A Critical Perspective Over Whether and How to Acknowledge the Use of Artificial Intelligence (AI) in Qualitative Studies

Artificial Intelligence (AI) has had far-reaching effects in research and the academic world. It has been used in many ways by the scientific community within the context of qualitative research, such as literature and systematic reviews, for conceptualization purposes, thematic and content analysis. It has however prompted concerns and questions about the potential for unreliable research, bias, and unethical behavior in the outcomes of AI-produced research. The purpose this paper is to examine the current use of AI in research, its strengths and limitations, dilemmas and ethical considerations from theoretical critical perspective principles, while delivering five key considerations for the appropriate, rigorous, and reliable use of AI in research practice. The first step is to become acquainted with the data generated by AI systems. The second is concerned with removing biased content and addressing ethical concerns when using AI, while the third is concerned with cross-referencing information generated by AI. The fourth step is to control the analysis process. The fifth and most important key consideration is the demonstration of cognitive input and skills by the researcher throughout the process of using AI in any qualitative research study and in reaching conclusions.

Guest editorial: What's common with qualitative nursing research these days?

Building Blocks for Critical Reviews in Health Professions Education

Who is sensitising whom? A participatory interview guide development as an awareness tool within a health care research project

Background and aim:Researchers, in qualitative researches, both influences on and take effect from the research process. One of the mainissues in qualitative research is validity of the researcher as an instrument of data collection. If the researcher doesnot have enough validity in the data collection, the results of the study will also not be cited. The researcher asinstrument provides an opportunity for researchers to enter into the unknown world of individual about thephenomena in question and sometimes faced many challenges in reaching this goal. This study has been reviewingthe opportunities and challenges of researchers as an instrument in the qualitative research.Materials and methods:This was a review study on the methodology of qualitative research. Using keywords including qualitative research,instrument, challenges, and opportunities, articles and available books were investigated in PubMed, Scopus,Science Direct, Proquest, Magiran iSCI iIranDoc, SID, Medlib, IranMedex databases with no time limit. TheQuality of the articles was assessed by using the McMaster Critical Review Form for Qualitative studies.Findings:Studies over 12 books and 18 articles showed that the role of the researcher can be varied in different qualitativeresearch designs such as phenomenology researches, grounded theory, ethnography, and content analysis researches.Also previous experience and skills in the process of observation and interview, having effective communicationand asking the appropriate questions have an important impact on the role of researchers as a key factor inqualitative research.Conclusions:Researchers have the main role, especially in data gathering of different types of qualitative researches. 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Artificial intelligence (AI) will revolutionize health science education. And it is happening now. The most recent reason? ChatGPT—a newly available Chatbot AI with pronounced synthesis and language capabilities (OpenAI, 2023). Like previous tech interventions and platforms, such as Twitter, that revolutionized communication and influenced research and public discourses in the health sciences, ChatGPT will not only influence health education, practice and research, but will shift them profoundly. While the time to fully pre-empt ChatGPT development is past, the opportunity for nursing to respond well has not. But first, ChatGPT and its possible impacts on higher education in the health sciences must be considered. Here, we introduce ChatGPT, highlight its likely impact on higher education, and what nursing and the health sciences can do about it. ChatGPT is an AI trained as an interactive conversational model chatbot capable of responding to prompts in various text formats (Gleason, 2022; OpenAI, 2023). ChatGPT runs off of GPT-3 (Generative Pre-Trained Transformer-3), the technology underlying its ability to understand and generate text. This means the application can perform more sophisticated functions in response to users' entries, including—seeking and clarifying through follow-up questions, challenging underlying definitions, and stating and questioning assumptions—among many. There are clear parallels between this and many other scholarly discourses—including the production and evaluation of student writing, conference conversations, and academic publications (Kamler & Thomson, 2006). These sophisticated functions have quickly raised attention. The AI laboratory, OpenAI, only launched ChatGPT on December 2, 2022 and has already gained attention in the mass media and academic press (Gleason, 2022; Graham, 2022; Wingard, 2023) While Chatbots and AI have existed for approximately 60 and 70 years, respectively (Ina, 2022), ChatGPT is different. It will have a transformative effect on higher education, especially around writing and student work. First, it is "generative." ChatGPT can create new text based on a range of inputs, avoiding the rote and repetitive responses of other AI chatbots—a glaring clue for any customer using a commercial AI customer service chat they are not, after all, having their complaint heard by a real person. ChatGPT can also demonstrate sensitivity to context and, from this, it can generate text that sounds natural—more human. Second, ChatGPT is a free to use application, thereby removing the pay-to-access barrier experienced by students seeking to access other AI applications. Given this, and its remarkable capacities, ChatGPT had over 1 million users within the first 5 days of its release (Gleason, 2022). Third, ChatGPT provides virtually instant, comprehensive and logical text responses in any format and genre requested of it (including numerous essay, prose, tweets, LinkedIn posts, or columns). Crucially, some authors report that this text is undetectable by current plagiarism software. For instance, Gleason (2022) asserted that there is no way to prove that ChatGPT generated text is AI generated. Fourth, the scale and complexity of ChatGPT is remarkable; it is among the largest language models ever created with 175 billion parameters, enabling ChatGPT to avoid rigid, script-based responses (OpenAI, 2023). The ability of ChatGPT to respond like a human would is its greatest strength—and paradoxically its greatest risk in scenarios in which a human response is necessary for ethical and scholarly standards and integrity. The possible impacts of ChatGPT on higher education are transformative, and ChatGPT is testing our ability to envision and respond to these. Should those in nursing and other parts of higher education integrate ChatGPT in the service of learning, avoid considering its impact altogether, or acknowledge it but prohibit students' engagement, assuming then that students will abide by this regulation? To address these vital questions first, we asked ChatGPT what its likely impacts are going to be on higher education. Its response was as follows: It is worth noting that GPT-3, although powerful, is not a silver bullet solution for all problems in education and proper implementation and ethical use of the technology is important. Also, there may be some concerns with privacy and security when using GPT-3 in educational settings." ChatGPT was able to provide this coherent, cogent and very human-like response within 10s of receiving the query. It was equally capable of responding meaningfully to follow-up prompts, providing additional information, depth and clarity in its responses, and composing tweets and LinkedIn postings to address the topic. As ChatGPT itself indicated, its profile in higher education is one of balancing benefits with risks, and hence, considering various stances towards its use can help educators and educational institutions makes such critical decisions. Here, we present three different hypothetical case scenarios reflecting responses to ChatGPT by nursing and the health sciences, postulating possible impacts for each. Previously, we argued that Twitter was a conversation that would be ongoing regardless of nursings' acceptance of it (Archibald & Clark, 2014). Nursing and other health professions face a similar, but more challenging decision about whether or not to respond to the emergence of ChatGPT. With already pressing concerns—avoidance is tempting and superficially may feel good, at least initially. Avoidance may stem from many causes, including: fear, a lack of awareness of the existence of this emergent platform and technology, a lack of appreciation of the full scope of its capabilities and possible impacts, or ignorance or dismissal of the possible influence ChatGPT may have on higher education. Yet in the famous words of Abraham Lincoln: "You cannot escape the responsibility of tomorrow by evading it today." Ignoring the existence of ChatGPT or avoiding its inevitable impact on higher education will result in multi-level harms almost immediately. It would be a grave mistake for nursing and the health sciences for many reasons. First, there will be no structures in place to ensure the integrity of student learning, particularly since safeguards would not be in place to determine if student writing was AI generated. Students are writing essays with ChatGPT at the very moment we write this. We are already behind the curve. Second, a 'head-in-the-sand' perspective does little for the professional images of nursing and other health professions. Rather, avoidance may have the negative effect of highlighting a lack of timeliness, or at worst, even make approaches to professional education appear irrelevant. Third, students will not have the benefit of integrating ChatGPT into their learning, meaning the opportunity to treat ChatGPT as another learning tool will be depleted. Fourth, an avoidance approach means that shortcomings in ChatGPT cannot be critically analysed, again undercutting the potential application as a learning tool. By avoiding the use of ChatGPT in higher education, educators also avoid the risk of manifold and serious privacy and security concerns, which ChatGPT adeptly highlighted for us. Without safeguards, students' personal data could be subject to unauthorized access, or other forms of misuse. If not secured, this information could be used for nefarious purposes. For those overseeing nursing (and indeed other professions health education), this risk extends not only to academia and education, but also to public safety. In the prohibition stance, nursing and health science educators and educational institutions take a strong stance against the use of ChatGPT, positioning its use—like essay writing mills—as a direct threat to academic integrity. Such a stance would require the use of browser lockdowns and student oaths or agreements within a punitive lens or model founded on distrust and codified in more bureaucracy (e.g., in forms and declarations). Patrolling and enforcing such measures will be resource intensive and would, in great probability, be ineffective given the complexity of monitoring a large student body, and the possibilities that students may ingeniously find ways to bypass instituted lockdown measures. Further, students may respond with frustration to such restrictive measures that appear to undermine the larger proportion of students who choose to think and act ethically in their scholarly conduct—and truly own their personal ethics and commitment to academic integrity. This may seem antithetical to the adult educational model central to higher education and educational institutions. Moreover it draws attention to the importance of education and prevention in ethical conduct—focusing on helping students (especially those early in their studies) understand what scholarly integrity is and why it is important, not only for the credibility of their eventual qualifications but more widely for public safety and the public good. While the prohibition stance carries with it many of the same shortcomings and challenges encountered in the avoidance stance, such as the inability for students to learn from ChatGPT, there is similarly a risk that prohibition prevents educators from developing students' critical appraisal of ChatGPT's outputs. Specifically, as a powerful tool capable of synthesizing and integrating large and disparate volumes of web-based information, ChatGPT will both reflect and then reproduce and amplify extant biases and stereotypes in this literature. Students require guidance to identify that ChatGPT may replicate these biases, what these biases are, and to formulate their critiques. Such exercises could be powerful learning opportunities for students. Like an avoidance stance, a prohibition stance fundamentally neglects that patients—as consumers of health care—are likely to turn to ChatGPT for knowledge regarding their health concerns or questions. This may, like Internet searching, amplify the individual information seeking behaviours that occur in the absence of access to or support from nurses and other healthcare providers. Similar to Internet searching which is not in itself problematic, nurses and other health professionals must have a working knowledge of ChatGPT—including its shortcomings in providing health information—in order to effectively support and educate individuals within their care. By avoiding or prohibiting the use of ChatGPT in higher education, institutions are less capable of preparing future health professionals for this forthcoming reality. A third possible stance of nursing and the health professions is one of integration of ChatGPT into educational processes and assessments. We align with and advocate for this pragmatic and forward thinking choice—one that accepts the likelihood and inevitable ubiquitous use of ChatGPT in nursing and health science education (similar to the Internet), and leverages, rather than dismisses, its potential implications and harms. However, such integration requires educators to re-imagine assessment to emphasize process over end point, such as the essay as the terminal output for grading (Gleason, 2022). For instance, Gleason (2022) proposes that educators require students to generate a ChatGTP text and use this in comparison to the course readings and objectives to critically appraise the content. Given that ChatGPT can replicate biases present in online text, facilitating such critical appraisal can prove useful pedagogically in developing vital critical and reflections skills—rendering these skills to be more and not less valuable due to the ChatGPT platform. ChatGPT itself indicates that it is fundamentally up to students to abide by and uphold principles of academic integrity (OpenAI, 2023). Contrary to some literature emerging on this topic that stipulates plagiarism detection software is ineffective in detecting ChatGPT generated content (e.g., Gleason, 2022), ChatGPT itself indicates that software exists that is sensitive to ChatGPT generated text (OpenAI, 2023). A team of investigators in the United Kingdom investigated, running student short form essay responses through the plagiarism software Grammarly and TurnitIn with scores of 2% ± 1% and 7% ± 2%, respectively (Yeadon et al., 2022). Based on these scores and the favourable grading that these essays received, these authors concluded that ChatGPT is a major threat to fidelity in their academic context. It is clear that educational institutions must ensure academic integrity policies and detection software is in place, staff are sufficiently attuned and trained to ChatGPT including the current shortcomings and forthcoming advances in plagiarism software, to mitigate the risk of inappropriate use of ChatGPT. Seen as a valid platform that lacks an inherent hostility to academic and professional integrity, ChatGPT should be used—similar to the Internet—fairly and in a manner aligned with student's skill levels. The tool can support complexity, however, learning and applying the tool demands a skillset that must be learned. As such, an integration stance requires rapid adaptation of educational institutions to ensure appropriate staff training is provided on its ethical use and practical applications, comprehensive policies are in place to guide educator and student behaviour, and that timely and responsive risk assessment and mitigation plans are in place to revisit the use and integration of ChatGPT at frequent intervals, given the rapidity of its ongoing development. In conclusion, with the advent and remarkable uptake of ChatGPT, health professionals and educators now face an important decision. As we have inferred above, it is a decision fused for each of us, and collectively for our institutions, with a mix of emotion, projection and reaction. ChatGPT will touch up each—but it is what disciplines, institutions, and people do in response that matters most. Do we choose to see and handle ChatGPT as a tool that, despite its risks, can not only co-exist with but can also improve student learning when approached appropriately? Or do we regard this technology as oppositional and antithetical to learning and scholarly ethics, governed by fear of its misuse and lack of awareness of its potential? How learners engage with higher degree and professional training, and how evaluation can occur within higher education will all be transformed by the public availability of ChatGPT. Despite the possible desire towards a knee-jerk reaction prohibiting its use in education, a more tempered solution is likely the most viable: balance the privacy, security and academic integrity risks with the possible benefits of its application. This position exerts intention, agency, and influence over how ChatGPT will shape higher education (Archibald & Barnard, 2018). Even within an integrative model, educators must recognize the rapid advancements and forthcoming scalability of ChatGPT. The volume of input data used to train ChatGPT is continually advancing. New techniques are being developed to reduce bias in ChatGPT's modelling, which will contribute to the power and applicability of ChatGPT in the future—further heightening its human-like responses, its strengths and its risks. As such, ensuring a flexible process of assessment and resulting institutional approaches and policies will be paramount to keeping pace with the current and rapidly evolving state of this AI technology. In the words of Fasano and White (1982), "by identifying the possibilities (of the future), we can decide more wisely what we should do today to create a better world for tomorrow" (p. 20). All authors have agreed on the final version and meet at least one of the following criteria (recommended by the ICMJE*): (1) substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content. This writing received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No conflict of interest has been declared by the authors.

This paper examines the ontology of artificial intelligence (AI) within the context of contemporary society. With the rapid progression of technology, the definition of legal subjects has become increasingly ambiguous, as the technological landscape continues to evolve. The orthodox perspective fails to provide adequate solutions to this problem. An alternative approach, as put forth by Visa A.J. Kurki’s bundle theory offers a potential pathway, yet AI’s intrinsic nature surpasses the minimum thresholds defined by Kurki’s model. The authors propose a periscopic model that explores the interaction between the material world and the virtual or augmented sphere, often referred to as the metaverse. This article contends that the current philosophical foundation of law is both outdated and insufficient, primarily due to the shift from singular to plural forms of agency. AI has transitioned from being purely instrumental or intermediary, as observed in Artificial Narrow Intelligence (ANI), to autonomous decision-making entities, exemplified by Artificial General Intelligence (AGI). Drawing on theoretical insights from Yuval Noah Harari, the paper underscores the need for a new conceptual framework to address AI’s lack of a material entity. In conclusion, the paper asserts that the recognition of AI as legal subjects is an inevitable development.

Abstract. The aim of the article is to explore the possibilities and challenges of integrating artificial intelligence (AI) into teaching English (TE). This aim provides for solving the following tasks of research: evaluating the effectiveness of AI in improving students’ learning outcomes, the potential for personalized learning, the automation of feedback, and the impact of AI on the role of the teacher; investigating the potential issues that arise in the context of AI integration, including data privacy concerns, access to technology, and the need for professional development. Methodology. Achieving the goal and solving the tasks have been enhanced by using such methods of research as qualitative and quantitative analyses, case studies, survey research (the survey questions were designed to gather both quantitative data (such as the frequency of AI tool usage) and qualitative feedback (such as perceived effectiveness and ease of use), content analysis (reviewing learning platforms, mobile apps, and AI-powered language assistants to assess their educational content, usability, and effectiveness in fostering language skills), and comparative analysis. Scientific novelty. It is reflected in its multi-dimensional approach to AI integration in teaching English, focusing on a specialized, non-traditional educational institution − the PAU. By exploring AI’s potential in such a unique context, the research offers new insights into how AI can be applied to meet the specific professional language needs of cadets preparing for careers in the penitentiary system. This study expands the body of knowledge regarding AI in education and provides a foundation for future research and practical implementation of AI tools in specialized educational settings. Research results. The article examines the integration of artificial intelligence (AI) in education, specifically focusing on TE and its implications for academic integrity. It highlights AI tools’ transformative potential, such as intelligent tutoring systems, automated feedback mechanisms, and conversational agents in enhancing personalized learning, engagement, and skill development. The research provides insights into how AI supports language proficiency improvement while addressing challenges like ethical concerns, teacher training, and technical limitations. Using the PAU as a case study, the article explores the unique applications of AI in specialized educational settings, emphasizing the importance of ethical AI use and ongoing research. Practical implications. Integrating artificial intelligence (AI) in TE opens new possibilities for personalized learning, automated feedback, and improving language skills. However, the research also emphasizes the importance of maintaining academic integrity, particularly in the context of potential misuse of AI tools. Using tools such as Grammarly, Duolingo, and ChatGPT significantly enhances grammar, vocabulary, pronunciation, and writing proficiency. Interactive platforms adapt to individual learners’ needs, providing a flexible and effective learning environment. AI complements, but does not replace, the role of the teacher. Educators remain crucial in motivating students, offering emotional support, and addressing complex issues beyond AI’s capabilities. Successful AI integration requires teacher training to use these technologies effectively. Technical difficulties, limited technological access, and AI’s inability to fully understand cultural context need attention. Ethical concerns, such as data privacy and preventing algorithmic biases, remain important issues. Value (originality). The value of the study is characterized by the presentation of evaluating the effectiveness of AI in improving students’ learning outcomes, the potential for personalized learning of English, the automation of feedback, and the impact of AI on the role of the teacher; investigating the potential issues that arise in the context of AI integration, including data privacy concerns, access to technology, and the need for professional development for teachers. Key words: AI in education, teaching English (TE), academic integrity, personalized learning, intelligent tutoring systems, automated feedback, ethical AI use.

The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need strategies to navigate these complexities and maintain rigorous, ethical assessments that promote higher order cognitive skills. This article provides practical guidance for educators on designing take-home assessments (e.g. research-based assignments) in the AI era. This guidance was developed through a collaborative, consensus-driven process involving a consortium of three educators with diverse academic backgrounds, career stages, and perspectives on AI in education. Members, holding experience in higher education across the United Kingdom, United States of America, Australia, and Middle East and North Africa regions, brought varied insights into AI's role in education. The team engaged in an iterative process of refining recommendations through biweekly virtual meetings and offline discussions. Four key recommendations are presented 1) codeveloping AI literacy among students and educators, 2) designing assessments that prioritize process over output, 3) validating learning through AI-free assessments, and 4) preparing students for AI-enhanced workplaces by developing AI communication skills and promoting human-AI collaboration. These strategies emphasize ethical AI use, personalized feedback, and creativity. By adopting these approaches, educators can balance the benefits and risks of AI in assessments, fostering authentic learning while preparing students for the challenges of an AI-driven world.NEW & NOTEWORTHY This paper presents a framework to effectively design take-home assessments in the generative artificial intelligence (AI) era with four key recommendations to navigate the challenges and opportunities posed by generative AI. From codeveloping AI literacy to fostering human-AI collaboration, the strategies empower educators to promote authentic learning, critical thinking, and ethical AI use. Adaptable to various contexts, these insights help prepare students for an AI-driven future while maintaining academic rigor and integrity.

BACKGROUND With the rapid development and iteration of generative artificial intelligence, the growing popularity of such groundbreaking tools among nurse researchers, represented by ChatGPT, is receiving passionate debate and intrigue. Although there has been qualitative research on generative artificial intelligence in other fields, little is known about the experiences and perceptions of nurse researchers, and this study seeks to report on the subject. OBJECTIVE This study aimed to describe the experiences and perceptions of generative artificial intelligence among Chinese nurse researchers. Provide a reference for the application of generative artificial intelligence in nursing research in the future. METHODS Semi-structured interviews were used to collect data in this qualitative study. Data were analyzed employing inductive content analysis. RESULTS Five themes and twelve sub-themes were categorized from 27 original interview documents as follows: (1) Diverse reflections on human-machine symbiosis, which includes the interplay between substitution, researchers shaping the potential space of generative artificial intelligence, and researchers accepting generative artificial intelligence with alacrity; (2) Heterogeneity of groups and experiences, including diversity in experiences of using and heterogeneity in the perception and use among different groups; (3) Research paradigm reshaping in the infancy stage, which involves a groundbreaking auxiliary tool in nursing research and the incubation of innovative research paths; (4) Ethical concerns and application challenges, considering insight into the public opinion around generative artificial intelligence, academic integrity and medical ethical challenges, and limitations on application in nursing research; (5) Future development and capacity reinforcement, which concerns reinforcement needs for utilization competency and collaboration and exploration in future nursing research. In this context, the first four themes form the rocket of the human-machine symbiosis journey. Only when humans fully leverage the advantages of machines (generative artificial intelligence) and overcome the shortcomings of them, can this human-machine symbiosis journey reach towards the correct future direction (fifth theme). CONCLUSIONS This study explored the experiences and perceptions of nurse researchers interacting with generative artificial intelligence, which was a "symbiotic journey" full of windings. The human-machine interaction process relentlessly moves nurse researchers to improve scientific literacy, digital literacy, and prompt skills. Meanwhile, the potential hazards and concerns of this topic for nurse researchers became apparent, with an emphasis on academic integrity, drafting relevant specifications, and the accuracy of generated content. Collaboration with interdisciplinary professionals, utilizing supervised fine-tuning, knowledge graphs, and retrieval augmented generation techniques, to develop nursing research-specific multimodal artificial general intelligence was expected to meet the individual needs of nurse researchers.

Artificial intelligence (AI) has emerged as a transformative force in second language (L2) education, reshaping teaching and learning methodologies. This chapter explores AI's impact on L2 educators and learners through insights from questionnaires and a follow-up conversation. Initial apprehension towards AI is counterbalanced by curiosity about its potential to enhance educational practices. The chapter provides practical guidance, showcasing how AI tools can be aligned with key language learning skills and offering structured examples of activities to enhance these skills. It highlights AI's role in providing immediate feedback, simplifying complex concepts, and creating inclusive classrooms tailored to individual learning styles and needs. The discussion also addresses educators' recognition of AI's potential and underscores the need for clear guidelines and training in ethical AI implementation. As AI technology evolves, it promises a more personalized, dynamic educational journey, enriching the L2 learning process.

Today, artificial intelligence (AI)-based research assistants are used in various stages of qualitative studies, including methodology, data collection, group interviews, writing, editing, and qualitative data analysis (1). However, it seems that chatbots can also be used as a data source in human-computer interaction (2). One of the key elements in qualitative research is reaching theoretical saturation, meaning that data collection reaches a stage where no new data is generated, and the researcher considers continuing the interview unnecessary (3). Perhaps at this stage, conversations with chatbots can be used as a complementary or even alternative data source in qualitative study interviews. Obviously, all aspects related to entry and exit criteria, such as the interviewee's previous experiences and cultural backgrounds, which are very important in the interview, must be observed. Perhaps AI can access diverse data from a wide range of sources to produce conceptually rich and relevant data and provide new perspectives. However, research integrity must be respected, but not necessarily in the same way as human studies. For example, we cannot define and identify specific inclusion criteria such as the real work experience of a human in an organization, the years of experience of a patient with a disease in real conditions, or the cultural and ideological backgrounds of the participant in the case of a chatbot. Therefore, interviewing with chatbots does not yield theoretical saturation and may produce incomplete and artificial results. Thus, in addition to the transparency of research and data collection, it is also necessary to define the framework for the ethical and correct use of chatbots instead of humans in interviews. This editorial highlights a new perspective on the use of AI-based chatbots in qualitative research, where the chatbot serves as a data source rather than as an analyst, methodologist, or assistant writer. Although AI provides opportunities for qualitative research, it also faces challenges that reviewers and authors should be aware of until the necessary technology is developed. Some of the opportunities and challenges of using AI chatbots in qualitative research can be the following: The use of AI and recommender systems in qualitative interviews helps reduce the cost and time of research, creates a sense of security, greater comfort for the interviewee, and allows them to express information without worry and bias (4), which helps with the depth of the data. Also, when reaching people who are geographically remote or specific groups that are not easily accessible, AI chatbots trained for specific purposes can be used. However, one must also recognize the challenges ahead and address them with appropriate policies. Among the most important of these is the depth of human feelings and emotions as they may arise in specific situations, which has not yet been defined for the machine. Also, informed consent, maintaining information security, and privacy are serious challenges and ethical issues for chatbots instead of humans (5). Ultimately, chatbots may be subject to a variety of errors, not from human error but from the data available to the AI, language limitations when translating data into the researcher's language, and even in countries like Iran, where access and use of IP from other countries are restricted. These technological challenges are unavoidable. Therefore, journal editors and authors should be cautious when using chatbots for various purposes, including as a substitute or complement to interviews and a source of data collection in qualitative studies.

This article examines the opportunities and challenges presented by artificial intelligence in the scientific evaluation of the social sciences, a field that faces difficulties in quantifying the impact of its output due to the complexity and qualitative nature of its subjects. Unlike the natural sciences, social sciences do not always align well with traditional metrics, such as citations or impact indices. Artificial intelligence, through advanced tools like natural language processing and machine learning, offers alternatives to enhance these evaluation processes. This study follows an exploratory methodology, grounded in a critical literature review and content analysis, aiming to identify the potential of artificial intelligence for measuring academic and social impact within the social sciences. The literature review includes analyses of academic sources and policy documents and is structured around three key areas: improvements in evaluation metrics, innovations in social impact analysis, and proposals for implementation in social sciences. The article concludes that, although artificial intelligence enables more comprehensive evaluations, its application presents ethical challenges, especially regarding algorithmic biases and system transparency. As an original contribution, the article proposes a theoretical model to integrate qualitative and quantitative methods into a more equitable and thorough evaluation adapted to the unique nature of the social sciences. It emphasizes the importance of developing AI tools designed ethically and collaboratively.

Qualitative Research in CKD: How to Appraise and Interpret the Evidence

This study specifically investigates the significant impact of artificial intelligence (AI) technology on the dynamic environment of the tourism industry, exposing its diverse and revolutionary implications. This qualitative research aims a complete understanding of the role of artificial intelligence (AI) within the tourism industry. It employs a critical Review approach to synthesize and analyze past qualitative research, revealing details about AI's subtle impact on the tourism business. The study employs a qualitative method, using a critical review to thoroughly investigate and analyze contemporary qualitative research on artificial intelligence (AI) in the tourism industry. This technique allows for a comprehensive synthesis of the qualitative findings, resulting in a deeper understanding of the intricate relationships between AI and tourism. This research, conducted as a comprehensive synthesis and evaluation study, qualitatively summarizes data on the different applications of AI in the tourism industry, shedding light on their multiple subtleties and repercussions. This study adds to our growing understanding of AI's critical role in the tourist sector, giving significant information to academics, developers, and managers investigating the potential of artificial intelligence (AI) in tourism. The constraints of this study highlight the necessity for continuing inquiry and adaptability as AI evolves. While the SLR technique provides a solid framework for synthesizing qualitative research, it has certain drawbacks, including possible gaps in the accessible literature. This study begs for more qualitative research to capture new trends and shifting viewpoints on AI in the dynamic environment of the tourist sector.