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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 GenAIgenerated 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.
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 GenAIgenerated 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.
Keywords: qualitative data analysis, GenAI, research methods, ethical issues, critical appraisal
Introduction
OpenAI introduced ChatGPT in November 2022, a conversational generative artificial intelligence (GenAI) system that offers unrestricted access and advanced language processing capabilities. ChatGPT incorporates natural language processing (NLP) technologies and has generated extensive discussions within academic communities. The launch of ChatGPT has led to an increasing use of GenAI in academia in general and qualitative research. This development creates opportunities and challenges for researchers and university teaching faculties (Hasija & Esper, 2022). Indeed, qualitative research endeavors to delve into and comprehend the meanings and experiences of people in their natural settings. Thus, qualitative research is usually done in natural and changeable places because these are the contexts where people live, work, interact, and express themselves. Qualitative researchers want to capture the richness and complexity of human phenomena by observing and interviewing people in their everyday situations (Denzin, 2006). So, researchers look at different types of information, like texts, pictures, interview responses, and observations. For instance, at the Kathmandu University School of Education (KUSOED), Nepal, many undergraduate, graduate, and postgraduate students have been using different versions of ChatGPT and other forms of GenAI to complete different forms of writing tasks such as assignments, research notes, research articles, and research proposals (to name but a few) without knowing the future consequences. However, as a university lecturer, it is difficult for me to identify what level of GenAI the students use for their submissions when they use GenAI tools like ChatGPT, Google Bard, and Bing Chat. Back then, I might not have been adept at utilizing GenAI tools like ChatGPT, Google Bard, and Bing Chat effectively due to the lack of the requisite skills or knowledge or being aware of the future consequences. Next, when it came to accessing the students' submissions, it was difficult for me to identify which students had used GenAI to accomplish such writing tasks.
Considering the recent uses of GenAI or AI, I am writing this commentary to rethink using GenAI tools such as ChatGPT, Google Bard, and Bing Chat in qualitative research as a critical appraisal as a researcher and practitioner. The adaptability of GenAI tools to various research environments and cultural nuances is impressive. However, Parker et al. (2023) evaluated the importance of ethical considerations and human supervision as vital elements in their responsible deployment. As the potential and challenges of human-AI collaboration in qualitative analysis, it involves inductive reasoning and interpretation of rich and contextual data and offers design implications for GenAI assistance that respects serendipity, human agency, and ambiguity in qualitative analysis (Jiang et al., 2021 ; Michel-Villarreal et al., 2023).
Several weeks following the launch of ChatGPT and other GenAI tools, various academic journals, educational institutions, and universities have expressed apprehensions regarding issues pertaining to research authorship and super plagiarism (Al Naqbi et al., 2024; Chiu, 2024). Furthermore, GenAI tools like ChatGPT, Google Bard, and Bing Chat are unable to monitor observable behavior during the learning and research process, authentic tasks, and multiple assessments simultaneously. There was indeed debate among academic journals, educational institutions, and universities to legitimate the research process as efforts (Chiu, 2023). These efforts are being made to resolve concerns and ethical considerations surrounding the use of GenAI or AI for academic endeavors. In this regard, Huang (2021) emphasized that qualitative research and GenAI technologies coexist, mirroring the relationship between strong and weak GenAI. However, the core of qualitative studies revolves around human intelligence and performance.
I wrote this commentary using ChatGPT, Google Bard, and Bing Chat. ChatGPT assisted me in brainstorming and organizing the content, while Google Bard was instrumental in extracting key themes from scholarly articles. I used Bing Chat to polish the language and ensure a smooth and cohesive flow across sentences and paragraphs. I would like to acknowledge the combined use of GenAI and/or AI and my cognitive and evaluative skills in crafting this commentary. However, it is important to note that GenAI did not dictate the commentary, and the work is fundamentally rooted in my cognitive and evaluative capabilities (Dahal et al., 2023). So, I offer a critical evaluation of the application of GenAI and AI in qualitative studies. GenAI-assisted qualitative researchers as co-authors, conversational partners, and research assistants have become increasingly prevalent in academic settings. Nevertheless, scholars worldwide must adhere to the ethical principles governing the use of GenAI and/or AI tools in qualitative research traditions.
GenAI: What you Need to Know?
Using input prompts, generating artificial intelligence (GenAI) can produce new content, including text, images, videos, or audio clips. It uses trained data to learn and generate results and patterns that share similar traits. As GenAI tools are based on machine learning algorithms and natural language processing (Aattouri et al., 2023), machine learning algorithms are used in quantitative GenAI to process and analyze large amounts of numerical data. In contrast, natural language processing is used in qualitative GenAI to analyze and understand text-based data such as reviews and feedback.
Programmers and software developers have created artificial intelligence (AI) technologies that incorporate natural language processing (NLP), and researchers can now use Al-powered tools to help them analyze qualitative data. GenAI-based "NLP uses linguistics and machine learning" (Anis & French, 2023, p. 1140) models to understand, interpret, and create language in the human style. Some GenAI and AI tools for qualitative studies in general that are being used on the web are Scite Assistant, Consensus, Elicit, ChatGPT, ChatPDF, Research Rabbit, SciSpace, Perplexity, Google Bard, and Bing Chat. These platforms and models offered researchers ample opportunities to communicate with GenAI by using ChatGPT from OpenAI, a specific NLP application in a chatbot format. However, there were AI tools that had been in practice before the launch of ChatGPT. For instance, with the most cutting-edge AI and machine learning algorithms, Atlas.ti is widely considered the most efficient tool for qualitative text analysis and market research because it automatically generates deep insights for quicker outcomes. Likewise, in a recent development, GenAI can perform challenging linguistic tasks like text generation, language translation, and even question-and-answer sessions as a co-author in conversational platforms and research assistants (Fui-Hoon Nah et al., 2023; Salinas-Navarro et al., 2024).
GenAI does, however, have some limitations. "Due to the training data, it uses and its limited capacity to interpret tacit knowledge" (Anis & French, 2023, p. 1140) in humanistic approaches with a critical perspective, GenAI frequently produces biased results and factual errors based on the caliber of the training data. It also doesn't have a solid understanding of the subtleties of human language-feelings, emotions, behavioral behaviors, and authentic tasks of the physical and social world. Shimizu et al. (2023) noted that GenAI positively impacts teaching and learning efficiency and access to information while negatively affecting independent thinking and the adaptability of current assessment methods. They suggested integrating GenAI literacy, ethics, and compliance into research and curriculum, enhancing learning efficiency, aiding information collection and distribution, promoting students' participation in learning processes by nurturing advanced cognitive learning domains and incorporating more communication exercises. However, while using "GenAI tools such as ChatGPT, Google Bard, and Bing Chat" (Dahal, 2023a, p. 249), among others, it is essential to note that certain aspects like observable behaviors during research, genuine tasks, and simultaneous multiple assessments may not be trackable.
Contrary to these significant downsides, GenAI benefits qualitative researchers in several ways. Christou (2023a) critically examines the ethical and methodological implications of using GenAI and/or AI tools in qualitative research by arguing that it can serve as an asset and collaborator for qualitative researchers; however, it also presents certain challenges and risks that need to be recognized and addressed. So, the discussion should be grounded in the potential advantages and drawbacks of GenAI, along with the ethical norms and guidelines that should guide its application in qualitative research. Consequently, researchers face a dilemma in reporting and justifying the use of GenAI or AI in their studies. This must include disclosing the GenAI's or Al's types, roles, and functions, explaining the reasoning and criteria for its selection and evaluation, and acknowledging its limitations and uncertainties.
GenAI in Qualitative Research
"GenAI tools, such as ChatGPT, Google Bard, and Bing Chat" (Dahal, 2023a, p. 249), among other similar platforms, can mitigate the limitations arising from the small data size in qualitative research. Nevertheless, limited data samples cannot frequently provide comprehensive insights into diverse experiences and perspectives across different temporal and spatial contexts (Dahal, 2023b). The advancement of technology has led to enhanced efficiency in the collection of extensive data, enabling faster and more comprehensive data gathering. In favor of GenAI, Anis and French (2023) advocated for the use of GenAI and/or AI tools in qualitative research, emphasizing that researchers should be cognizant of its limitations and challenges and noted that GenAI could assist in tasks such as text generation, language translation, and question-answering, thereby making qualitative research more efficient, explicatory, and equitable. As a result, these processes reduce the time and effort needed for data collection. This engagement for the researcher provides insights and explanations for complex phenomena and addresses issues of bias and representation in qualitative data generation and analysis. Similarly, Longo (2019) explored the impact of GenAI and AI tools on qualitative research methods in education and argued that GenAI or AI has yet to be fully exploited in education, a discipline that aims to design and evaluate approaches for facilitating learning and knowledge acquisition. Also, Longo (2019) identified some challenges and limitations of using GenAI or AI in qualitative research, such as ethical, legal, and social issues.
Despite the challenges and limitations of using GenAI or AI in qualitative research, the analysis of qualitative research has undergone a different level of improvement, as it continues to be a time-consuming and labor-intensive undertaking. Researchers can use GenAI or AI tools to address these limitations and enhance the depth of qualitative studies. By leveraging GenAI tools such as ChatGPT, Google Bard, and Bing Chat, researchers can overcome data scarcity and delve deeper into their research questions. These GenAI tools offer a bridge between limited datasets and comprehensive insights, allowing for more nuanced analyses and a broader understanding of qualitative phenomena. This can be achieved through insightful engagement as a co-author, a conversational platform, and a research assistant for all researchers-novice and veteran while adhering to a humanistic approach and critical perspectives.
GenAI: Insightful Engagement as a Co-Author
In this digital era, educators and researchers are engaging with a substantial portion of mundane text, wherein GenAI can serve as an augmentation of the researcher's capacity to comprehend and discern the underlying significance within data. This allows the researchers and/or users to direct their attention toward the more interpretive elements of their research, including the refinement of the code book, the establishment of conceptual connections, the process of meaning-making, and the development of theoretical frameworks. Consequently, the efficiency of research is enhanced as the researcher's primary focus shifts towards critical thinking, deliberation, and the cultivation of an interpretative repertoire, thereby relieving them from the more time-intensive tasks that GenAI can handle. On the other hand, educators and researchers in the field of academia frequently face a multitude of unorganized qualitative data, posing a substantial obstacle in terms of its organization and analysis. For example, a scholar engaged in charting the evolving landscape of discourse in qualitative research (Fransman, 2018). In this alignment, Ciechanowski et al. (2020) presented a tutorial on how to conduct GenAI or AI research without coding. They used the metaphor of "the art of fighting without fighting" (p. 322) from the movie Enter the Dragon to explain how qualitative researchers can benefit from using Gen AI or AI tools that do not require programming skills. These tools include text mining, sentiment analysis, social network analysis, and natural language generation. The authors also discussed the ethical and methodological challenges of using GenAI and/or AI tools, such as data quality, validity, reliability, and transparency. In addition, common techniques such as content analysis and sentiment analysis can capture certain aspects of information. However, their effectiveness is often constrained to measuring the frequency of phrases or assessing emotional tone, respectively. Next, Hasija and Esper (2022) concluded that GenAI technology acceptance is a complex and dynamic process that requires a holistic approach to address the challenges and opportunities of AI in supply chain management (SCM) and suggest that SCM professionals should adopt a trust-based mindset towards AI and leverage their organizational resources to enhance their AI capabilities and performance.
With all of the above, GenAI can comprehend textual content and identify patterns according to the researcher's interpretive framework. For instance, GenAI can emphasize textual content pertaining to political, social, and cultural matters. For instance, Parker et al. (2023) conducted a study on the role of ChatGPT in assisting researchers with creating and refining interview protocols. They discovered that ChatGPT has the capability to generate suitable interview questions, formulate key inquiries, provide feedback on protocols, and simulate interviews. This demonstrates its potential to save time and effort, especially when human resources are scarce. Therefore, this engagement leverages insights and suggestions to enhance the development and refinement of interview protocols, thereby increasing the likelihood of researchers achieving their research objectives.
Conversational Platform of GenAI
A conversational GenAI platform is an artificial intelligence (AI) system designed to interact with humans in their natural languages. These interactions commonly occur in dialog systems, serving diverse practical purposes. They find applications in customer service, request routing, research, and information gathering. Some of these systems employ intricate word classification techniques and natural language processors, while others identify general keywords and generate responses using common phrases stored in an associated library or database. These platforms aim to create an engaging and natural experience for researchers, to help answer their questions or guide them to the right information. Likewise, GenAI tools such as ChatGPT, Google Bard, and Bing Chat have the capability of conversational flow (Dahal, 2023a). This conversational flow is based on the input commands or prompts the users or researchers give. However, the limitations of GenAI are evident in its need for more capacity to attain a level of comprehension of the social realm equivalent to that of humans. Several limitations can be identified in the system, including a deficiency in employing common sense, an incapacity to acquire knowledge from past encounters, and a need for more contextual comprehension regarding social and cultural intricacies.
Nevertheless, the strategic use of these constraints can amplify the analysis's explanatory capacity as a discursive framework. For example, GenAI frequently encounters difficulties in comprehending intricate human-generated text that encompasses multiple levels of meaning and requires understanding metaphors. GenAI can accurately interpret explicit text and code based on predetermined coding guidelines established by researchers. Instead, regarding intricate scenarios, GenAI can effectively employ conversational techniques to filter out complex cases intelligently. The instances that deviate from the coding scheme as a result of ambiguity and intricate language are valuable cases that can be identified by GenAI for clarification while generating the text.
Research Assistant for Researchers
GenAI has the potential to greatly benefit as research assistants for researcher-novice and/or veteran from diverse backgrounds who possess limited social and cultural capital (Anis & French, 2023). Social and cultural capital are the resources that are available to an individual through their social interactions and cultural knowledge, which can influence their social mobility and status in society. GenAI is proving advantageous to researchers of all experience levels and backgrounds while also tackling the challenges of limited social and cultural capital that some researchers encounter. On the contrary, GenAI may present challenges for underprivileged groups due to differential access to resources within an academic context. The use of GenAI can assist researchers in addressing the constraints associated with language proficiency, academic conventions, and the various forms and styles employed in scholarly discourse (Dahal et al., 2023). For example, GenAI can serve as a valuable research assistant for researchers by aiding in tasks such as information retrieval, hypothesis generation, experiment design, data analysis, and qualitative report writing (Parker et al., 2023). However, ChatGPT and other GenAI or AI tools cannot produce high-quality scientific abstracts that can replace human writing. Gao et al. (2022) conducted the study three methods to evaluate the ChatGPT abstracts: an artificial intelligence output detector (AIOD), a plagiarism detector (PD), and blinded human reviewers (BHR) and concluded that ChatGPT abstracts were more likely to be detected as AI-generated than human-written ones by AIOD, had lower similarity scores with the original abstracts by PD, and were rated lower in terms of clarity, accuracy, and relevance by BHR.
Nevertheless, GenAI and AI technologies can enhance researchers' capabilities, enabling them to become more resourceful and self-reliant in their roles. These forms of independence can empower researchers to conduct significant and analytical research according to their preferences. Furthermore, Gen AI has the potential to enhance scholarly discourse by incorporating diverse perspectives and augmenting their impact within the realm of mainstream academia.
Ethics of GenAI in Qualitative Research
GenAI can enhance the depth and value of qualitative research by serving as a coauthor, a conversational platform for users-researchers, and educators, and a research assistant for researchers. However, it is crucial to consider the ethical implications associated with using GenAI or AI tools in the digital era. In general, the ethics of GenAI in qualitative research is a critical area of consideration. It ensures that GenAI systems are used responsibly and transparently in research processes. This includes respecting the privacy and confidentiality of data, obtaining informed consent for data use, and avoiding harm to participants.
Furthermore, it is essential to ensure that GenAI tools are unbiased and that their use does not lead to unfair outcomes. Transparency in the use of GenAI and/or AI in research and the limitations of these tools are also critical ethical requirements. Ultimately, the goal is to use GenAI to enhance research without compromising ethical standards. With the above, Vianello et al. (2023) proposed a qualitative approach to evaluate and improve "the trustworthiness of GenAI solutions from the perspectives of end-user explain ability and normative ethics and present a case study of a GenAI recommendation system used in a real business setting" (p. 1408) and show how their approach can identify practical issues and ethical considerations related to the GenAI system. Here, the researcher's explainability refers to the researcher's ability to clearly convey research methods, findings, and limitations to experts and the public and sometimes to understand subjective explanations from research subjects. As a user of GenAI in qualitative studies, it is essential to recognize that GenAI serves as a tool to enhance researchers' capabilities rather than replace them. In this regard, Anis and French (2023) have warned about GenAI's and Al's potential pitfalls, including factual inaccuracies, biased outcomes, an absence of subtlety, and ethical dilemmas. Thus, qualitative researchers should view GenAI as an instrument to aid their work rather than a replacement by advocating for a thoughtful and introspective methodology to assess the rigor and credibility of the qualitative research insights (Dahal, 2023b) produced by GenAI. So, ownership and authorship of research cannot be attributed to GenAI or AI tools.
Moreover, Albalawi and Mustafa (2022) explored the capabilities of AI to offer groundbreaking strategies for disease prevention and control and highlighted the constraints and obstacles that must be overcome for the successful and ethical application of AI. The results produced by AI minimize the ethical impediments to its responsible deployment. Likewise, Marshall et al. (2023) have underscored the ethical considerations of employing GenAI in qualitative research. They contend that while GenAI can augment qualitative research by introducing novel techniques for data gathering, scrutiny, and elucidation, it also presents considerable challenges and hazards. In this regard, Yu and Yu (2023) explored the ethical issues of GenAI in education. They identified principles of GenAI ethics in education, such as "deontology, utilitarianism, virtue, transparency, justice, fairness, equity, non-maleficence, responsibility, and privacy" (p. 9). In opposition, Akabayashi et al. (2022) argued that ChatGPT and similar tools could hijack author contributions by generating indistinguishable human - written text without proper disclosure or attribution. So, when utilizing GenAI or AI tools, the challenges associated with obtaining informed consent, ensuring privacy and confidentiality, establishing trust and rapport, and maintaining research validity and reliability for investigators and participants are significantly reduced.
Meanwhile, GenAI cannot be regarded as an autonomous and unbiased interpreter of the social sphere. Human researchers will always be responsible for the task of interpretation. Elali and Rachid (2023) added that the ethical and practical implications of using GenAI to generate research papers in the scientific community and argue that GenAI-generated papers pose a severe threat to the integrity and quality of scientific research, as they can be used to fabricate or plagiarize data, methods, results, and conclusions. So, Elali and Rachid (2023) added the challenges and limitations of detecting and preventing GenAI-generated papers, such as the lack of standardized criteria, the difficulty of verifying sources, and the possibility of adversarial attacks, and propose some potential solutions and recommendations, such as developing more robust and transparent peer-review processes, enhancing the education and awareness of researchers and editors, and establishing ethical guidelines and policies for using GenAI in scientific writing. Thus, GenAI can be used to automate identifying patterns and trends in data. However, the researcher needs to maintain control by designing the interpretative repertoire. The researcher's values and assumptions ultimately shape the research process and findings. By allowing the researcher to support interpretative control, concerns on ownership, authorship, and the researcher's positionality in the research can be addressed.
Furthermore, GenAI exhibits inherent biases that are present in the data used for training. Hence, the dominant ideas, beliefs, and attitudes prevalent in society are expected to influence the various tasks performed during the research. This is particularly crucial for researchers engaged in critical work that challenges societal norms and aims to change social structures. In conclusion, researchers aware of the ethical considerations surrounding GenAI and AI can effectively use this technology to analyze extensive qualitative data, conduct meaningful research, and empower fellow researchers.
Future of Gen AI in Qualitative Research
Generative artificial intelligence (GenAI) is significantly reshaping the field of research, with qualitative research no exception. In recent trends, GenAI has been used in qualitative research for various purposes, such as automating data collection, coding, analysis, and generating insights. Using GenAI can result in fresh perspectives, a deeper comprehension of human behavior and experience, and cognitive and evaluative skills. In discussing the potential of GenAI or AI as a resource, methodological tool, and analytical instrument in qualitative research, Christou (2023b) suggested the numerous advantages to researchers, including aiding in literature and systematic reviews, creating conceptual frameworks, and performing thematic and content analysis by recognizing the challenges and ethical dilemmas that come with the use of GenAI in research, such as possible bias, unreliability, and plagiarism. Hence, GenAI can be invaluable in qualitative research if researchers consider these factors and adhere to good research practices. As an outcome, GenAI can boost researchers' creativity and innovation and the quality and impact of their research findings. Next, GenAI or AI has the potential to automate various tasks in qualitative research, including data collection, coding, and analysis. This can allow researchers to dedicate their attention to the research process's more creative and strategic aspects. In this regard, Gröger (2021) proposes the "data ecosystem for industrial enterprises, a framework of data producers, data platforms, data consumers, and data roles for AI and data analytics in industrial environments." (p. 108). Furthermore, GenAI can identify intricate patterns and correlations within qualitative data, which would prove challenging or unattainable for humans.
Automating data collection is a valuable tool for gathering qualitative data from various sources, including social media platforms and other online forums. GenAI tools ChatGPT, Google Bard, and Bing Chat can assist researchers in efficiently and effortlessly collecting substantial amounts of data. GenAI or AI tools can help code and analyze qualitative studies and their associated data. These tools can assist researchers in identifying patterns and relationships in the data that would be challenging or impossible for humans to detect (Cingillioglu, 2023). Utilizing GenAI to generate insights is an additional aspect of qualitative data analysis. This can assist researchers in gaining new and innovative insights into human behavior and experience.
While some believe GenAI poses a threat to scientific research, in contrast, the empirical study of Chubb et al. (2022) explored the potential for GenAI to enhance the research process and culture. They interviewed leading scholars from various disciplines and analyzed their views on how GenAI or AI could help or hinder research practice and creativity. I found that their significant contribution offers valuable insights into how Gen AI or AI could be harnessed to improve research outcomes. Likewise, Chubb et al. (2022) argued that there is a need for more meta-research on the role of GenAI in research, as well as anticipatory approaches and critical voices to ensure a responsible and beneficial use of GenAI or AI in research.
In addition, Amann et al. (2023) raise an important point about medical Al's potential benefits and challenges. There is no doubt that AI has the potential to revolutionize healthcare in many ways, from improving the accuracy of diagnoses to streamlining administrative tasks. However, it is also important to carefully consider this technology's ethical, social, and legal implications before it is widely adopted. Feuston and Brubaker (2021) offered design implications for future AI tools used for tasks like data exploration and coding and remarked that there is a lack of automated analytic work. More so, Amann et al. (2023) highlight the complexity of integrating AI into healthcare and the need for a balanced, thoughtful, and inclusive approach. They underscore the importance of technological innovation, human values, social relationships, and ethical considerations in the development and use of medical AI. Akabayashi et al. (2022) offered some ground rules for the use of Gen AI or AI tools in scientific manuscripts, such as declaring the use of GenAI or AI tools in the author contributions statement, providing the source code and parameters of the tool, and ensuring that the GenAI-generated text is consistent with the original data and findings. Hence, for the future use of GenAI, it is recommended that various academic journals, educational institutions, and universities establish policies to prevent the misuse of GenAI or AI tools and uphold the transparency and integrity of scientific publishing.
Without a doubt, the future of GenAI or AI in qualitative research is highly promising. As GenAI technology continues to evolve, it is likely to take on a progressively important role in automating numerous tasks within qualitative research. This will allow researchers to focus more on the creative and strategic elements of the research process, leading to the uncovering of new insights and a more profound comprehension of human behavior and experiences.
Final Remarks
This commentary is offered for researchers-novices and/or veterans to stimulate a critical dialogue among qualitative researchers about the opportunities and challenges of using GenAI and/or AI in their work. Thus, there is a need for increased dialogue and collaboration between qualitative researchers and AI developers to enhance the quality and utility of GenAI and AI in qualitative research. GenAI and AI in qualitative research have become increasingly popular. GenAI can automate text generation, data collection, and analysis, identify patterns and trends in data, generate hypotheses, and offer feedback on research findings. This commentary argues that GenAI can be used in qualitative studies, including qualitative empirical studies, systematic reviews, and conceptual studies (among others). However, several ethical and practical considerations must be considered when using GenAI in qualitative studies. First, qualitative researchers need to be well-versed in the qualitative engagement process. This means understanding the different types of qualitative data, how to collect and analyze qualitative data, and how to interpret the results of qualitative research. Secondly, GenAI or AI tools must be employed ethically and responsibly. This entails utilizing diverse and unbiased training data, implementing transparency and accountability measures, and candidly acknowledging any Ai-generated content's limitations and potential risks. Moreover, researchers' active and cognitive input is crucial for ensuring the accuracy and credibility of results. Researchers achieve this by cross-referencing AI-generated content with other data sources and drawing upon their knowledge of the phenomenon under investigation for interpretation.
In conclusion, this commentary has highlighted essential practical considerations for the ethical, relevant, and defensible utilization of GenAI and AI in scientific qualitative studies. Further research is necessary to fully explore the research outcomes related to the use of GenAI and AI and determine the most effective ways to integrate AI into research planning and execution. While GenAI or AI has significantly impacted the modern world, academia, and the research community, it remains imperative that qualitative studies leveraging GenAI and/or AI adhere to rigorous standards of trustworthiness and ethics. Thus, researchers can actively engage in the research process by applying their cognitive and evaluative skills from conception to the conclusion of the qualitative studies.
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