physical activity. The co-design process enabled the collective design of digital health interventions with the potential to overcome these barriers and support Autistic students to enjoy and more active, engaged University experience. Support/Funding Source Erasmus+ Sport grant

Eco-literacy in the digital age: Comparing student comprehension with AI capabilities

The role of AI in eco-literacy education is crucial in today's technological era. Students increasingly engage with digital texts, such as fiction, enhancing their understanding of eco-literacy. This study compares the eco-literacy skills of university students and AI to evaluate their effectiveness in environmental education. Participants included 199 students from Makassar State University and Timor University in Indonesia and ten web-based AI chatbots running various large language models. Instruments included a 20-item multiple-choice test for input eco-literacy and a short story synopsis task, "Pohon Larangan di Bengayoan" (Prohibited Tree in Bengayoan) for output eco-literacy, distributed via Google Forms. The study applied independent sample t-test analysis and Yuen's test for robustness using Jamovi 2.4.8.0 software. Results showed no significant gender differences in students' eco-literacy but significant differences between universities, with Makassar State University students performing better. AI outperformed students, highlighting AI's potential in eco-literacy education and the need for curriculum enhancement in universities. Future research should involve broader samples and comprehensive assessment methods.

Understanding users' effective use of generative conversational AI from a media naturalness perspective: A hybrid SEM-ANN approach

Understanding users' effective use of generative conversational AI from a media naturalness perspective: A hybrid SEM-ANN approach

Self-disclosure to conversational AI: a literature review, emergent framework, and directions for future research

AbstractConversational technologies have become increasingly prevalent, with interactions becoming more and more personalized. This paper reviews literature on the phenomenon of self-disclosure to conversational technologies. Five types of factors emerge as influencing self-disclosure: interface modality, conversational factors, user characteristics, mediating mechanisms, and contextual factors. We describe each type of factor, cover findings from the literature, present the framework of factors influencing self-disclosure that thus emerges, and put forth pertinent questions for future research on self-disclosure to conversational technologies.

Adolescents' use and perceived usefulness of generative AI for schoolwork: exploring their relationships with executive functioning and academic achievement.

In this study, we aimed to explore the frequency of use and perceived usefulness of LLM generative AI chatbots (e.g., ChatGPT) for schoolwork, particularly in relation to adolescents' executive functioning (EF), which includes critical cognitive processes like planning, inhibition, and cognitive flexibility essential for academic success. Two studies were conducted, encompassing both younger (Study 1: N = 385, 46% girls, mean age 14 years) and older (Study 2: N = 359, 67% girls, mean age 17 years) adolescents, to comprehensively examine these associations across different age groups. In Study 1, approximately 14.8% of participants reported using generative AI, while in Study 2, the adoption rate among older students was 52.6%, with ChatGPT emerging as the preferred tool among adolescents in both studies. Consistently across both studies, we found that adolescents facing more EF challenges perceived generative AI as more useful for schoolwork, particularly in completing assignments. Notably, academic achievement showed no significant associations with AI usage or usefulness, as revealed in Study 1. This study represents the first exploration into how individual characteristics, such as EF, relate to the frequency and perceived usefulness of LLM generative AI chatbots for schoolwork among adolescents. Given the early stage of generative AI chatbots during the survey, future research should validate these findings and delve deeper into the utilization and integration of generative AI into educational settings. It is crucial to adopt a proactive approach to address the potential challenges and opportunities associated with these emerging technologies in education.

Integrating AI chatbots into the metaverse: Pre-service English teachers’ design works and perceptions

Integrating AI chatbots into the metaverse: Pre-service English teachers’ design works and perceptions

Generative AI chatbots in higher education: a review of an emerging research area

Generative AI chatbots in higher education: a review of an emerging research area

Performance of large language models (LLMs) in providing prostate cancer information

PurposeThe diagnosis and management of prostate cancer (PCa), the second most common cancer in men worldwide, are highly complex. Hence, patients often seek knowledge through additional resources, including AI chatbots such as ChatGPT and Google Bard. This study aimed to evaluate the performance of LLMs in providing education on PCa.MethodsCommon patient questions about PCa were collected from reliable educational websites and evaluated for accuracy, comprehensiveness, readability, and stability by two independent board-certified urologists, with a third resolving discrepancy. Accuracy was measured on a 3-point scale, comprehensiveness was measured on a 5-point Likert scale, and readability was measured using the Flesch Reading Ease (FRE) score and Flesch–Kincaid FK Grade Level.ResultsA total of 52 questions on general knowledge, diagnosis, treatment, and prevention of PCa were provided to three LLMs. Although there was no significant difference in the overall accuracy of LLMs, ChatGPT-3.5 demonstrated superiority over the other LLMs in terms of general knowledge of PCa (p = 0.018). ChatGPT-4 achieved greater overall comprehensiveness than ChatGPT-3.5 and Bard (p = 0.028). For readability, Bard generated simpler sentences with the highest FRE score (54.7, p < 0.001) and lowest FK reading level (10.2, p < 0.001).ConclusionChatGPT-3.5, ChatGPT-4 and Bard generate accurate, comprehensive, and easily readable PCa material. These AI models might not replace healthcare professionals but can assist in patient education and guidance.

Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies

With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review analyzes 24 empirical articles published between 1 January 2022 and 5 September 2023 on students’ perceptions and use of AI chatbots in higher education. The articles were reviewed using a five-stage scoping review methodology. The findings underscore a global research interest in how students engage with AI chatbots, which is especially pronounced in Asia. The studies span diverse disciplines, with a predominance in science, technology, engineering, and mathematics disciplines. The empirical findings reveal that students perceive AI chatbots as highly useful and motivating as personal task assistants and for getting immediate feedback and help with writing, coding, and academic tasks. However, students are concerned about the accuracy and reliability of the responses from the chatbots, as well as potential negative impacts on their learning processes, critical thinking, discipline, and creativity. The purpose-driven use of AI chatbots among students and their potentially positive influence on motivation and learning processes offer insights for educators and policymakers. Our research concludes that while positive attitudes, perceptions, and critical use prevail, addressing students’ concerns is crucial for responsible AI integration in higher education.

Investigating the role of artificial intelligence in predicting perceived dysphonia level.

This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level. Demographic information, voice self-assessments, and acoustic measurements related to a sample of 50 adult dysphonic outpatients were presented to ChatGPT and Perplexity AI chatbots, which were interrogated for the perceived dysphonia level. The agreement between the auditory-perceptual assessment by experts and ChatGPT and Perplexity AI chatbots, as determined by Cohen's Kappa, was not statistically significant (p = 0.429). There was also a low positive correlation (rs = 0.30, p = 0.03) between the diagnosis made by ChatGPT and Perplexity AI chatbots (rs = 0.30, p = 0.03). It seems that AI could not play a vital role in helping the voice care teams determine the perceptual level of dysphonia.

A Qualitative Study with Informal Caregivers and Healthcare Professionals for Individuals with Head and Neck Cancer on the Usage of AI Chatbots.

Informal caregivers (ICs), including the patient's spouse, close relatives, or friends, play an important role in caregiving individuals with head and neck cancer (HNC). AI-based chatbots might offer information and assistance related to caregiving. This study presents the viewpoints of ICs and healthcare professionals (HCPs) on using AI-based chatbots in caring for individuals with HNC. A total of six focus groups were conducted with 15 ICs and 13 HCPs from three Swedish university hospitals. The study uncovers a widespread hesitancy toward the intention to use AI-based chatbots among ICs and HCPs. Factors contributing to this reluctance include their distrust in chatbot-provided information, negative past experiences of using chatbots, and lack of human connection in chatbot interactions. Embracing a holistic approach is crucial when designing chatbots, ensuring active user engagement and incorporating their perspectives into the design process.

Building Conversational AI for Assamese Religious Text using Deep Learning

Building Conversational AI for Assamese Religious Text using Deep Learning

Enhancing conversational ai with the Rasa framework: intent understanding and NLU pipeline optimization

Implementing the Rasa NLU pipeline allowed intent detection and entity recognition, particularly in complex scenarios with multi-intent queries. Communication within the Rasa NLU pipeline was effectively managed, ensuring seamless data flow between components, which preserved context and enhanced interpretability. The voice assistant developed with STT and TTS capabilities demonstrated robust real-time natural language processing, handling spoken queries efficiently. This confirmed the practical viability of using the Rasa framework for scalable and customizable conversational AI applications. Discussing: The findings underscore the robustness of the Rasa NLU pipeline in handling diverse conversational demands and the flexibility of its components to adapt to different linguistic contexts. The research discusses the potential of integrating sophisticated NLU techniques to create more intuitive and responsive conversational agents, highlighting the critical role of context-aware processing in improving user interaction with AI systems.

A bibliometric analysis of the application of AI chatbots in language learning contexts

A bibliometric analysis of the application of AI chatbots in language learning contexts

Comparative Analysis of Accuracy, Readability, Sentiment, and Actionability: Artificial Intelligence Chatbots (ChatGPT and Google Gemini) versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery.

Eye surgeries often evoke strong negative emotions in patients, including fear and anxiety. Patient education material plays a crucial role in informing and empowering individuals. Traditional sources of medical information may not effectively address individual patient concerns or cater to varying levels of understanding. This study aims to conduct a comparative analysis of the accuracy, completeness, readability, tone, and understandability of patient education material generated by AI chatbots versus traditional Patient Information Leaflets (PILs), focusing on local anesthesia in eye surgery. Expert reviewers evaluated responses generated by AI chatbots (ChatGPT and Google Gemini) and a traditional PIL (Royal College of Anaesthetists' PIL) based on accuracy, completeness, readability, sentiment, and understandability. Statistical analyses, including ANOVA and Tukey HSD tests, were conducted to compare the performance of the sources. Readability analysis showed variations in complexity among the sources, with AI chatbots offering simplified language and PILs maintaining better overall readability and accessibility. Sentiment analysis revealed differences in emotional tone, with Google Gemini exhibiting the most positive sentiment. AI chatbots demonstrated superior understandability and actionability, while PILs excelled in completeness. Overall, ChatGPT showed slightly higher accuracy (scores expressed as mean ± standard deviation) (4.71 ± 0.5 vs 4.61 ± 0.62) and completeness (4.55 ± 0.58 vs 4.47 ± 0.58) compared to Google Gemini, but PILs performed best (4.84 ± 0.37 vs 4.88 ± 0.33) in terms of both accuracy and completeness (p-value for completeness <0.05). AI chatbots show promise as innovative tools for patient education, complementing traditional PILs. By leveraging the strengths of both AI-driven technologies and human expertise, healthcare providers can enhance patient education and empower individuals to make informed decisions about their health and medical care.