Artificial Intelligence Chatbot Research Articles

The ethical dimensions of utilizing Artificial Intelligence in palliative care

Palliative care aims to improve the quality of life for seriously ill individuals and their caregivers by addressing their holistic care needs through a person- and family-centered approach. While there have been growing efforts to integrate Artificial Intelligence (AI) into palliative care practice and research, it remains unclear whether the use of AI can facilitate the goals of palliative care. In this paper, we present three hypothetical case examples of using AI in the palliative care context, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers’ unmet needs. Using these cases, we examine the ethical dimensions of utilizing AI in palliative care by applying five widely accepted moral principles that guide ethical deliberations in AI: beneficence, nonmaleficence, autonomy, justice, and explicability. We address key ethical questions arising from these five core moral principles and analyze the potential impact the use of AI can have on palliative care stakeholders. Applying a critical lens, we assess whether AI can facilitate the primary aim of palliative care to support seriously ill individuals and their families. We conclude by discussing the gaps that need to be further addressed in order to promote ethical and responsible AI usage in palliative care.

Assessing Yemeni university students' public perceptions toward the use of artificial intelligence in healthcare.

Artificial intelligence (AI) integration in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates university students' perceptions of using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants' views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied to compare variables. Two hundred seventy-nine (279) students completed the questionnaire. More than half of the participants (52%, n = 145) expressed their belief in AI's potential to reduce treatment errors. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the "Benefits and Positivity Toward AI in Healthcare" and "Concerns and Fears" domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.

Assessing Biases in the Names Generated by Generative Artificial Intelligence Chatbots

Assessing Biases in the Names Generated by Generative Artificial Intelligence Chatbots

Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.

Artificial intelligence (AI) chatbots such as ChatGPT are expected to impact vision health care significantly. Their potential to optimize the consultation process and diagnostic capabilities across range of ophthalmic subspecialties have yet to be fully explored. This study aims to investigate the performance of AI chatbots in recommending ophthalmic outpatient registration and diagnosing eye diseases within clinical case profiles. This cross-sectional study used clinical cases from Chinese Standardized Resident Training-Ophthalmology (2nd Edition). For each case, 2 profiles were created: patient with history (Hx) and patient with history and examination (Hx+Ex). These profiles served as independent queries for GPT-3.5 and GPT-4.0 (accessed from March 5 to 18, 2024). Similarly, 3 ophthalmic residents were posed the same profiles in a questionnaire format. The accuracy of recommending ophthalmic subspecialty registration was primarily evaluated using Hx profiles. The accuracy of the top-ranked diagnosis and the accuracy of the diagnosis within the top 3 suggestions (do-not-miss diagnosis) were assessed using Hx+Ex profiles. The gold standard for judgment was the published, official diagnosis. Characteristics of incorrect diagnoses by ChatGPT were also analyzed. A total of 208 clinical profiles from 12 ophthalmic subspecialties were analyzed (104 Hx and 104 Hx+Ex profiles). For Hx profiles, GPT-3.5, GPT-4.0, and residents showed comparable accuracy in registration suggestions (66/104, 63.5%; 81/104, 77.9%; and 72/104, 69.2%, respectively; P=.07), with ocular trauma, retinal diseases, and strabismus and amblyopia achieving the top 3 accuracies. For Hx+Ex profiles, both GPT-4.0 and residents demonstrated higher diagnostic accuracy than GPT-3.5 (62/104, 59.6% and 63/104, 60.6% vs 41/104, 39.4%; P=.003 and P=.001, respectively). Accuracy for do-not-miss diagnoses also improved (79/104, 76% and 68/104, 65.4% vs 51/104, 49%; P<.001 and P=.02, respectively). The highest diagnostic accuracies were observed in glaucoma; lens diseases; and eyelid, lacrimal, and orbital diseases. GPT-4.0 recorded fewer incorrect top-3 diagnoses (25/42, 60% vs 53/63, 84%; P=.005) and more partially correct diagnoses (21/42, 50% vs 7/63 11%; P<.001) than GPT-3.5, while GPT-3.5 had more completely incorrect (27/63, 43% vs 7/42, 17%; P=.005) and less precise diagnoses (22/63, 35% vs 5/42, 12%; P=.009). GPT-3.5 and GPT-4.0 showed intermediate performance in recommending ophthalmic subspecialties for registration. While GPT-3.5 underperformed, GPT-4.0 approached and numerically surpassed residents in differential diagnosis. AI chatbots show promise in facilitating ophthalmic patient registration. However, their integration into diagnostic decision-making requires more validation.

Pemanfaatan Artificial Intellegence (AI) dalam Meningkatkan Inklusi Ekonomi dan Keuangan

In the current digital era, economic and financial inclusion is a crucial issue in various countries including Indonesia. The use of artificial intelligence (AI) has great potential to improve economic and financial inclusion by providing easier, more efficient, and affordable access to financial and economic services for those who were previously marginalized. This research aims to identify Artificial Intelligence (AI) can be used to provide more inclusive financial services to those who are underserved, as well as to analyze its impact on the overall economic welfare of the community, especially Gorontalo city. This research used a descriptive qualitative approach, involving case studies and interviews with stakeholders. The results show that the utilization of Artificial Intelligence (AI) chatbots and virtual assistants has a significant impact on service and user satisfaction, as well as providing confidence in decision making so as to increase economic and financial inclusion for the community

Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.

Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates the precision of ChatGPT-4 in interpreting multimodal images for paediatric cardiology knowledge assessment, including echocardiograms, angiograms, X-rays, and electrocardiograms. One hundred multiple-choice questions with accompanying images from the textbook Pediatric Cardiology Board Review were randomly selected. The chatbot was prompted to answer these questions with and without the accompanying images. Statistical analysis was done using X2, Fisher's exact, and McNemar tests. Results showed that ChatGPT-4 answered 41% of questions with images correctly, performing best on those with electrocardiograms (54%) and worst on those with angiograms (29%). Without the images, ChatGPT-4's performance was similar at 37% (difference = 4%, 95% confidence interval (CI) -9.4% to 17.2%, p = 0.56). The chatbot performed significantly better when provided the image of an electrocardiogram than without (difference = 18, 95% CI 4.0% to 31.9%, p < 0.04). In cases of incorrect answers, ChatGPT-4 was more inconsistent with an image than without (difference = 21%, 95% CI 3.5% to 36.9%, p < 0.02). In conclusion, ChatGPT-4 performed poorly in answering image-based multiple-choice questions in paediatric cardiology. Its accuracy in answering questions with images was similar to without, indicating limited multimodal image interpretation capabilities. Substantial training is required before clinical integration can be considered. Further research is needed to assess the clinical reasoning skills and progression of ChatGPT in paediatric cardiology for clinical and academic utility.

Detecting and Reducing Gender Bias in Spanish Texts Generated with ChatGPT and Mistral Chatbots: The Lovelace Project

Current Artificial Intelligence (AI) systems can effortlessly and instantaneously generate text, images, songs, and videos. This capability will lead us to a future where a significant portion of available information will be partially or wholly generated by AI. In this context, it is crucial to ensure that AI-generated texts and images do not perpetuate or exacerbate existing gender biases. We examined the behavior of two common AI chatbots, ChatGPT and Mistral, when generating text in Spanish, both in terms of language inclusiveness and perpetuation of traditional male/female roles. Our analysis revealed that both tools demonstrated relatively low gender bias in terms of reinforcing traditional gender roles but exhibited higher gender bias concerning language inclusiveness, at least in the Spanish language. Additionally, although ChatGPT showed lower overall gender bias than Mistral, Mistral provided users with more control to modify its behavior through prompt modifiers. As a final conclusion, while both AIs exhibit some degree of gender bias in their responses, this bias is significantly lower than the gender bias present in their human-authored source materials.

KNOWLEDGE, ATTITUDES, AND PRACTICES REGARDING CHATBOTS AMONG HEALTHCARE PROFESSIONALS IN A TERTIARY CARE HOSPITAL

Objective: (1) Primary: Determine healthcare professionals’ knowledge, attitudes, and practices (KAP) related to AI Chatbots. (2) Secondary: Assess healthcare professionals’ perspectives on using Chatbots as teaching tools and implementing them in the Competency-Based Medical Education curriculum. Methods: An online questionnaire was distributed to 132 health professionals, including faculty and CRMIs, through Google Forms. Data on artificial intelligence (AI)-related KAP and barriers were collected following IHEC approval. The KAP scores, along with the relationship between categorical variables – such as population type and the use of AI Chatbots – were analyzed using Statistical Package for the Social Sciences software. Results: The study revealed that participants had a moderate level of knowledge about AI Chatbots. Attitudes were mixed, with some skepticism about AI replacing human teachers but also recognition of its benefits. Most participants infrequently used AI Chatbots in their daily activities. Barriers to usage included lack of knowledge, limited access, time constraints, and curriculum gaps. Conclusion: This study underscored the need to enhance medical education with AI topics and address existing barriers. It is crucial to better prepare health professionals for AI integration to leverage AI’s potential for improving patient care and training.

Bias Sensitivity in Diagnostic Decision-Making: Comparing ChatGPT with Residents.

Diagnostic errors, often due to biases in clinical reasoning, significantly affect patient care. While artificial intelligence chatbots like ChatGPT could help mitigate such biases, their potential susceptibility to biases is unknown. This study evaluated diagnostic accuracy of ChatGPT against the performance of 265 medical residents in five previously published experiments aimed at inducing bias. The residents worked in several major teaching hospitals in the Netherlands. The biases studied were case-intrinsic (presence of salient distracting findings in the patient history, effects of disruptive patient behaviors) and situational (prior availability of a look-alike patient). ChatGPT's accuracy in identifying the most-likely diagnosis was measured. Diagnostic accuracy of residents and ChatGPT was equivalent. For clinical cases involving case-intrinsic bias, both ChatGPT and the residents exhibited a decline in diagnostic accuracy. Residents' accuracy decreased on average 12%, while the accuracy of ChatGPT 4.0 decreased 21%. Accuracy of ChatGPT 3.5 decreased 9%. These findings suggest that, like human diagnosticians, ChatGPT is sensitive to bias when the biasing information is part of the patient history. When the biasing information was extrinsic to the case in the form of the prior availability of a look-alike case, residents' accuracy decreased by 15%. By contrast, ChatGPT's performance was not affected by the biasing information. Chi-square goodness-of-fit tests corroborated these outcomes. It seems that, while ChatGPT is not sensitive to bias when biasing information is situational, it is sensitive to bias when the biasing information is part of the patient's disease history. Its utility in diagnostic support has potential, but caution is advised. Future research should enhance AI's bias detection and mitigation to make it truly useful for diagnostic support.

Artificial Intelligence Chatbots (ChatGPT and Google Gemini) Versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery: Correspondence.

Artificial Intelligence Chatbots (ChatGPT and Google Gemini) Versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery: Correspondence.

Can ChatGPT help patients understand radiopharmaceutical extravasations?

A previously published paper in the official journal of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) concluded that the artificial intelligence chatbot ChatGPT may offer an adequate substitute for nuclear medicine staff informational counseling to patients in an investigated setting of 18F-FDG PET/CT. To ensure consistency with the previous paper, the author and a team of experts followed a similar methodology and evaluated whether ChatGPT could adequately offer a substitute for nuclear medicine staff informational counseling to patients regarding radiopharmaceutical extravasations. We asked ChatGPT fifteen questions regarding radiopharmaceutical extravasations. Each question or prompt was queried three times. Using the same evaluation criteria as the previously published paper, the ChatGPT responses were evaluated by two nuclear medicine trained physicians and one nuclear medicine physicist for appropriateness and helpfulness. These evaluators found ChatGPT responses to be either highly appropriate or quite appropriate in 100% of questions and very helpful or quite helpful in 93% of questions. The interobserver agreement among the evaluators, assessed using the Intraclass Correlation Coefficient (ICC), was found to be 0.72, indicating good overall agreement. The evaluators also rated the inconsistency across the three ChatGPT responses for each question and found irrelevant or minor inconsistencies in 87% of questions and some differences relevant to main content in the other 13% of the questions. One physician evaluated the quality of the references listed by ChatGPT as the source material it used in generating its responses. The reference check revealed no AI hallucinations. The evaluator concluded that ChatGPT used fully validated references (appropriate, identifiable, and accessible) to generate responses for eleven of the fifteen questions and used generally available medical and ethical guidelines to generate responses for four questions. Based on these results we concluded that ChatGPT may be a reliable resource for patients interested in radiopharmaceutical extravasations. However, these validated and verified ChatGPT responses differed significantly from official positions and public comments regarding radiopharmaceutical extravasations made by the SNMMI and nuclear medicine staff. Since patients are increasingly relying on the internet for information about their medical procedures, the differences need to be addressed.

ChatGPT as a Source for Patient Information on Patellofemoral Surgery—A Comparative Study Amongst Laymen, Doctors, and Experts

Introduction: In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. Methods: A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann–Whitney U Test. A p-value of less than 0.05 was considered statistically significant. Results: The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. Conclusion: ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of “AI hallucinations” remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study.

Recent surveys indicate that 48% of consumers actively use generative artificial intelligence (AI) for health-related inquiries. Despite widespread adoption and the potential to improve health care access, scant research examines the performance of AI chatbot responses regarding emergency care advice. We assessed the quality of AI chatbot responses to common emergency care questions. We sought to determine qualitative differences in responses from 4 free-access AI chatbots, for 10 different serious and benign emergency conditions. We created 10 emergency care questions that we fed into the free-access versions of ChatGPT 3.5 (OpenAI), Google Bard, Bing AI Chat (Microsoft), and Claude AI (Anthropic) on November 26, 2023. Each response was graded by 5 board-certified emergency medicine (EM) faculty for 8 domains of percentage accuracy, presence of dangerous information, factual accuracy, clarity, completeness, understandability, source reliability, and source relevancy. We determined the correct, complete response to the 10 questions from reputable and scholarly emergency medical references. These were compiled by an EM resident physician. For the readability of the chatbot responses, we used the Flesch-Kincaid Grade Level of each response from readability statistics embedded in Microsoft Word. Differences between chatbots were determined by the chi-square test. Each of the 4 chatbots' responses to the 10 clinical questions were scored across 8 domains by 5 EM faculty, for 400 assessments for each chatbot. Together, the 4 chatbots had the best performance in clarity and understandability (both 85%), intermediate performance in accuracy and completeness (both 50%), and poor performance (10%) for source relevance and reliability (mostly unreported). Chatbots contained dangerous information in 5% to 35% of responses, with no statistical difference between chatbots on this metric (P=.24). ChatGPT, Google Bard, and Claud AI had similar performances across 6 out of 8 domains. Only Bing AI performed better with more identified or relevant sources (40%; the others had 0%-10%). Flesch-Kincaid Reading level was 7.7-8.9 grade for all chatbots, except ChatGPT at 10.8, which were all too advanced for average emergency patients. Responses included both dangerous (eg, starting cardiopulmonary resuscitation with no pulse check) and generally inappropriate advice (eg, loosening the collar to improve breathing without evidence of airway compromise). AI chatbots, though ubiquitous, have significant deficiencies in EM patient advice, despite relatively consistent performance. Information for when to seek urgent or emergent care is frequently incomplete and inaccurate, and patients may be unaware of misinformation. Sources are not generally provided. Patients who use AI to guide health care decisions assume potential risks. AI chatbots for health should be subject to further research, refinement, and regulation. We strongly recommend proper medical consultation to prevent potential adverse outcomes.

AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information.

This study investigates the accuracy of Artificial Intelligence (AI) chatbots, ChatGPT and Bard, in providing information on Hidradenitis Suppurativa (HS), aiming to explore their potential in assisting HS patients by offering insights into symptoms, thus possibly reducing the diagnostic and treatment time gap. Using questions formulated with the help of HS patient associations, both ChatGPT and Bard were assessed. Responses to these questions were evaluated by 18hS experts. ChatGPT's responses were considered accurate in 86% of cases, significantly outperforming Bard, which only achieved 14% accuracy. Despite the general efficacy of ChatGPT in providing relevant information across a range of HS-related queries, both AI systems showed limitations in offering adequate advice on treatments. The study identifies a significant difference in the performance of the two AIs, emphasizing the need for improvement in AI-driven medical advice, particularly regarding treatment options. The study highlights the potential of AI chatbots, particularly ChatGPT, in supporting HS patients by improving symptom understanding and potentially reducing the time to diagnosis and treatment. AI chatbots, while promising, cannot yet substitute for professional medical diagnosis and treatment, indicating the importance of enhancing AI capabilities for more accurate and reliable medical information dissemination.

Comparative analysis of communication strategies impact on the effectiveness of interaction with an AI chatbot

Introduction. The integration of artificial intelligence (AI) into everyday human interactions, particularly through chatbots, has significantly increased in recent years. AI-based chatbots successfully mimic human conversations by understanding user requests and responding appropriately. Improving the effectiveness of educational and professional activities related to text processing in these conditions seems extremely relevant and predetermines the goal of our study: comparing the effectiveness of interaction with ChatGPT when formulating queries in various communication strategies. Procedure and methods. The study on the effectiveness of interacting with ChatGPT was conducted using a natural experiment scheme with preliminary training. The subjects of the study were 34 employees (15 men, 19 women, aged 25-36; M = 31.6; SD = 1.8) from the marketing department of the company "Technologies of Trust". These employees performed rewriting tasks by interacting with ChatGPT within the framework of specially adapted communication strategies (persuasive and cooperative) and evaluated the chatbot's usability and the quality of the text generated by ChatGPT. The Wilcoxon signed-rank test for dependent samples was used to identify the effects of communication strategies on interaction efficiency, and exploratory factor analysis with subsequent varimax rotation was employed to identify latent variables of interaction under different communication strategies. Research Results. Comparison of the results of the ChatGPT interaction performance evaluation when using 2 strategies (cooperative and persuasive) showed that for all 11 usability indicators, there is a significant advantage of the cooperative communicative strategy at the significance level of p&lt;0.05. However, on 6 indicators of text quality obtained through rewriting, the persuasive strategy shows advantages and on 4 of them at a significance level of p&lt;0.05. In the course of the study it was found that the factor solutions for the studied communicative strategies differ significantly: the key factor in using the persuasive strategy is speed and accuracy in achieving goals, while in the cooperative strategy the adequate response of chatbots to objections is critical. Conclusion. The results obtained in the study contribute significantly to the understanding of communication with AI chatbots, highlighting the nuances of interacting through linguistic means typical for human communication. The findings demonstrate the complexity of choosing the optimal strategy for interacting with ChatGPT and uncover the factors that determine the features of subjective evaluation of the interaction.

Correspondence to the Editor: Performance of Multimodal Artificial Intelligence Chatbots Evaluated on Clinical Oncology Cases

Correspondence to the Editor: Performance of Multimodal Artificial Intelligence Chatbots Evaluated on Clinical Oncology Cases

Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey.

Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.

Artificial Intelligence Chatbots in Action: Optimizing Benefits Enrollment in Public Administration

This article examines the implementation of artificial intelligence-powered chatbots in public sector benefits enrollment processes, focusing on their potential to streamline operations, enhance user experience, and reduce administrative burdens. Through a comprehensive analysis of case studies in healthcare and social security benefits programs, we demonstrate that chatbots can significantly improve efficiency, accuracy, and accessibility in enrollment procedures. Our findings indicate a 30-40% reduction in processing times and a marked increase in application completion rates. However, challenges related to data privacy, system integration, and user acceptance persist. This article contributes to the growing body of literature on digital transformation in public administration by providing empirical evidence of chatbots' effectiveness in benefits enrollment. We conclude that while chatbots offer promising solutions to longstanding issues in public sector service delivery, their successful implementation requires careful consideration of technical, ethical, and user-centric factors. Our study has important implications for policymakers and public administrators seeking to leverage AI technologies to enhance public service efficiency and accessibility

The impact of different conversational generative AI chatbots on EFL learners: An analysis of willingness to communicate, foreign language speaking anxiety, and self-perceived communicative competence

Based on the Interaction Hypothesis, the study investigates the impact of different conversational Generative Artificial Intelligence (GenAI) chatbots on English as a Foreign Language (EFL) learners’ willingness to communicate (WTC), foreign language speaking anxiety (FLSA), self-perceived communicative competence (SPCC) and speaking performance. Three groups of Chinese undergraduate students were recruited: a control group (CG, N = 33) and two experimental groups (EG1, N = 33; EG2, N = 33). The CG interacted with the teacher and classmates during the speaking class. In contrast, EG1 interacted with a text- and voice-based conversational GenAI chatbot called Typebot, while EG2 engaged with a conversational GenAI chatbot that featured both text and voice interaction along with human-like avatars named D-ID Agent. Quantitative analysis using multilevel modelling revealed that EG2 showed significant improvements in WTC and SPCC and a notable reduction in FLSA levels compared to CG. However, the pre- and post-speaking test results showed no significant differences in speaking performance across the groups. Qualitative data from semi-structured interviews supported these findings, highlighting the immersive learning experience and emotional support provided by the human-like avatars. These results suggest that visually embodied GenAI chatbots can effectively enhance the emotional experience during the language learning. The study provides practical insights for language educators on integrating GenAI technologies in language teaching, emphasising the benefits of human-like avatars in fostering a more engaging and supportive learning environment.

Collaboration of Artificial Intelligence and Journalists in Online Media from the Perspective of Human-Machine Communication

This study aims to analyze the collaboration between Artificial Intelligence (AI) and journalists in online media using the Human-Machine Communication (HMC) approach. The research method used is a literature study. Data is processed using Miles and Huberman data analysis techniques. The results of this study indicate that AI is used in three primary stages of journalism: news gathering, news production, and news distribution. At the news gathering stage, AI assists journalists in collecting news materials from various sources and analyzing audience interest in specific topics. AI is used in news script creation, editing, and proofreading at the news production stage. AI chatbots and NLP programs help with automatic news writing and factual verification. Meanwhile, at the news distribution stage, AI is used for content personalization, news recommendations, and SEO optimization in online media. From the HMC perspective, collaboration between AI and journalists can be conceptualized as a unidirectional and two-way process. Collaboration between AI and journalists in a social context also occurs at the micro, meso, and macro levels, where interactions between humans and machines affect the social situation, the immediate reality of individuals, and the structure of society as a whole.