Chatbot Research Articles

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.

Membership categorisation, sociological description and role prompt engineering with ChatGPT

Large Language Models (LLMs) and generative Artificial Intelligence (A.I.) have become the latest disruptive digital technologies to breach the dividing lines between scientific endeavour and public consciousness. LLMs such as ChatGPT are platformed through commercial providers such as OpenAI, which provide a conduit through which interaction is realised, via a series of exchanges in the form of written natural language text called ‘prompt engineering’. In this paper, we use Membership Categorisation Analysis to interrogate a collection of prompt engineering examples gathered from the endogenous ranking of prompting guides hosted on emerging generative AI community and practitioner-relevant social media. We show how both formal and vernacular ideas surrounding ‘natural’ sociological concepts are mobilised in order to configure LLMs for useful generative output. In addition, we identify some of the interactional limitations and affordances of using role prompt engineering for generating interactional stances with generative AI chatbots and (potentially) other formats. We conclude by reflecting the consequences of these everyday social-technical routines and the rise of ‘ethno-programming’ for generative AI that is realised through natural language and everyday sociological competencies.

Merevolusi Pendidikan dengan Kecerdasan Buatan Chatbots: Meningkatkan Pembelajaran dan Penilaian

This study explores the integration of AI chatbots in education to enhance learning and assessment. Focusing on their implementation in classroom teaching and small-scale scientific inquiry activities, the research aims to improve interactions between teachers and students by introducing new learning topics, methods, and providing feedback. The study involved 18 participants 11 fifth-grade and 7 sixth-grade students from a university-run science talent program. Findings highlight benefits such as increased engagement, personalized learning experiences, and automated assessment capabilities. Examples from language learning, mental health support, and academic advising illustrate chatbots' versatility in education. Overall, AI chatbots have the potential to transform education by offering tailored learning experiences, enhancing student engagement, and facilitating efficient assessment. The study suggests further exploration of chatbots' role in promoting student initiative within scientific inquiry learning.

AI chatbots show promise but limitations on UK medical exam questions: a comparative performance study

Large language models (LLMs) like ChatGPT have potential applications in medical education such as helping students study for their licensing exams by discussing unclear questions with them. However, they require evaluation on these complex tasks. The purpose of this study was to evaluate how well publicly accessible LLMs performed on simulated UK medical board exam questions. 423 board-style questions from 9 UK exams (MRCS, MRCP, etc.) were answered by seven LLMs (ChatGPT-3.5, ChatGPT-4, Bard, Perplexity, Claude, Bing, Claude Instant). There were 406 multiple-choice, 13 true/false, and 4 "choose N" questions covering topics in surgery, pediatrics, and other disciplines. The accuracy of the output was graded. Statistics were used to analyze differences among LLMs. Leaked questions were excluded from the primary analysis. ChatGPT 4.0 scored (78.2%), Bing (67.2%), Claude (64.4%), and Claude Instant (62.9%). Perplexity scored the lowest (56.1%). Scores differed significantly between LLMs overall (p < 0.001) and in pairwise comparisons. All LLMs scored higher on multiple-choice vs true/false or “choose N” questions. LLMs demonstrated limitations in answering certain questions, indicating refinements needed before primary reliance in medical education. However, their expanding capabilities suggest a potential to improve training if thoughtfully implemented. Further research should explore specialty specific LLMs and optimal integration into medical curricula.

Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots

Abstract AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics and well-being. Despite burgeoning interest in human–AI relationships, the conversational and emotional nuances of real-world, in situ human–AI social interactions remain underexplored. Through computational analysis of a multimodal dataset with over 35,000 screenshots and posts from r/replika, we identified seven prevalent types of human–AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, customization, transgression, and communication breakdown, and examined their associations with six basic human emotions. Our findings suggest the paradox of emotional connection with AI, indicated by the bittersweet emotion in intimate encounters with AI chatbots, and the elevated fear in uncanny valley moments when AI exhibits semblances of mind in deep self-disclosure. Customization characterizes the distinctiveness of AI companionship, positively elevating user experiences, whereas transgression and communication breakdown elicit fear or sadness.

Light bulbs have energy ratings - so why can't AI chatbots?

Light bulbs have energy ratings - so why can't AI chatbots?