Utilizing Artificial Intelligence to Facilitate Qualitative Surgical Research

Chapter 1: World Views, Paradigms, and the Practice of Social Science Research Case 1. Quantitative Research Case 2. Qualitative Research Thinking about the Foundations and Practice of Research What This Book Is and Is Not About. What Warrants Our Attention? The Traditional Canon Alternative Paradigms New Techniques or New Paradigms? Chapter 2: History and Context of Paradigm Development Positivism: A Response to Metaphysical and Magical Explanations Critical Theory: A Response to Inequities in Society Interpretivism: A Response to the Excesses of Scientific Social Science The Special Cases of Postmodernism and Feminism Chapter 3: Foundational Issues: Postpositivist and Critical Perspectives Social Science Research: The View from the Postpositivist Paradigm Social Science Research: The View from the Critical Theory Paradigm Chapter 4: History and Foundations of Interpretivist Research (1) Nature of Reality. (2) Purpose of Research. (3) Acceptable Methodology/Data. (4) The Meaning of Data (5) Relationship of Research to Practice. The Implications of an Interpretivist Approach What Sorts of Research are Worthwhile? Examples of Interpretive Research Chapter 5: Frameworks for Qualitative Research Postpositivist Research Moments of Qualitative Research Some General Frameworks for Qualitative Research Chapter 6: General Guidelines for Qualitative Research Guidelines for Qualitative Research Situated or Contextual Understanding, Not Truth, is the Purpose of Research Accept Multiple Sources of Influence Take A Foundational Rather Than Technique Perspective Practice Recursive (Iterative ) and Emergent Data Collection and Analysis Use Multiple Sources of Data Think of Research as a Reflective Process The Researcher is the Primary Tool for Data Collection and Analysis An Emphasis on Participatory versus Nonparticipatory Research. Adopt an Open Approach Deal With Bias Directly Select Natural Contexts for Research Research Should be Holistic, Not Atomistic Research Involves More Than Induction and Deduction: Analogical Reasoning, Abduction, and Family Resemblances Alternatives to Postpositivist Criteria for Believability: Validity and Reliability Alternative Approaches to Validity and Reliability: Triangulation and More Conclusions? Aren't They Generalizations? Chapter 7: Methods of Qualitative Research Case 1: Action Research on a Pediatric Surgical Ward Established Qualitative Research Methods Ethnography Case Studies: Another Form of Qualitative Observation Interview Research Historigraphy Historiography: The Research Methods of History Innovative Methods Participatory Qualitative Research Emancipatory Research Critical Emancipatory Action Research Chapter 8: Approaches to Data Analysis and Intepretation The Purpose of Research General Theory Objective Description Hermeneutic (Verstehen) Understanding Story telling/Narrative Data Analysis Families Eyeballing the Data Connoisseurship: A Global Perspective Hermeneutics as a Data Analysis Method Grounded Theory Analytic Induction A Final Topic: The Ethics of Research Chapter 9: 21st Century Social Science: Peering into the Future Will the Cacophony Continue? Why Can't Social Science Converge on The Answer? Competition Linearity Dialog as an Alternative to Competition Three Approaches to Knowing in Greek Thought Plato Aristotle The Humanities Choices 20th Century Social Science Made Suppose We Chose Badly Two Theories That May Help Us Build 21st Century Social Science Poetic Logic Chaos and Complexity Theory: Another Route to a Nonlinea Social Science

This article examines the integration of artificial intelligence (AI) into qualitative psychological research, focusing specifically on AI-assisted data analysis and its epistemological and ethical implications. While recent publications highlight AI’s potential to support analysis, such approaches risk undermining the reflexive, situated, and culturally sensitive foundations of qualitative inquiry. Drawing on relational and social constructionist epistemologies, as well as examining risks inherent in AI technologies, this work critiques the superficial outsourcing of analytical and interpretive processes to AI models. This trend reflects a broader tendency to regard AI as a neutral and objective research tool, rather than as an active participant whose outputs are shaped by, and in turn shape, the social, cultural, and technological contexts in which it operates. An alternative framework is proposed for integrating AI into qualitative inquiry, particularly in psychological research, where data are often sensitive, situated, and ethically complex. A list of best practices is also included and discussed. Key ethical concerns, such as data privacy, related algorithmic affordances, and the need for comprehensive informed consent, are examined. The article concludes with a call to nurture a qualitative research culture that embraces relational and reflective practices alongside a critical and informed use of AI in research.

In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews.

Artificial Intelligence (AI) and other large language models are rapidly infiltrating the world of education and educational research. These new technological developments raise questions about use and ethics throughout the world of educational research, particularly for qualitative methods given the philosophical and structural foundations of its associated designs. This paper seeks to interrogate the perceived ethics around the use of AI in qualitative research and draws on survey data from qualitative researchers (n = 101) collected from April-May 2023. Findings indicate that researchers were more apt to embrace the use of AI for transcription purposes, and to a lesser extent for preliminary coding. Researchers from high research productivity (R1) universities were generally less accepting of AI's use in the research process than other researchers.

BackgroundAs part of qualitative research, the thematic analysis is time-consuming and technical. The rise of generative artificial intelligence (A.I.), especially large language models, has brought hope in enhancing and partly automating thematic analysis.MethodsThe study assessed the relative efficacy of conventional against AI-assisted thematic analysis when investigating the psychosocial impact of cutaneous leishmaniasis (CL) scars. Four hundred forty-eight participant responses from a core study were analysed comparing nine A.I. generative models: Llama 3.1 405B, Claude 3.5 Sonnet, NotebookLM, Gemini 1.5 Advanced Ultra, ChatGPT o1-Pro, ChatGPT o1, GrokV2, DeepSeekV3, Gemini 2.0 Advanced with manual expert analysis. Jamovi software maintained methodological rigour through Cohen’s Kappa coefficient calculations for concordance assessment and similarity measurement via Python using Jaccard index computations.ResultsAdvanced A.I. models showed impressive congruence with reference standards; some even had perfect concordance (Jaccard index = 1.00). Gender-specific analyses demonstrated consistent performance across subgroups, allowing a nuanced understanding of psychosocial consequences. The grounded theory process developed the framework for the fragile circle of vulnerabilities that incorporated new insights into CL-related psychosocial complexity while establishing novel dimensions.ConclusionsThis study shows how A.I. can be incorporated in qualitative research methodology, particularly in complex psychosocial analysis. Consequently, the A.I. deep learning models proved to be highly efficient and accurate. These findings imply that the future directions for qualitative research methodology should focus on maintaining analytical rigour through the utilisation of technology using a combination of A.I. capabilities and human expertise following standardised future checklist of reporting full process transparency.

ABSTRACTBackgroundNatural language understanding (NLU), a subfield of artificial intelligence, focuses on the computational understanding of human language. This technology offers an objective and quantitative approach to analysing interviews in qualitative research. This study hypothesises that NLU can assess the impact of oral health on quality of life by analysing semi‐structured interviews.ObjectiveThis study aimed to assess the utility of NLU in evaluating oral health‐related quality of life by analysing semi‐structured interviews with individuals diagnosed with hypodontia.MethodsA cross‐sectional qualitative study was conducted on 10 participants (aged 16–25 years) suffering from hypodontia. Semi‐structured interviews were transcribed and analysed using IBM Watson NLU text analysis. The analysis identified entities, keywords, sentiments (positive and negative) and emotions (joy, sadness, anger, fear and disgust) expressed in the interviews.ResultsNLU analysis revealed a predominantly negative sentiment towards hypodontia and its management, with 93.2% of identified entities presenting a negative sentiment and only 6.8% showing a positive sentiment. Patient sentiment correlated inversely with age (R = −0.49), treatment waiting time (R = −0.22) and OHIP score (R = −20). Negative sentiments and sadness were most prominent when discussing the history of dental problems and feelings about their teeth, whereas joy and positive sentiments were expressed regarding successful dental work. Keywords associated with negative sentiment were primarily related to treatment length and delays.ConclusionNLU effectively identified patients' negative sentiments and emotional responses to oral health conditions, demonstrating its potential as a valuable tool in qualitative dental research.

Artificial intelligence (AI) has very soon changed numerous aspects, and the world of digital media is no exception. With its ability to replicate human-like intelligence and learning capabilities, AI has opened up new possibilities for enhancing various aspects of digital media creation, distribution, and consumption. From personalized content recommendations to advanced image and video editing techniques, AI applications are revolutionizing how human beings interact with digital media. This study searches for a comprehensive and systematic literature review to explore the impact of AI in the digital media world. The objective is to offer a detailed examination of how AI is being integrated into various aspects of digital media areas. This has been achieved in this study through an analysis of existing research, news and reports updates, and scholarly works. The problem statement is that there exists a lack of inclusive research examining the complex implications and applications of AI integration. The current studies more focus on specific sub-fields or isolated examples of AI adoption, resulting in a disjointed knowledge base. A complete understanding of the larger impact of AI in digital media has been described for decision-making, policy development, and strategies. The methodology used in this study is to examine the existing literature on digital media. The population is several bibliographical resources and the samples are 104 news research reports, journal papers, scholarly articles, and other sources that have been systematically reviewed and studied in this research during Jan-March 2024. The study is qualitative research and several techniques have been applied to the secondary data which are: Literary Search technique, inclusion and exclusion technique, and selection and extraction technique as the data mainly relies on secondary data. The results of this study show how AI has changed the media industry by enabling automated data analysis and enhancing content creation and curation. Furthermore, AI-powered image recognition technologies have greatly influenced visual media production. Through deep learning algorithms, computers can now accurately identify objects within images or videos. The results highlight the need for moral guidelines, skill-updating techniques, and ethical AI practices. As AI advances, we can expect even more innovative applications that will shape the future of digital media. Furthermore, the study will identify any existing research gaps and propose recommendations for future research, policy, and practical applications. While efficiency, customization, and creativity are advantages of AI, privacy issues, job displacement, and algorithmic bias require careful study. Scholars, professionals, and policymakers must hold the implications of AI's influence as it becomes increasingly universal across various societal domains.

This chapter delves into the perceptions of artificial intelligence (AI) experts on the societal implications, governance, and ethical responsibilities associated with AI. Drawing on qualitative research, including interviews with six AI experts, the study investigates three key questions: experts' perceptions of AI's societal impacts, their visions for an AI-governed society, and their views on their responsibilities in addressing AI's consequences. The findings suggest that AI experts emphasize the importance of balancing innovation with education and regulation to ensure AI's responsible development and application, leaving out important questions to analyze further, such as their role in AI political governance, and the interest of wide public participation in the process of AI creation.

PurposeThe purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.Design/methodology/approachWe adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.FindingsThe results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.Originality/valueThis study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.

Surgical research is a wide-ranging field that includes innovative procedures, novel devices, novel procedures, questioning established operative procedures, sham surgical placebo-controlled trials, perioperative care pathways, national registry studies, risk assessment modeling, and qualitative research. This chapter discusses these various types of surgical research, provides examples of surgical research trials that fit into these categories, and highlights the central ethical considerations in assessing each type of surgical research trial. Ultimately, the chapter shows that research ethics guidelines and benchmarks can be broadly applied to ethical questions regarding surgical research but must be tailored to the context of the surgical research study.

In the field of tourism, where quantitative methods are widely preferred as an interdisciplinary science, it is common for qualitative research to be handled with quantitative research designs and approaches. This situation raises questions about the quality of the studies. This study aims to determine the level of attention paid to the points that should be considered in the reporting of qualitative research in the field of tourism. In this context, the systematic review approach, which enables the systematic evaluation of the information available in the literature on a subject, was adopted. Accordingly, the COREQ checklist, one of the reporting criteria for the evaluation of qualitative research, was used. Within the scope of the research, 75 studies published in journals indexed in the TR Index in 2023 in the field of tourism were evaluated. A Custom GPT based on GPT-4o was created for the evaluation of the studies according to the COREQ checklist. As a result of the study, it was determined that the information based on the COREQ criteria was not sufficient in the studies examined, and the studies were inadequate, especially in terms of method and theory.

The recent rise of artificial intelligence represents an unprecedented revolution for our societies. AI has brought about a significant transformation in higher education, which has affected the majority of pedagogical aspects and all stakeholders in the Ministry of Higher Education and Scientific Research. This article aims to explore the perception of artificial intelligence among teachers and students. Thus, this work attempts to understand the role of AI in improving the performance of higher education in Tunisia. To this end, we carried out a state-of-the-art review on this subject. Semi-directive interviews were conducted with 12 teachers and 12 students at two Higher Institutes of Management (ISG Tunis and ISG Bizerte). Using two qualitative analysis techniques, we were able to gain insight into the perception of AI in the university world. In addition, this research enabled us to identify potential intelligent applications to be used to improve pedagogy, with the aim of minimising the potential negative effects of this new technology. This qualitative research has led us to put forward a set of evaluation factors relating to the performance of higher education in Tunisia.

The digitization era in public affairs is increasingly disrupting Indonesia. Governance in Indonesia is forced to implement aspects of digitalization in its public services. Big data and artificial intelligence have been used in other scientific activities that provide colour to governance with various scientific disciplines. This study discusses the use of big data and artificial intelligence in policymaking in Indonesia by focusing on the ability of policymakers in policymaking. The method used in this paper is qualitative research with a literature study approach. We reviewed articles with related themes as many as 25 articles published in the last five years from ScienceDirect. The result of this research is that the dynamics that exist in the implementation of public services require appropriate and fast decision-making, considering that this is a community demand. Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly by enriching skills in big data and artificial intelligence. In conclusion, the critical aspect of the public policy decision-making process is similar to that of any other stage in the policymaking process. Like the preceding steps in the public policy process, the decision-making stage differs depending on the nature of the policy subsystems involved and the degree of consensus faced by decision-makers. The presence of big data in the public sector cannot be disputed as an intriguing approach, particularly during the policy formation cycle. Also, big data and artificial intelligence can help public leaders make decisions to deliver the best policies.

The primary aim of this study is to explore how Artificial Intelligence can enhance the effectiveness of program performance evaluations. By leveraging data-driven techniques, the research aims to identify methods that facilitate more accurate assessments of program outcomes using LLM models, thereby enhancing decision-making processes. The study adopts a mixed-methods design, combining qualitative and quantitative approaches to assess the impact of Artificial Intelligence on program performance evaluation. The research was conducted over twelve months, enabling a detailed analysis of both the immediate and long-term impacts of Artificial Intelligence interventions on program management. The methodology employed in this study is structured around a comprehensive approach to data collection and analysis, ensuring robust insights into program performance evaluations. Qualitative research was conducted to identify relevant metrics for assessment. The qualitative component encompasses in-depth interviews with key stakeholders, providing insights into the contextual factors that influence analytics deployment. Concurrently, the quantitative analysis employs statistical methodologies to evaluate performance metrics both before and after the implementation of AI-driven methodologies. The study's findings highlight the critical role of Artificial Intelligence in enhancing program performance evaluation. Detailed data analysis revealed that employing Artificial Intelligence facilitates the extraction of real-time insights, which significantly assist in strategic decision-making. Programs that integrated advanced Artificial Intelligence and data analytics tools showed improved capability in identifying trends, directly impacting their effectiveness and adaptability. The study concludes that Artificial Intelligence and data analytics enhance program performance evaluations. By providing dynamic, real-time insights and risk assessments, the utilization of Artificial Intelligence and data analytics significantly improves decision-making processes and aids in strategic planning. It emphasizes the importance of robust data quality and governance practices that ensure the accuracy and reliability of evaluations.

This article explores the impact of artificial intelligence (AI) on the aviation and hospitality industries, both of which are rapidly evolving due to technological advancements. It aims to understand the increasing importance of artificial intelligence by examining the various ways in which it is used in these sectors through qualitative research. The research included an analysis of online sources such as airport and hotel websites, booking platforms, and social media accounts of travel-related businesses. This comprehensive data collection provides insight into the various applications of artificial intelligence in tourism. Thematic analysis was then used to categorise the data according to similar uses, providing a detailed understanding of the role of AI in these areas. It compares and examines artificial intelligence applications adopted by aviation and hospitality organisations, evaluating their effectiveness and differences. The study reveals the various ways in which AI is being integrated into these industries and highlights its significant contributions across various dimensions. It also highlights how AI can deliver competitive advantage, improve customer experiences, and introduce innovative paradigms to the aviation and hospitality industries. One important aspect of the research is its ability to provide a deeper understanding of emerging AI trends in these sectors and lay a strong foundation for future research. Ultimately, this study provides valuable insight to stakeholders in aviation and hospitality, equipping them with an informed perspective on leveraging AI for growth and long-term sustainability in their respective industries