Conversational Artificial Intelligence Research Articles

Love it or hate it? Deconstructing consumers' attitudes towards AI enabled voice shopping

AbstractThe conversational, social and intelligent capabilities of Artificial Intelligence (AI) enabled voice assistants (VAs) to assist humans have grown. However, their use still remains limited for complex tasks such as shopping. While some studies find that consumers are willing to use them, others report negative reactions that lead to rejection, especially for complex activities. This study aims to bridge the gap between the dichotomous streams of literature by investigating the overall attitude towards the use of AI VAs for a multi‐step task that is, shopping. We first identify 27 attitudinal criteria through a comprehensive literature review in light of the Uncanny Valley Theory and the Self Determination Theory. The criteria are organized using the cognitive, affective and conative framework of attitude, and ranked using the Fuzzy Analytical Hierarchy Process. Sensitivity analysis is done to affirm the robustness of the framework. Findings reveal that the behavioral intentions to use (conative criteria) are the strongest, followed by feelings evoked during use (affective criteria), followed by beliefs people hold regarding AI VAs (cognitive criteria). Intention to use AI voice assistants during all stages of the purchase journey takes precedence over negative feelings such as loss of control, unfulfillment and vulnerability. This study reconciles the existing stream of literature on conversational AI and provides managerial implications based on the dimensions of attitude.

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.

A Survey on Conversational AI Question answering System for Low Resource Language

Question-and-answer (QA) systems allow users to pose questions about the information accessible in a variety of formats, such as structured and unstructured information in natural languages. It contributes significantly to conversational artificial intelligence (AI), which has led to the creation of a novel research field on conversational question answering (CQA), in which a system is required to understand the provided context before engaging in multi-turn QA to satisfy a user's informational needs. While the majority of currently done research focuses on single-turn QA, pre-trained language models and the availability of large multi-turn QA datasets have recently made multi-turn QA more significant. This study aims to give a thorough analysis of the most recent CQA research trends, mostly based on publications that have been reviewed in recent years. Our data indicates a shift in the industry's preference for multi-turn QA over single-turn QA, which has numerous advantages for conversational AI. In order to create a solid basis for the field of CQA, this survey aims to serve as the pinnacle for the research community.   

Unlocking the Future of Patient Education: ChatGPT vs. LexiComp® as sources of patient education materials

BackgroundChatGPT is a conversational artificial intelligence (AI) technology that has shown application in various facets of healthcare. With the increased use of AI, it is imperative to assess the accuracy and comprehensibility of AI platforms. ObjectiveThis pilot project aimed to assess the understandability, readability, and accuracy of ChatGPT as a source of medication-related patient education as compared with an evidence-based medicine tertiary reference resource, LexiComp®. MethodsPatient education materials (PEMs) were obtained from ChatGPT and LexiComp® for eight common medications (albuterol, apixaban, atorvastatin, hydrocodone/acetaminophen, insulin glargine, levofloxacin, omeprazole, and sacubitril/valsartan). PEMs were extracted, blinded, and assessed by two investigators independently. The primary outcome was a comparison of the Patient Education Materials Assessment Tool-printable (PEMAT-P). Secondary outcomes included Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, word count, and accuracy. A 7-item accuracy checklist for each medication was generated by expert consensus among pharmacist investigators, with LexiComp® PEMs serving as the control. PEMAT-P interrater reliability was determined via intraclass correlation coefficient (ICC). Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, and word count were calculated by Microsoft® Word®. Continuous data were assessed using the Student’s t-test via SPSS (version 20.0). ResultsNo difference was found in the PEMAT-P understandability score of PEMs produced by ChatGPT versus LexiComp® [77.9% (11.0) vs. 72.5% (2.4), P=0.193]. Reading level was higher with ChatGPT [8.6 (1.2) vs. 5.6 (0.3), P<0.001). ChatGPT PEMs had a lower percentage of passive sentences and lower word count. The average accuracy score of ChatGPT PEMs was 4.25/7 (61%), with scores ranging from 29-86%. ConclusionDespite comparable PEMAT-P scores, ChatGPT PEMs did not meet grade level targets. Lower word count and passive text with ChatGPT PEMs could benefit patients, but the variable accuracy scores prevent routine use of ChatGPT to produce medication-related PEMs at this time.

A longitudinal model of continued acceptance of conversational artificial intelligence

PurposeThe existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.Design/methodology/approachAn autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.FindingsThe results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.Originality/valueThis study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.

Evolving Minds and Riding Waves: Development and Progress in ChatGPT

This research paper delves into the dynamic evolution of ChatGPT, a foremost conversational Artificial Intelligence (AI) model, with a focus on its developmental journey and progressive advancements. Termed "Evolving Minds" and "Riding Waves," we investigate the intricate interplay between cognitive evolution and the dynamic trajectory of progress. Through a comprehensive methodological framework encompassing historical analysis, comparative studies, and trend assessment, we meticulously trace ChatGPT's developmental milestones and transformative shifts. Our inquiry extends into the underlying mechanisms propelling its advancement within the landscape of conversational AI. By unravelling these mechanisms, our research aims to provide insights into the fundamental drivers of AI-driven conversation evolution. By shedding light on ChatGPT's evolution, we seek to deepen the understanding of the dynamics shaping progress in this swiftly evolving domain. Ultimately, this study contributes to the ongoing discourse on the future trajectory of conversational AI and its potential implications across various sectors and applications. Through a nuanced examination of ChatGPT's evolution, we aim to inform future developments and pave the way for innovative advancements in AI-driven conversation technologies. Key Words: ChatGPT, Artificial Intelligence, progress, innovative.

Exploring the Potential of Chatbots in Extending Tourists’ Sustainable Travel Practices

Recent advancement in Generative Conversational Artificial Intelligence (AI) has opened the possibility of promoting chatbots for nudging. Tourists returning home from destinations that enforce sustainable transport regulations might be nudged to continue traveling sustainably. Through exploratory qualitative research, this study conceptualizes a chatbot to facilitate pro-environmental behavior spillover among domestic tourists in the Gili Islands, Indonesia. The Service Design approach and Grounded Theory were applied throughout the data collection process involving semi-structured in-depth interviews, focus group discussions and field observations. Insights obtained from stakeholders from multidisciplinary backgrounds were used to provide an overview of how the chatbot can be developed and the potential impact it could bring. A theoretical model predicting factors that play important roles in adopting the proposed chatbot and its respective nudges was developed. It informs policymakers and practitioners about the potential use of AI to help transform tourist experiences and shape lasting responsible behaviors.

Is ChatGPT-like technology going to replace commercial search engines?

Purpose This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services. Design/methodology/approach In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services. Findings Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described. Originality/value This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

Conversational AI: An Explication of Few-Shot Learning Problem in Transformers-Based Chatbot Systems

With the recent advancements in conversational artificial intelligence (AI), the practical applications of chatbots have risen significantly in diverse domains such as healthcare, education, e-government, customer support systems, social platforms, and entertainment. The chatbot converses with humans in natural language and responds to their queries with precise and relevant answers. The relevant literature presents several methods for intent classification and slot mapping for chatbot question answering. However, the existing chatbot architectures still suffer from few-shot learning problem (imbalance ratio of class labels over samples) and ineffective dialog management to retain the context and slots mapping. This study aims to introduce the architecture of chatbot by focusing few-shot learning problem and context management in dialog-based conversations. First, to mitigate the few-shot learning problem with chatbots, we propose a novel hybrid intent and slots transformers (HIST) model. The HIST chatbot architecture utilizes transformers and self-attention mechanisms along with bigated recurrent unit and combines conditional random field algorithms for intent classification and slot extraction. Second, to address dialog management, we introduce a hybrid interaction strategy for slots mapping and effective conversational context management. To validate the proposed model’s effectiveness, comprehensive empirical analysis is carried out using three benchmark datasets including airline travel information system (ATIS), banking77, and conversational language interface for natural conversation 150 (CLINC150). The results show that HIST outperforms against the state-of-the-art existing methods with a clear margin and obtained an accuracy of 94.89% and 96.17% for intent classification and slots extraction, respectively. The empirical results confirm the effectiveness of the HIST chatbot for resolving the few-shot learning problem with effective dialog management in chatbot systems.

Immersive multi-modal pedagogical conversational artificial intelligence for early childhood education: An exploratory case study in the wild

Educational technology research has found that parents of young children widely share concerns about extended screen time, lack of physical activity, and lack of social interaction. Kid Space was developed to address these concerns by enabling multi-modal and immersive collaborative play-based learning. Kid Space utilizes multiple sensing technologies with an immersive physical space through a human-scale wall projection and incorporates a conversational AI agent to interact with children, understand individual progress, and personalize learning experiences in a blended physical and digital environment. To evaluate Kid Space in the wild, we conducted a multi-method user study involving a quasi-experimental design and exploratory case study with 14 students and three educators in an elementary school. Mixed methods for data collection and analysis were used to understand the students' and educators' perceptions of Kid Space and its impact on the students’ educational outcomes (learning engagement, experience, and performance). The findings showed (1) positive perceptions toward Kid Space, (2) high levels of engagement - with decreased screen time (41% of the time), increased physical activity (99.3% of the time), and increased social interactions with conversational AI agent and the other collaborating student (52% of the time), and (3) significant learning gains after experiencing Kid Space (24% gain, paired t-test: p < 0.01). These positive results are accompanied by critical user insights for improving future iterations of Kid Space to validate long-term educational outcomes.

ChatGPT versus Bard: A comparative study

AbstractThe rapid progress in conversational AI has given rise to advanced language models capable of generating human‐like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human‐like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting‐edge language models.

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.

Designing an age-friendly conversational AI agent for mobile banking: the effects of voice modality and lip movement

Age factor is a significant barrier in adopting mobile banking mainly due to low competence in using the system and security concerns. Thus, this study presents a conversational artificial intelligence agent-based mobile banking app that is specially designed for seniors considering their needs and physical characteristics. Voice modality and lip-movement were identified as the important factors to improve seniors’ mobile banking service experience and the effect of them were empirically tested by conducting a controlled experiment with between-subject design. In total, 91 participants who were 55 years or older used the app to complete the given tasks of online banking. Results showed that those in the two voice modality conditions, compared to text agent condition, reported higher levels of trust, perceived ease of use, and social presence. The effect of lip movement of the agent also showed significant results in those three dependant variables. The result of mediation analysis revealed that agent's lip movement increased social presence and perceived ease of use, which further increased trust toward the agent. The results are meaningful especially because trust and perceived ease of use were listed as the main reason for not using online banking services among non-users. The implications of the study were discussed.

Is ChatGPT a Responsible Communication: A Study on the Credibility and Adoption of Conversational Artificial Intelligence

In the realm of generative artificial intelligence (AI), ChatGPT has garnered widespread acclaim for its remarkably realistic chatbot interactions. Despite its popularity, recent challenges in reliability have emerged. This research uniquely combines the Stimulus-Organism-Response (S-O-R) framework with dual-process theory to dissect the factors influencing ChatGPT’s credibility and popularity. Employing cross-sectional data from select social media platforms, the study identifies quality, accuracy, timeliness, audience familiarity, and congruence with other sources as pivotal elements shaping ChatGPT’s credibility. Significantly, this research reveals how users evaluate the reliability of generative AI-generated search results by comparing them to conventional methods. The study’s novel contribution lies in its exploration of ChatGPT as a medium of communication, offering valuable insights into understanding and enhancing the reliability of generative AI applications in the information landscape.

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.

Conversational artificial intelligence development in healthcare

Conversational artificial intelligence development in healthcare

Conversational AI facilitates mental health assessments and is associated with improved recovery rates

Mental health services across the globe are overburdened due to increased patient need for psychological therapies and a shortage of qualified mental health practitioners. This is unlikely to change in...

Accuracy of the Information on Sudden Sensorineural Hearing Loss From Chat Generated Pre-Trained Transformer

Background and Objectives Chat generated pre-trained transformer (ChatGPT) is a conversational artificial intelligence model, which has recently attracted worldwide attention by enabling natural conversations based on huge information from deep learing in various fields. Several studies have reported usefullness and reliability of medical information obtained by ChatGPT, but there are no studies explaining reliability and accuracy in the field of otorhinolarynoglogy. On this regard, we investigated the accuracy of information on sudden sensorineural hearing loss obtained by ChatGPT.Materials and Method Twenty-five questions and answeres related to sudden sensorineural hearing loss were recorded from ChatGPT based on textbook from Korean Society of Otorhinolaryngology-Head and Neck Surgery and Clinical Guidelines of American Academy of Otolaryngology-Head and Neck Surgery. Answers were shown to one specialist in otorhinolaryngoly in a blind test and asked to assess their accuracy. Each question was rated as ‘accurate’ or ‘inaccurate.’ If the contents were not found in the textbook or the guidelines, it was rated as ‘unreliable.’Results Of the 25 questions, 19 (76%) were identified as ‘accurate,’ 6 (24%) were ‘inaccurate,’ and 0 (0%) were ‘unreliable.’ Questions about definition, prevalence, hearing rehabilitation, diagnosis and treatment were found to be more accurate than the average, while causes and prognosis were less accurate than the average.Conclusion The information on sudden sensorineurla hearing loss obatined from ChatGPT was quite accurate. It is expected to provide substantial help to patients and doctors. As medicine and medical artificial intelligence develop together, further research is needed on reliability and accuracy in vaious diseases and fields of otorhinolaryngology-head and neck surgery.

Conversational AI Chatbots in library research: An integrative review and future research agenda

The growing role of conversational AI Chatbots continues to change the library and information service landscape. Chatbots are replacing some of the library services that humans conventionally perform. In the era of instant evolution of artificial intelligence (AI), the role of Chatbots in libraries keeps expanding and acquiring more experience. This paper aims to examine the extant research on library Chatbots using an integrative literature review (ILR) approach. Empirical and non-empirical papers from the Scopus database to ascertain what is already known about the topic. Forty papers (articles and conference papers) were scrutinized for further analysis. The leading emergent themes in the literature were (1) The evolution of Chatbots technology in libraries, (2) Antecedents for Chatbot use in libraries, (3) User experience with Chatbot use in libraries, (4) Chatbot use in libraries amidst COVID-19, and (5) Challenges facing Chatbot use in libraries. Research on Chatbots in library services is still embryonic and has only begun to flourish. Nevertheless, there is still a significant research gap despite its surging curve. The findings of this integrative review contribute to the body of knowledge on the nexus between artificial intelligence and library operations. It also furnishes academics and practitioners with six potential directions for future research opportunities.

TRANSFORMATION OF RECRUITMENT PROCESS THROUGH IMPLEMENTATION OF AI SOLUTIONS

This article reviews how the recruitment process across various industries has been transformed due to recent advancements in artificial intelligence (AI) solutions. A literature review summarizes key findings on AI-enabled improvements in sourcing, screening, assessment, interviewing, and onboarding of candidates. Both recruitment analytics using big data and conversational AI chatbots are discussed as drivers reshaping traditional human resource workflows. Challenges around AI bias, privacy, and job automation fears are also analyzed. Overall, AI implementation led to enhanced efficiency, quality, and experience in talent acquisition. However, maintaining a human touch remains vital, suggesting an optimal balance between AI and human recruiters.