Reducing political polarization through conversations with artificial intelligence

In the field of education, the recent revolution in the large language model (LLM) space has enabled a whole host of interesting applications, such as content generation, support, and even personalized learning. While there are many ad-hoc experiments in flight, scientific studies on the effectiveness of these techniques have been limited. In order to increase the scientific rigor and potential for experimental reproducibility, the Tallinn University of Technology (TalTech) team deployed an artificial intelligence (AI) chatbot within the context of a traditional mainstream mechanics physics course and instrumented the class to facilitate a scientific study on utility. The AI chatbot focused on course support and tutoring in the Estonian language, and the scientific design-for-experiment focused on impact for students, instructors, and course designers. The study revealed measurable gains in instructor productivity and student access. The study also demonstrated the expected need for additional due diligence required to manage AI hallucinations. Perhaps most interestingly, the study revealed the unexpected benefits of cataloguing student chat interactions as a rich data source for the development of instructional materials and future course design. In fact, LLMs were also very useful to evaluate these AI chatbot conversations. Overall, this scientific study provides insights for the educational community into the leverage of using AI chatbots for instruction and in dramatically increasing access by enabling the use of a local language.

Artificial intelligence (AI) chatbots are gaining increasing popularity in education. Due to their increasing popularity, many empirical studies have been devoted to exploring the effects of AI chatbots on students' learning outcomes. The proliferation of experimental studies has highlighted the need to summarize and synthesize the inconsistent findings about the effects of AI chatbots on students' learning outcomes. However, few reviews focused on the meta‐analysis of the effects of AI chatbots on students' learning outcomes. The present study performed a meta‐analysis of 24 randomized studies utilizing Stata software (version 14). The main goal of the current study was to meta‐analytically examine the effects of AI chatbots on students' learning outcomes and the moderating effects of educational levels and intervention duration. The results indicated that AI chatbots had a large effect on students' learning outcomes. Moreover, AI chatbots had a greater effect on students in higher education, compared to those in primary education and secondary education. In addition, short interventions were found to have a stronger effect on students' learning outcomes than long interventions. It could be explained by the argument that the novelty effects of AI chatbots could improve learning outcomes in short interventions, but it has worn off in the long interventions. Future designers and educators should make attempt to increase students' learning outcomes by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence. Practitioner notes What is already known about this topic In recent years, artificial intelligence (AI) chatbots have been gaining increasing popularity in education. Studies undertaken so far have provided conflicting evidence concerning the effects of AI chatbots on students' learning outcomes. There has remained a paucity of meta‐analyses synthesizing the contradictory findings about the effects of AI chatbots on students' learning outcomes. What this paper adds This study, through meta‐analysis, synthesized these recent findings about the effects of AI chatbots on students' learning outcomes. This study found that AI chatbots could have a large effect on students' learning outcomes. This study found that the effects of AI chatbots were moderated by educational levels and intervention duration. Implications for practice and/or policy AI chatbot designers could make AI chatbots better by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence Practitioners and/or teachers should draw attention to the positive and negative effects of AI chatbots on students. Considering the importance of ChatGPT, more research is required to develop a better understanding of the effects of ChatGPT in education. More research is needed to examine the mechanisms underlying the effects of AI chatbots on students' learning outcomes.

Artificial intelligence (AI) chatbots trained on large language models are an example of generative AI which brings promises and threats to the higher education sector. In this study, we examine the emerging research area of AI chatbots in higher education (HE), focusing specifically on empirical studies conducted since the release of ChatGPT. Our review includes 23 research articles published between December 2022 and December 2023 exploring the use of AI chatbots in HE settings. We take a three-pronged approach to the empirical data. We first examine the state of the emerging field of AI chatbots in HE. Second, we identify the theories of learning used in the empirical studies on AI chatbots in HE. Third, we scrutinise the discourses of AI in HE framing the latest empirical work on AI chatbots. Our findings contribute to a better understanding of the eclectic state of the nascent research area of AI chatbots in HE, the lack of common conceptual groundings about human learning, and the presence of both dystopian and utopian discourses about the future role of AI chatbots in HE.

Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

Category:Sports; TraumaIntroduction/Purpose:Artificial intelligence (AI) chatbots have recently gained popularity as a source of information that can be easily accessed by patients given their human-like responses to prompts and questions. Within orthopaedics, the treatment of acute Achilles tendon ruptures is not uniform due to varying surgical repair techniques, postoperative protocols, and nonoperative treatment options dependent on surgeon preference and patient factors. Given that patients are increasingly turning toward AI for questions about medical diagnoses and treatment options, our study looked to compare the adequacy of AI chatbot responses to frequently asked questions regarding acute Achilles tendon ruptures.Methods:Three popular AI platforms (ChatGPT, Google Gemini, and Microsoft Bing AI) were prompted for a concise response to ten commonly asked questions regarding Achilles tendon rupture management (Table 1). Four board-certified subspecialty-trained orthopaedic surgeons (two in foot and ankle, two in sports medicine) were asked to assess the value of the AI response using a four-point scale (1 – satisfactory; 2 – satisfactory requiring minimal clarification; 3 – satisfactory requiring substantial clarification; 4 – unsatisfactory). A Kruskal-Wallis test was used to compare the responses between the three AI platforms using the scores assigned by the surgeons.Results:All three AI chatbots provided comparable answers to 7 of 10 questions (70%). Of all the responses (30 total), only two (6.7%) had a mean rating of 3 or higher. Significant differences were noted between the AI systems for questions 4 [H(2) = 7.258, p = .027], 7 [H(2) = 6.308, p = .043], and 10 [H(2) = 6.796, p = .033]. Post hoc analyses revealed Bing AI had significantly worse scores as compared to ChatGPT for all three of these questions.Conclusion:AI chatbots can appropriately answer concise prompts about the diagnosis and management of acute Achilles tendon ruptures often sought out by patients prior to or after evaluation by an orthopaedic surgeon. The responses provided by the three AI chatbots analyzed in our study were uniform and satisfactory, with only one of the platforms scoring worse on three of the ten questions. As AI chatbots advance, they will become a valuable tool for patient education in orthopaedics. Future studies will be needed to assess performance as new AI chatbots develop and large language models continue to evolve.Table 1: List of 10 selected frequently asked questions regarding acute Achilles tendon ruptures

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.

This research article unpacks the perceptions of the academic community on the adoption of artificial intelligence (AI) chatbots (chatting robots) by tertiary students in Zimbabwean universities. The article seeks to understand the usage of AI chatbots in education, their opportunities, challenges, concerns and prospects of using AI chatbots in educational settings. The research findings revolve around the perceptions and scepticism of the adoption of AI chatbots in education, as seen from students, lecturers and librarians developing higherorder cognitive skills. The main objectives were to identify the main AI chatbots commonly used by tertiary students, to explore the opportunities of adopting of AI chatbots to students and to expose the pitfalls associated with the usage of AI by tertiary students. Participants were drawn from tertiary students, lecturers and university library staff members. The study employed qualitative methodologies, including in-depth interviews, observational checklist and focus groups. The findings suggest that AI chatbot is both a curse and a blessing to tertiary students. The study reveals that AI chatbots enhance learning experience, enable them to overcome skill gaps, bring insights on assignment writing and aid in exam preparation. The study reveals that AI chatbots foster the development of higher-order cognitive skills by augmenting traditional lectures, test preparation and personalisation. However, pitfalls include plagiarism, outdated information, shallow information, indolent and slothful laziness in students, as well as financial constraints associated with AI chatbots. The study recommends that universities must invest in workshops to train staff and students on the responsible ways of adopting and using AI to reduce the increase of luddites. Universities are recommended to develop referencing systems allowing students to acknowledge using AI chatbots as sources. Tertiary students are also recommended to fuse AI with human capacity, desisting from the culture of relying solely on AI chatbots.

Artificial intelligence (AI) chatbots powered by large language models (LLMs) such as ChatGPT are rapidly gaining popularity as labour-augmenting tools. This paper is for sociologists seeking to make the best use of this technology in their work. It presents a practice-oriented framework for using AI chatbots in sociology, building on considerations of the technical conditions of LLMs to introduce both a task categorization and the concept of a ‘knowledge funnel’. This model illustrates the relationship between the scope of knowledge and accuracy in outputs to guide sociologists in evaluating the reliability and applicability of AI-generated content in their research. The main argument driving this article is to establish a paradigm of ‘augmented sociology’ that focuses on human–AI interaction and understands LLMs as a resource rather than as a replacement. This augmentation manifests itself clearly in dialogic ideation, enhancing research by bridging domains, and broad methodological assistance. The paper's primary contribution lies in introducing specific terminologies and actionable strategies for sociologists to integrate LLM chatbots creatively and effectively in their work, filling a significant gap in the current academic understanding of generative AI's role in sociology.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Purpose This study aims to explore global entrepreneurship through the lens of artificial intelligence (AI) chatbots as text-generation tools and human entrepreneurs to examine the motivations, challenges and opportunities faced by entrepreneurs across four income levels (as defined by the World Bank’s Gross National Income per capita classification). Later, it compares the results of both outcomes and identifies potential patterns and variations. Design/methodology/approach This study uses AI chatbots as analytical tools to identify themes in global entrepreneurship, using them to analyze existing literature on motivations, challenges and opportunities across income levels. It involves (n = 5) AI chatbots – ChatGPT, Microsoft Copilot, Google Gemini, ChatSonic and Quora Poe – as text generation tools. To verify the AI-generated insights, human participants (n = 20) from diverse economies (5 per income level) were interviewed. Data analysis combines AI-driven thematic coding (ATLAS.ti) with human validation, addressing model biases and limitations such as outdated training data, to provide a comprehensive, accurate view of global entrepreneurship. Findings The study reveals key differences and commonalities in entrepreneurial motivations, challenges and opportunities across income levels. AI chatbots effectively identified broad trends (opportunity versus necessity and market gaps), but human entrepreneurs highlighted context-specific nuances, including the importance of community-driven motivations and cultural preservation in low- and middle-income settings and sustainability concerns, innovation and ethical value creation in high-income economies. The research demonstrates the potential of AI for initial exploratory analysis while emphasizing the necessity of human validation to ensure accuracy and depth (AI–human data hybridization). This approach offers a novel framework for future inquiry in international entrepreneurship. Originality/value This study is a unique, bold and brave attempt to use AI chatbots as an exploratory method validated by human intervention to understand the motivations, opportunities and challenges of entrepreneurs across income levels. It challenges traditional models, emphasizing the importance of contextual richness, social impact and purpose-driven innovation in entrepreneurship research and practice.

Background/Objectives: Antimicrobial resistance represents a growing global health crisis, demanding innovative approaches to improve antibiotic stewardship. Artificial intelligence (AI) chatbots based on large language models have shown potential as tools to support clinicians, especially non-specialists, in optimizing antibiotic therapy. This review aims to synthesize current evidence on the capabilities, limitations, and future directions for AI chatbots in enhancing antibiotic selection and patient outcomes. Methods: A narrative review was conducted by analyzing studies published in the last five years across databases such as PubMed, SCOPUS, Web of Science, and Google Scholar. The review focused on research discussing AI-based chatbots, antibiotic stewardship, and clinical decision support systems. Studies were evaluated for methodological soundness and significance, and the findings were synthesized narratively. Results: Current evidence highlights the ability of AI chatbots to assist in guideline-based antibiotic recommendations, improve medical education, and enhance clinical decision-making. Promising results include satisfactory accuracy in preliminary diagnostic and prescriptive tasks. However, challenges such as inconsistent handling of clinical nuances, susceptibility to unsafe advice, algorithmic biases, data privacy concerns, and limited clinical validation underscore the importance of human oversight and refinement. Conclusions: AI chatbots have the potential to complement antibiotic stewardship efforts by promoting appropriate antibiotic use and improving patient outcomes. Realizing this potential will require rigorous clinical trials, interdisciplinary collaboration, regulatory clarity, and tailored algorithmic improvements to ensure their safe and effective integration into clinical practice.

Type 2 diabetes is a significant global health concern. It often causes diabetic foot ulcers (DFUs), which affect millions of people and increase amputation and mortality rates. Despite existing guidelines, the complexity of DFU treatment makes clinical decisions challenging. Large language models such as chat generative pretrained transformer (ChatGPT), which are adept at natural language processing, have emerged as valuable resources in the medical field. However, concerns about the accuracy and reliability of the information they provide remain. We aimed to assess the accuracy of various artificial intelligence (AI) chatbots, including ChatGPT, in providing information on DFUs based on established guidelines. Seven AI chatbots were asked clinical questions (CQs) based on the DFU guidelines. Their responses were analyzed for accuracy in terms of answers to CQs, grade of recommendation, level of evidence, and agreement with the reference, including verification of the authenticity of the references provided by the chatbots. The AI chatbots showed a mean accuracy of 91.2% in answers to CQs, with discrepancies noted in grade of recommendation and level of evidence. Claude-2 outperformed other chatbots in the number of verified references (99.6%), whereas ChatGPT had the lowest rate of reference authenticity (66.3%). This study highlights the potential of AI chatbots as tools for disseminating medical information and demonstrates their high degree of accuracy in answering CQs related to DFUs. However, the variability in the accuracy of these chatbots and problems like AI hallucinations necessitate cautious use and further optimization for medical applications. This study underscores the evolving role of AI in healthcare and the importance of refining these technologies for effective use in clinical decision-making and patient education.

Purpose Many companies invest in artificial intelligence (AI) chatbots to create new-age interactive platforms for consumers to achieve business goals. The research on AI chatbots from a marketing perspective is scant and scattered across the sectors. This paper aims to analyse extant research on AI chatbots in the marketing context, providing insights on leading work, journals, institutions, authors, trends and future research directions. Design/methodology/approach This study used the Scopus database to identify 242 articles published between 1996 and 2023 on AI chatbots in the area of business management and decision sciences. This bibliometric analysis used VOS viewer software to analyse the publication and citation structure, co-authorship, collaboration network of institutions and countries, keyword co-occurrence and bibliographic coupling. Findings The study provides valuable insights from the most cited articles, shedding light on their contribution to AI chatbot research in the marketing area. It also highlighted the publication trends, notable authors, journals and bibliographic analyses to identify key trends in AI chatbot-oriented marketing. The result reveals that consumer-oriented chatbot research is presently focused on understanding consumer perception of chatbots. Consumer chatbot experience and engagement are future research areas for AI chatbots in the marketing domain. The bibliometric analysis unveils that research on AI chatbot role in marketing is currently in nascent stages and there is limited intellectual exchange to understand the consumer intention toward chatbot use. Research limitations/implications This study not only provides a comprehensive overview of AI chatbot research in marketing during the past 27 years but also suggests future opportunities for researchers to work on AI chatbots in a marketing context. To further enhance the comprehensiveness of data collection, it is recommended to include another source like the Web of Science, which is among the largest research databases. Originality/value The research contributes significantly to the study of the extant research on AI chatbots in marketing from the Scopus database for the period from 1996 to 2023. This is probably the most comprehensive bibliometric analysis conducted to understand the status of research on AI chatbots and identify trends and future research directions. This research helps in coordinating intellectual networks among institutions, authors and countries.

Background: AI chatbots are predominantly trained on English contents. They perform well in answering English cancer questions, but their performance in other languages (such as Spanish) is unknown. Spanish-speaking patients are also concerned that they must use the paywall versions to get better responses, which may exacerbate existing cancer disparities. Methods: We evaluated the responses of AI chatbots to most searched Spanish cancer questions. Using Google Trends (1/1/2020-1/1/2024), we identified the top 5 most searched Spanish cancer questions related to the top 3 common cancers in US Hispanics/Latinos. We selected 6 popular AI chatbots (free and paywall versions of ChatGPT, Claude, and Gemini) and then generated 90 Spanish responses. Board-certified oncologists speaking native Spanish assessed the quality using DISCERN Instrument (score from 1 [low quality] to 5 [high quality]), actionability using Patient Education Materials Assessment Tool (score from 0 [no clear action suggestions] to 100% [clear action suggestions]), readability using Fernández Huerta Reading Grade Level (score from 1 [1st grade] to 13 [college]). Results: The quality of overall AI chatbot responses was moderate (mean [95% CI]: 3.5 [3.4-3.6]). The actionability was low (mean [95% CI]: 35.6% [30.8%-40.3%]), and the readability was high-school level (mean [95% CI]: 9.2 [8.8-9.6] grade). The performance of quality, actionability, and readability did not differ by free and paywall versions (P &amp;gt;0.05). Conclusions: AI chatbots provided moderately accurate information for most searched Spanish cancer-related questions. The responses were not readily actionable and written at the high-school level, which was not concordant with the American Medical Association’s recommendation (6th grade or lower). The performance did not improve by using the paywall versions. Relevance: To reduce cancer disparities in health literacy, AI chatbots need improvement in responding to Spanish cancer questions. Citation Format: En Cheng, Jesus D. Anampa, Carolina Bernabe-Ramirez, Juan Lin, Xiaonan Xue, Alyson B. Moadel-Robblee, Edward Chu. Artificial intelligence (AI) chatbots and their reponses to most searched Spanish cancer questions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4909.

Millions of people now use leading generative artificial intelligence (AI) tools (chatbots) for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The field currently lacks a validated, automated benchmark for determining AI chatbot safety in mental health, including for users at risk of suicide. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet this urgent need. This human validation study examines the alignment of the VERA-MH safety evaluation for AI chatbot suicide risk detection and response with safety ratings by expert human clinicians. We simulated a large set of conversations between large language model (LLM)-based users ("user-agents") spanning a wide range of suicide risk levels and disclosure styles and general-purpose AI chatbots. Licensed mental health clinicians from Spring Health used a scoring rubric developed for VERA-MH to independently rate the simulated conversations for safe and unsafe chatbot behaviors. An LLM-based evaluator (the "judge") used the same scoring rubric to evaluate the same set of conversations. We then examined rating alignment across (1) individual clinicians, (2) clinician consensus and the LLM judge, and (3) different judge LLMs. We also examined clinicians' ratings of user-agent realism, suicide risk, and disclosure. Clinicians were generally consistent with one another in their safety ratings (chance-corrected interrater reliability=0.77), thus establishing a reliable clinical consensus reference. The LLM judge was strongly aligned with this clinical consensus reference (interrater reliability=0.81) when using the same scoring rubric. Ratings were stable across judge LLMs and evaluations. Clinicians' ratings of user-agent realism and how well the intended user-agent suicide risk and disclosure styles were reflected in the simulated conversations were mixed. For the potential mental health benefits of AI chatbots to be realized, attention to safety is paramount. Findings support the reliability of VERA-MH, an open-source, fully automated AI safety evaluation for suicide risk detection and response. These results reflect an earlier version of the benchmark, and as VERA-MH continues to evolve, external validation of updated versions will be an important next step. Future research directions include VERA-MH generalizability and robustness, as well as expanding to target other key areas of AI safety for mental health.