Artificial Intelligence in Qualitative Research: Opportunities, Challenges, and Solutions

Artificial intelligence (AI) is increasingly being integrated into qualitative research. While AI provides tools that claim to promise greater efficiency and productivity, it also raises vital concerns about methodological integrity, ethical conduct, and the maintenance of contextual depth. This paper explores the implications of AI for qualitative research. It seeks to address the ethical, methodological, and epistemological concerns associated with AI integration. It also aims to promote critical engagement with AI while upholding the foundational values of context-rich qualitative research. The article initially addresses the growing publicity that AI has received in studies and public discourse, focusing on inflated hopes and misconceptions. The central debate concerns the impacts of AI on qualitative research, such as the way it could improve analysis and transcription efficiency. However, the use of AI is not without issues related to ethics, bias, loss of interpretive depth, and over-reliance on automation. The article argues in favour of responsible, critical, and ethically aware use of AI in qualitative research. AI can be a valuable tool to support, but not replace, qualitative researchers. Its use must be governed by reflexivity, ethical sensitivity, and contextual knowledge to uphold the foundational values of qualitative inquiry.

Qualitative health research has been shaken by the rapid uptake of artificial intelligence (AI), especially large language models. Drawing on Kübler-Ross’s five-stage grief heuristic, we articulate a provocative, yet constructive, map of the field’s current tensions (denial, anger, negotiation, depression, acceptance) around AI’s pros and cons. We argue that what is at stake is not simply efficiency but our very identity of qualitative researchers: reflexivity, intersubjectivity, temporality, and the role of researcher subjectivity. We propose concrete practices compatible with qualitative research’s epistemic and ethical commitments: collective prompt-writing, “coding retreats” for critical oversight of outputs, explicit disclosures, and transparency about what is (and is not) delegated to machines. Rather than reject or romanticize AI, we advocate a rigorous, ethically grounded co-working with it, that safeguards slowness, presence, and dialogical sense-making. Our contribution is to reframe AI as a catalyst for renewed reflexivity and methodological clarity, while warning against the erosion of embodied and collective thinking when research becomes “alone-with-AI.” We conclude with actionable recommendations for reviewers, editors, and researchers to evaluate AI-assisted manuscripts without abandoning qualitative health research’s core: careful attention to meaning, situated ethics, and intersubjective critique.

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.

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.

The integration of artificial intelligence (AI) into qualitative research is transforming the landscape of social inquiry, raising significant epistemological and methodological, questions. This study explores the dual potential of AI to enhance the scalability in qualitative research while challenging its interpretive depth. It situates this tension within the historical trajectory of qualitative research -and specifically Grounded Theory- from positivist to constructivist paradigms, highlighting how AI’s automated, data-driven approaches may signal a resurgence of positivist assumptions. Key research questions guide this exploration: To what extent do qualitative researchers harness AI’s efficiencies in data analysis? Can the extended use of AI in qualitative research impact the depth and reflexivity essential to interpretive analysis? To delve into these questions the study employs a Technology Acceptance Model (TAM) survey combined with semi-structured interviews, strategically targeting European researchers to explore AI’s perceived usefulness, ease of use, and implications for qualitative methodologies. Survey and interview findings reveal a generational divide: early-career researchers embrace AI’s capacity for large-scale data analysis and thematic identification, while experienced researchers express scepticism about its impact on qualitative reflexivity and contextual richness. This generational gap implies that the receptiveness of younger researchers could lead to a gradual return to a methodological positivism. While this study brings a generational divide under the spotlight, future directions call for deeper investigations into the structural inequalities shaping AI adoption, such as access to resources, geography and gender.

Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinical tasks, but concerns regarding biases, lack of regulation, and potential technical issues pose significant challenges. The lack of a clear definition of AI, combined with limited focus on qualitative research exploring clinicians' perspectives, has limited the understanding of perspectives on AI in primary health care settings. This review aims to synthesize current qualitative research on the perspectives of clinicians on AI in primary care settings. A systematic search was conducted in MEDLINE (PubMed), Scopus, Web of Science, and CINAHL (EBSCOhost) databases for publications from inception to February 5, 2024. The search strategy was designed using the Sample, Phenomenon of Interest, Design, Evaluation, and Research type (SPIDER) framework. Studies were eligible if they were published in English, peer-reviewed, and provided qualitative analyses of clinician perspectives on AI in primary health care. Studies were excluded if they were gray literature, used questionnaires, surveys, or similar methods for data collection, or if the perspectives of clinicians were not distinguishable from those of nonclinicians. A qualitative systematic review and thematic synthesis were performed. The Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach was used to assess confidence in the findings. The CASP (Critical Appraisal Skills Program) checklist for qualitative research was used for risk-of-bias and quality appraisal. A total of 1492 records were identified, of which 13 studies from 6 countries were included, representing qualitative data from 238 primary care physicians, nurses, physiotherapists, and other health care professionals providing direct patient care. Eight descriptive themes were identified and synthesized into 3 analytical themes using thematic synthesis: (1) the human-machine relationship, describing clinicians' thoughts on AI assistance in administration and clinical work, interactions between clinicians, patients, and AI, and resistance and skepticism toward AI; (2) the technologically enhanced clinic, highlighting the effects of AI on the workplace, fear of errors, and desired features; and (3) the societal impact of AI, reflecting concerns about data privacy, medicolegal liability, and bias. GRADE-CERQual assessment rated confidence as high in 15 findings, moderate in 5 findings, and low in 1 finding. Clinicians view AI as a technology that can both enhance and complicate primary health care. While AI can provide substantial support, its integration into health care requires careful consideration of ethical implications, technical reliability, and the maintenance of human oversight. Interpretation is constrained by heterogeneity in qualitative methods and the diversity of AI technologies examined across studies. More in-depth qualitative research on the effects of AI on clinicians' careers and autonomy could prove helpful for the future development of AI systems.

Editorial: Opposites Attract at CORR®-Machine Learning and Qualitative Research.

Background: This study examines the realm of qualitative research in accounting, highlighting its evolving methodologies and thematic emphasis. There has been increasing interest in qualitative methodologies, such as literature reviews, SLRs, archival research, bibliometric studies, and interviews, to explore the complex nature of accounting practices. Methods: This study identifies the principal themes, trends, and prominent authors in qualitative accounting research through a systematic approach that incorporates bibliometric analysis using VOSviewer and a thorough assessment of Scopus-indexed journals. Findings: The findings highlight significant themes, including sustainability, technology, governance, and the impact of global occurrences, such as the COVID-19 pandemic, on accounting practices. The discussion also encompasses challenges in conducting qualitative research, such as obtaining access and maintaining anonymity. Conclusions: This study enhances comprehension of the field’s conceptual framework and guides future research, especially in areas such as blockchain, artificial intelligence, and sustainability accounting. The findings provide essential insights for researchers seeking to address the complexities inherent in modern accounting. Novelty/Originality of this article: The qualitative research currently available either discusses a specific topic or focuses on a particular journal, but none examines the general approach to qualitative research in accounting. This paper aims to address this gap by exploring how qualitative research has been conducted in top-ranked journals, providing readers with insights into qualitative research relevant to the field of accounting.

The rapid advancement of artificial intelligence (AI) is increasingly shaping research methodologies across disciplines. However, its integration in qualitative research remains controversial due to epistemological, ethical, and human-centered concerns. This study explores the perspectives of 14 expert qualitative researchers from socio-anthropological and healthcare fields working in Italian academic and hospital settings, with a focus on the opportunities, challenges, and future directions of AI use in qualitative inquiry. Through semi-structured interviews and reflexive thematic analysis, four main themes were developed. First, participants expressed ambivalent attitudes-balancing curiosity with technophobia and emphasizing the need for human oversight and contextual interpretation. Second, an anthropological and philosophical dimension was constructed, underscoring the importance of reflexivity, creativity, and researcher identity as essential counterbalances to AI's mechanistic tendencies. Third, researchers acknowledged AI's practical benefits in tasks such as transcription and data management, and they remained skeptical of its ability to perform complex interpretative work. Finally, ethical and sustainability concerns were raised, including algorithmic bias, data privacy, and the environmental impact of AI technologies. The findings reveal persistent epistemological tensions but also highlight emerging opportunities for AI to enhance research efficiency and accessibility, provided that human interpretative agency remains central. Participants stressed the importance of developing robust ethical frameworks, fostering critical reflexivity, and adopting innovative conceptual approaches to responsibly integrate AI into qualitative research and education. This study offers valuable insights for scholars and practitioners navigating the evolving landscape of AI in qualitative inquiry, advocating a balanced approach that leverages AI's potential while safeguarding the human core of qualitative research.

Quality is one of the most debated topics in the history of Qualitative Research Methods (QRM). It establishes the benchmarks, norms and values that a researcher should follow when involved in research. Quality is a critical tool for promoting value, effectiveness and efficiency in research processes. Qualitative research is popular in several disciplines such as local governance studies, sociology, education, gender studies, public management, media studies, human resource management, political science etc. The proponents of Qualitative Research (QR) believe that it has unique characteristics compared to quantitative and mixed research methods. Qualitative Research (QR) focuses on understanding lived experiences through narrative inquiry, field observations, focus study groups, and the use of digital photos. The literature on quality advocates for methodological rigor in QR. The discourse of quality in qualitative research is evolving with time and the evolving trends in technology and AI. This review explores the key characteristics of QR and the evolving trends. It provides a meta-summary of the quality benchmarks identified across the various domains of qualitative research literature. It evaluates the advantages and limitations of using Artificial Intelligence in QR. Findings from the literature revealed that there is scholarly attention on quality components: credibility, transferability, dependability, confirmability, and authenticity. The characteristics of qualitative research call for different quality standards such as trustworthiness, reflexivity, contextual sensitivity, and rigour. Overall, the findings indicate that embracing Artificial intelligence (AI) in Qualitative Research presents opportunities and threats. AI’s ability to manage large-scale and multimodal data has enhanced the collection of qualitative data. AI can produce brief summaries or spot reoccurring patterns by offering real-time insights. The authors of this paper recommend that future studies should evaluate how quality standards in QR are interpreted and implemented across different academic disciplines, cultures and contexts.

Artificial intelligence (AI) has an increasing presence in scholarship, posing new challenges and opportunities for qualitative researchers. Generative AI, such as Chat-GPT, can supposedly produce humanlike responses, which has implications for online qualitative research, which relies on human participation. In this paper, we contribute to debates about AI as a research participant (‘AI-as-participant’) and the threat of imposter participation in qualitative research. We share our unexpected encounter with AI during our story completion study on mobile dating during the COVID-19 pandemic and discuss how we identified AI responses within our dataset. Central to our analysis was our theoretical grounding of feminist new materialism, which attuned us to the affective and discursive qualities of our participant data. Using our theoretical lens, in tandem with other strategies, we examined the affective forces that signalled stark differences between previous, human-generated data and that of the current study. Analysing the discursive construction of narratives further alerted us to the absence of humans within our data. We conclude that AI cannot sufficiently replicate affect or capture the richness of human experience that is central to qualitative research, and offer recommendations for future researchers to anticipate and check for AI as an unwelcome research participant.

Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice.

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.

Qualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT—GPT-3.5 or GPT-4, Google Bard—now renamed as a Gemini, and Bing Chat—now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. I explore the controversy surrounding the proper acknowledgment of GenAI or AI use in qualitative studies and how GenAI can support or challenge qualitative studies. First, I discuss what qualitative researchers need to know about GenAI in their research. Second, I examine how GenAI can be a valuable tool in qualitative research as a co-author, a conversational platform, and a research assistant for enhancing and hindering qualitative studies. Third, I address the ethical issues of using GenAI in qualitative studies. Fourth, I share my perspectives on the future of GenAI in qualitative research. I would like to recognize and record the utilization of GenAI and/or AI alongside my cognitive and evaluative abilities in constructing this critical appraisal. I offer ethical guidance on when and how to appropriately recognize the use of GenAI in qualitative studies. Finally, I offer some remarks on the implications of using GenAI in qualitative studies

This study examines the potential integration of artificial intelligence (AI) tools, particularly ChatGPT, in qualitative data analysis educational research. The research tries to clarify AI-supported data analysis processes in qualitative data from teacher interviews that examine deficiencies in a "Fundamentals of Programming" course in vocational high schools. The methodology employs a hybrid (both heuristic and literature based) prompt engineering strategy, utilizing open, axial, and selective coding, to ensure a comprehensive analysis. The aim was to compare human and AI-supported analysis to evaluate the depth, efficiency, and reliability of AI tools in qualitative research. This study chose a comparative case study design because examining programming instruction with two different data analysis methods (human researcher and AI-supported) requires a comparative perspective. The findings indicate that hybrid prompt design for AI can significantly enhance the efficiency and accuracy of qualitative data analysis, providing deeper insights and more structured outputs. However, the study also highlights the importance of carefully designed prompts and human oversight to mitigate potential biases and errors inherent in AI-supported analysis. This research contributes to the growing field of AI in data analysis, offering a framework for future studies to leverage AI technologies for qualitative data analysis, thereby enhancing research quality and productivity.