Chatbot Research Articles

AI Chatbot integration in SME marketing platforms: Improving customer interaction and service efficiency

This review paper examines the integration of AI chatbots in small and medium-sized enterprise (SME) marketing platforms, focusing on their impact on customer interaction, service efficiency, and future implications. AI chatbots, powered by advancements in natural language processing and machine learning, have revolutionized customer service by automating routine tasks, providing 24/7 availability, and personalizing interactions based on user data. The paper explores historical developments, current trends, and applications of chatbots in SMEs, highlighting benefits such as cost efficiency and improved service processes. Furthermore, it discusses emerging technologies in chatbot development and outlines long-term benefits for SMEs, including enhanced customer satisfaction and operational efficiencies. Practical recommendations for SMEs considering chatbot integration are provided to optimize implementation and leverage this technology effectively in their marketing strategies. Keywords: AI Chatbots, SME Marketing, Customer Interaction, Service Efficiency, Automation, Future Trends.

Reshaping Mathematics Instruction Via Impact of AI Chatbots on Secondary Education Pre-service Teachers

This study assessed the level of impact of AI Chatbots (ChatGPT, Gemini, and Perplexity) in Mathematics Education. The study employed a descriptive research design where structured survey questionnaires were used and administered to ninety (90) Secondary Education Pre-service Teachers at Bukidnon State University, Philippines. Results showed that the level of impact of the AI Chatbots (ChatGPT, Gemini, and Perplexity) in Mathematics Education on the nine (9) factors is Very High, namely; (1) Effectiveness ; (2) Engagement ; (3) Accessibility ; (4) Personalization ; (5) Feedback Quality ; (6) Confidence Building ; (7) Time Efficiency ; (8) Adaptability ; and (9) Student Satisfaction . The findings demonstrate a positive student assessment on AI chatbots (ChatGPT, Gemini, and Perplexity) in mathematics education, consistently rated highly across various factors, indicating their significant impact on reshaping instruction, providing valuable support, enhancing learning experiences, and boosting confidence and proficiency in math, underscoring the transformative potential of AI technology and its essential integration into educational practices to address students' evolving needs in the digital age.

Competition concerns with foundation models: a new feast for big tech?

ABSTRACT The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry.

Emergence of Artificial Intelligence Art Therapies (AIATs) in Mental Health Care: A Systematic Review.

The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and the therapeutic potential of creative arts. This systematic review aimed to assess the effectiveness and ethical considerations of AIATs, incorporating robots, AI painting and AI Chatbots to augment traditional art therapies. Aligning with the Preferred Reporting Items for systematic reviews (PRISMA) guidelines, we meticulously searched PubMed, Cochrane Library, Web of Science and CNKI, resulting in 15 selected articles for detailed analysis. To ensure methodological quality, we applied the Joanna Briggs Institute (JBI) criteria for quality assessment and extracted data using the PICO(S) format, specifically targeting randomised controlled trials (RCTs). Our findings suggest that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges. This examination underscores AIATs' potential to enrich mental health therapies, emphasising the critical importance of ethical considerations and the responsible application of AI as the field evolves. With a focus on expanding treatment efficacy and patient expressiveness, the promise of AIATs in mental health care necessitates a careful balance between innovation and ethical responsibility. Trial Registration: PROSPERO: CRD42024504472.

Benefits of AI chatbots and engagement of senior high school students in Divine Word College of San Jose

Benefits of AI chatbots and engagement of senior high school students in Divine Word College of San Jose

Can the AI tools ChatGPT and Bard generate energy, macro- and micro-nutrient sufficient meal plans for different dietary patterns?

Artificial intelligence chatbots based on large language models have recently emerged as an alternative to traditional online searches and are also entering the nutrition space. In this study, we wanted to investigate whether the artificial intelligence chatbots ChatGPT and Bard (now Gemini) can create meal plans that meet the dietary reference intake (DRI) for different dietary patterns. We further hypothesized that nutritional adequacy could be improved by modifying the prompts used. Meal plans were generated by 3 accounts for different dietary patterns (omnivorous, vegetarian, and vegan) using 2 distinct prompts resulting in 108 meal plans total. The nutrient content of the plans was subsequently analyzed and compared to the DRIs. On average, the meal plans contained less energy and carbohydrates but mostly exceeded the DRI for protein. Vitamin D and fluoride fell below the DRI for all plans, whereas only the vegan plans contained insufficient vitamin B12. ChatGPT suggested using vitamin B12 supplements in 5 of 18 instances, whereas Bard never recommended supplements. There were no significant differences between the prompts or the tools. Although the meal plans generated by ChatGPT and Bard met most DRIs, there were some exceptions, particularly for vegan diets. These tools maybe useful for individuals looking for general dietary inspiration, but they should not be relied on to create nutritionally adequate meal plans, especially for individuals with restrictive dietary needs.

Co-designing the integration of voice-based conversational AI and web augmentation to amplify web inclusivity

The Web has become an essential resource but is not yet accessible to everyone. Assistive technologies and innovative, intelligent frameworks, for example, those using conversational AI, help overcome some exclusions. However, some users still experience barriers. This paper shows how a human-centered approach can shed light on technology limitations and gaps. It reports on a three-step process (focus group, co-design, and preliminary validation) that we adopted to investigate how people with speech impairments, e.g., dysarthria, browse the Web and how barriers can be reduced. The methodology helped us identify challenges and create new solutions, i.e., patterns for Web browsing, by combining voice-based conversational AI, customized for impaired speech, with techniques for the visual augmentation of web pages. While current trends in AI research focus on more and more powerful large models, participants remarked how current conversational systems do not meet their needs, and how it is important to consider each one’s specificity for a technology to be called inclusive.

Improving elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions

Generative artificial intelligence (GAI) and automatic speech recognition (ASR) have ushered in promising tools for foreign language learning, notably GAI chatbots. This study investigated the impact of GAI chatbots on elementary school English as a foreign language (EFL) learners' speaking skills, focusing on two interaction configurations—individual and paired. Eighty-five elementary school EFL learners participated in a three-week summer program, engaging in daily 45-min interactions with CoolE Bot. The participants were randomly assigned to three groups: (1) individual interaction with CoolE Bot (I-Bot group), (2) paired interaction with CoolE Bot (P-Bot group), and (3) interaction with teachers and peers in a conventional English classroom (No-Bot group). In each class, participants in the Bot group received worksheets with a topic, prompts, and vocabulary to guide their interactions with CoolE Bot, while those in the No-Bot group also received worksheets for comparable activities. Quantitative (English-speaking tests) and qualitative data (semi-structured interviews) were collected and analyzed. Results revealed that the I-Bot and P-Bot groups' post-test speaking skills were significantly higher than those of the No-Bot group. CoolE Bot significantly improved the speaking skills of EFL learners. Both individual and paired interactions with CoolE Bot demonstrated positive effects, with no significant differences between groups. Interviews highlight CoolE Bot's adeptness in coherent interaction, charismatic conversational style with a human-like voice, diverse topic discussions tailored to learners' interests, and supportive functions. The participants found GAI chatbot-assisted EFL speaking enjoyable, motivating, and engaging appreciating its cartoonish, human-like characters, conversational style, and voice. Additionally, CoolE Bot fostered rapport and a supportive environment enhancing learners' confidence and reducing anxiety regarding EFL speaking. Individual interactions encourage personalized engagement and self-directed learning, whereas paired interactions involve social dynamics, shared learning experiences, and mutual resolution of language challenges.

AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications

Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential benefits in improving mental and emotional well-being, addressing specific mental health conditions, and facilitating behavior change. However, challenges related to usability, engagement, and integration with existing healthcare systems were identified. AI chatbots hold promise for mental health interventions, but widespread adoption hinges on improving usability, engagement, and integration with healthcare systems. Enhancing personalization and context-specific adaptation is key. Future research should focus on large-scale trials, optimal human–AI integration, and addressing ethical and social implications.

Conversing with AI chatbots: examining what OpenAI ChatGPT-4, Microsoft Bing Chatbot, and google bard know, think they know, do not know, and would like to know about engineering

Conversing with AI chatbots: examining what OpenAI ChatGPT-4, Microsoft Bing Chatbot, and google bard know, think they know, do not know, and would like to know about engineering

Marine information-seeking behaviours and AI chatbot impact on information discovery

Purpose This research investigates students’ information-seeking behaviours at the Indian Maritime University (IMU) and assesses the impact of AI chatbots on their marine science knowledge and awareness. The study aims to provide insights into the role of AI-driven solutions in enhancing knowledge sharing and the challenges faced in using AI tools for marine information retrieval. Design/methodology/approach The study used a stratified random sampling method, encompassing 152 respondents from IMU’s B.Sc. in Nautical Science and B. Tech in Marine Engineering programs. Data collection involved a structured electronic survey questionnaire. The analysis encompassed descriptive statistics using SPSS. Findings Information needs were met through diverse channels, with 57.9% of respondents using AI-driven chatbots for marine information retrieval. AI significantly recommended research papers (61.8%). The chatbot positively impacted marine science awareness and knowledge, with a mean satisfaction rating of approximately 3.3. Challenges included insufficient access to AI tools, data privacy concerns and accuracy issues. Originality/value This study contributes original insights into the information-seeking behaviours of marine students at IMU and the impact of AI chatbots on their knowledge and awareness. It highlights the multifaceted nature of marine information retrieval, the effectiveness of AI-driven solutions in enhancing knowledge sharing and the challenges that need to be addressed for the broader adoption of AI tools in this context.

Incorporating Artificial Intelligence, Tai Chi and Qigong into a Gynecology & Obstetrics

This study uses Microsoft Copilot, an artificial intelligence chat bot, to generate nonprofessional useful information regarding the use of tai chi and qigong, Chinese medical techniques, that can be incorporated in a gynecology and obstetrics medical practice. It then goes to the PubMed database to search for studies that have incorporated qigong or tai chi into their breast cancer treatment program. The study concluded that both artificial intelligence and Chinese medical techniques such as tai chi and qigong have a bright future in obstetric and gynecology practices.

Exploring the impact of ChatGPT: conversational AI in education

Exploring the impact of ChatGPT: conversational AI in education

Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity

Abstract Background Artificial intelligence chatbots have demonstrated potential to enhance clinical decision-making and streamline health care workflows, potentially alleviating administrative burdens. However, the contribution of AI chatbots to radiologic decision-making for clinical scenarios remains insufficiently explored. This study evaluates the accuracy and reliability of four prominent Large Language Models (LLMs)—Microsoft Bing, Claude, ChatGPT 3.5, and Perplexity—in offering clinical decision support for initial imaging for suspected pulmonary embolism (PE). Methods Open-ended (OE) and select-all-that-apply (SATA) questions were crafted, covering four variants of case scenarios of PE in-line with the American College of Radiology Appropriateness Criteria. These questions were presented to the LLMs by three radiologists from diverse geographical regions and setups. The responses were evaluated based on established scoring criteria, with a maximum achievable score of 2 points for OE responses and 1 point for each correct answer in SATA questions. To enable comparative analysis, scores were normalized (score divided by the maximum achievable score). Result In OE questions, Perplexity achieved the highest accuracy (0.83), while Claude had the lowest (0.58), with Bing and ChatGPT each scoring 0.75. For SATA questions, Bing led with an accuracy of 0.96, Perplexity was the lowest at 0.56, and both Claude and ChatGPT scored 0.6. Overall, OE questions saw higher scores (0.73) compared to SATA (0.68). There is poor agreement among radiologists' scores for OE (Intraclass Correlation Coefficient [ICC] = −0.067, p = 0.54), while there is strong agreement for SATA (ICC = 0.875, p < 0.001). Conclusion The study revealed variations in accuracy across LLMs for both OE and SATA questions. Perplexity showed superior performance in OE questions, while Bing excelled in SATA questions. OE queries yielded better overall results. The current inconsistencies in LLM accuracy highlight the importance of further refinement before these tools can be reliably integrated into clinical practice, with a need for additional LLM fine-tuning and judicious selection by radiologists to achieve consistent and reliable support for decision-making.

Optimizing Student Support. A Review of the Use of AI Chatbots in Higher Education

Optimizing Student Support. A Review of the Use of AI Chatbots in Higher Education

Towards interculturally adaptive conversational AI

Abstract Among the many ways that AI technologies are becoming embedded in our social worlds is the proliferation of Conversational User Interfaces, such as voice assistants (e.g. Apple Siri and Amazon Alexa), chatbots and voice-based conversational agents. Such conversational AI technologies are designed to draw upon the designers’ understanding of interactional practices employed in human–human conversation, and therefore have implications for intercultural communication (ICC). In this paper, we highlight some of the current shortcomings of conversational AI, and how these relate to ICC. We also draw on findings from Conversation Analysis to discuss how pragmatic norms vary across linguacultural groups (see Risager, Karen. 2019. Linguaculture. In Carol A. Chapelle (ed.). Encyclopedia of applied linguistics. Wiley-Blackwell for a discussion of the term ‘linguaculture’), noting that this poses further challenges for designers of conversational AI systems. We argue that the solution is to work towards what we call interculturally adaptive conversational AI. Finally, we propose a framework for how this can be conceptualised and researched, and argue that researchers with expertise in language and ICC are uniquely placed to contribute to this endeavour.

Examining Chinese preservice mathematics teachers’ adoption of AI chatbots for learning: Unpacking perspectives through the UTAUT2 model

Examining Chinese preservice mathematics teachers’ adoption of AI chatbots for learning: Unpacking perspectives through the UTAUT2 model

Feeling rules in artificial intelligence: norms for anger management

The rapid spread of conversational AI, as well as the potential for personal conversations with chatbots, makes it relevant to examine what norms and values underlie chatbot responses. This article examines the feeling rules for anger implicitly communicated by a recent chatbot (ChatGPT). Querying the chatbot about appropriate and inappropriate anger, the study shows how specific feeling rules are articulated by AI. The chatbot communicates norms of productive, respectful, constructive, controlled and calm expression of anger through talk and, as such, relies on communication as a pervasive cultural repertoire. Based on a rereading of Boltanski and Thévenot’s (2006) economies of worth focusing on feeling rules, it is argued that different moral repertoires have implications for feeling rules. Using this theoretical framework to analyse the responses of the chatbot, it is evident that it primarily relies on both the industrial and the domestic orders of worth to assess anger. The chatbot articulates the problem of anger as unproductiveness and disrespect. The feeling rules implied in the responses of the chatbot reflect a neoliberal conception of self as individually responsible, productive, self-regulating, emotionally competent and able to find solutions. The seemingly neutral advice of the chatbot potentially depoliticises anger, disciplines people to remain productive and respectful and narrows the scope of anger expressions that are deemed acceptable.

Comprehensive analysis of human and AI task allocation in the education sector: Defining futuristic roles and responsibilities

This research explores the integration of AI chatbots in higher education, examining their impact on teaching practices, learning outcomes and student engagement. AI's ability to predict and adapt offers transformative potential for educational environments, shifting skill demands and enhancing accessibility. Using the Technological Pedagogical Content Knowledge (TPACK) framework, we assess the integration of AI tools by educators. At the same time, the Technology Acceptance Model (TAM) gauges teacher’s perceptions and Constructivist Learning Theory (CLT) explores AI's support for interactive, student-centered learning. Our developed framework includes the roles and responsibilities of human educators and AI chatbots, strategies for maximizing educational benefits and addressing challenges and best practices for fostering human-AI partnerships and promoting inclusivity. The findings reveal that AI chatbots can significantly reduce administrative burdens on educators, offer personalized learning experiences, and enhance real-time feedback. This study underscores the importance of teacher training and ethical considerations in AI integration. Ultimately, we aim to contribute to the effective integration of AI in education, promoting a balanced synergy between human and technological efforts to enhance educational practices.

Effects of Adolescents’ Peer Group Norms on Intention to Stop Unethical Behaviors, Closeness to and Satisfaction with AI Chatbots : The Moderating Role of Message Framing

Effects of Adolescents’ Peer Group Norms on Intention to Stop Unethical Behaviors, Closeness to and Satisfaction with AI Chatbots : The Moderating Role of Message Framing