Artificial Intelligence in Qualitative Research: Beyond Outsourcing Data Analysis to the Machine

Qualitative and Mixed Methods Research in Cross-Cultural Psychology

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

This article reviews the current and emerging status of qualitative research in psychology. The particular value of diverse philosophical paradigms and varied inquiry approaches to the advancement of psychology generally, and multicultural psychology specifically, is emphasized. Three specific qualitative inquiry approaches anchored in diverse philosophical research paradigms are highlighted: consensual qualitative research, grounded theory, and participatory action research. The article concludes by highlighting important ethical considerations in multicultural qualitative research.

The integration of artificial intelligence (AI) in rural healthcare systems holds great promise for improving access to quality medical care and addressing healthcare disparities. However, numerous barriers must be overcome to successfully implement AI technologies in these settings. This paper explores the key challenges and potential solutions for implementing AI in rural healthcare systems, focusing on infrastructure, data privacy, internet connectivity, and workforce readiness. One of the primary barriers is the lack of infrastructure in rural areas. Many rural healthcare facilities are under-resourced, lacking the necessary hardware, software, and technical support to adopt AI solutions. Addressing this issue requires significant investment in healthcare infrastructure, including upgrading existing facilities and ensuring they are equipped with modern technology. Data privacy and security present another significant challenge. AI systems rely on vast amounts of patient data to function effectively, raising concerns about data confidentiality and protection. Implementing robust cybersecurity measures and developing clear data governance policies are essential to safeguard patient information and build trust in AI technologies. Internet connectivity is a crucial enabler for AI-driven healthcare, yet rural areas often suffer from unreliable or limited internet access. Solutions to this challenge include investing in broadband infrastructure, exploring alternative connectivity options such as satellite internet, and leveraging offline AI capabilities that can operate with minimal connectivity. Workforce readiness and acceptance of AI technology are also critical for successful implementation. Many rural healthcare providers may lack the training and expertise needed to utilize AI tools effectively. Comprehensive training programs, ongoing education, and support from technology providers can help bridge this knowledge gap and encourage the adoption of AI. In conclusion, while the integration of AI in rural healthcare systems faces significant barriers, targeted solutions can address these challenges. Investment in infrastructure, robust data privacy measures, improved internet connectivity, and comprehensive training programs are essential steps to enable the successful implementation of AI in rural healthcare. By overcoming these barriers, AI has the potential to transform rural healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Collaborative efforts among government entities, healthcare providers, and technology companies will be crucial in realizing this potential and ensuring equitable access to advanced healthcare technologies in rural areas.

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

This study delves into the ethical implications of Artificial Intelligence (AI) in financial decision-making, exploring the transformative impact of AI technologies on the financial services sector. Through a comprehensive literature review, the research highlights the dual nature of AI's integration into finance, showcasing both its potential to enhance operational efficiency and decision accuracy and the ethical challenges it introduces. These challenges include concerns over data privacy, algorithmic bias, and the potential for systemic risks, underscoring the need for robust ethical frameworks and regulatory standards. The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles. Future directions for research are identified, focusing on the development of adaptive ethical guidelines, methodologies for embedding ethical principles into AI systems, and the investigation of AI's long-term impact on market dynamics and consumer behaviour. This research contributes valuable insights into the ethical integration of AI in finance, offering recommendations for ensuring that AI technologies are utilized in a manner that is both ethically sound and conducive to the advancement of the financial services industry. Keywords: Artificial Intelligence, Financial Decision-Making, Ethical Implications, Algorithmic Bias, Data Privacy, Regulatory Standards, Multidisciplinary Approach.

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

Substantial research has been performed to explore the effective integration of artificial intelligence (AI) into healthcare. Consequently, it becomes imperative to grasp the perspectives and concerns of general public in relation to the utilization of their health data in AI research, particularly within the context of privacy issues. This study aims to explore the awareness of participants about the AI, their privacy concerns about data sharing using AI and its use on healthcare data. We carried out a comprehensive study by employing a self-administered questionnaire tool with participants using convenience sampling methodology. A total of 450 participants were enlisted from Saudi Arabia. Conditional binary logistic regression models were employed to compute odds ratios (ORs) and 95% confidence intervals. Among the participants, 168 (37.3%) showed that they have knowledge about AI. In terms of personal data’s vulnerability when using AI technology, 186(41.3%) perceived a privacy risk about their health data. 201(44.7%) indicated their trust in AI’s ability to safeguard data privacy. Regarding the use of machine learning for medical record analysis, 180(40.0%) declared it riskier than benefits. For AI research purposes, 205 (45.6%) supported data sharing and 213(47.3%) believed hospitals should have strict regulations while 214 (47.6%) believed hospitals should provide limited access to data to ensure health data privacy. Furthermore, the study found that younger individuals were more likely to trust AI with their data privacy (OR = 0.540, 95% CI; 0.300-0.972), while participants with higher education levels were nearly three times more likely to trust AI with their data privacy compared to those with lower education (OR = 2.894, p = 0.047, 95% CI; 1.012-8.278). Patients’ viewpoints, the extent of assistance they receive, and their comprehension of health data research and artificial intelligence exhibited significant variations, often influenced by privacy issues. To ensure the acceptability of AI research and its seamless integration into clinical practice in the future, it is imperative to engage the public more extensively and stimulate discussions, particularly concerning privacy concerns.

The aim of this article is to explore the future of qualitative research as seen from a students' perspective. This exploration will initially be incited through a discussion of the use of the term 'qualitative research', and the risks associated with the use of such an umbrella term. It is discussed that the use of an overarching umbrella term can lead to an overhomogenized understanding of qualitative research, that fails to represent the diversity and variety of methodological and epistemological approaches that exist within this research paradigm. It is also discussed that this overhomogenization reinforces the idea of qualitative research as an anti-doctrine to quantitative research, which is argued to discourage interparadigmatic integration. Lastly, it is considered how these (mis)conceptions of qualitative research influence how psychology students are taught about research methodology and how this education could affect these (mis)conceptions. We advocate that the future for qualitative research in psychology should be ensured through a restructure and a refocus on an educational level. This change should overall be centered around teaching students how to be reflective research practitioners based on an in-depth understanding of the variety of epistemologies within both meta-research-paradigms.

This systematic literature review (SLR) study explores artificial intelligence (AI) as a tool to address academics challenges in South African higher education. Numerous difficulties beset higher education system in South Africa, such as a lack of funding, high student-to-lecturer ratios, and unequal access to high-quality education. Relevant academic databases, such as PubMed, Google Scholar, ERIC, and Web of Science, was searched using a combination of keywords relating to South African higher education, AI in education, academic issues, and AI technologies. A total of twenty (20) documents were identified and used in this study. The examination looks at how AI is currently being used, how it affects how people teach and learn, and the opportunities and challenges that come with using it in this setting. The results show that artificial intelligence (AI) technologies, including automated assessment tools, intelligent tutoring systems, and adaptive learning platforms, have the potential to improve personalized learning experiences, boost student engagement and academic performance, and facilitate effective management of educational resources. However, overcoming several issues, such as a lack of technological know-how, restricted infrastructure, concerns about equity, and ethical implications is necessary for the successful integration of AI in South African higher education. It encourages educators, politicians, and technology experts to work together to establish long-term solutions for incorporating AI into teaching and learning processes while maintaining inclusivity, equity and ethical issues.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Contemporary digital ecosystems present a paradigmatic shift in how extremist organisations operationalise artificial intelligence (AI) technologies to facilitate radicalisation, recruitment, and propaganda dissemination. While AI has catalysed transformative advancement across multiple sectors, its dual-use characteristics render it susceptible to weaponisation by violent non-state actors. Extremist groups systematically exploit AI-driven recommendation algorithms, behavioural profiling mechanisms, and generative content systems to identify and target psychologically vulnerable populations, thereby circumventing traditional counterterrorism methodologies. This study examines the multifaceted mechanisms through which AI technologies encompassing machine learning, natural language processing (NLP), facial recognition, and synthetic media generation facilitate the radicalisation trajectory outlined in contemporary theoretical models of violent extremism. This qualitative exploratory research employs secondary data collection methodologies integrating academic literature, cybersecurity reports, government publications, and open-source intelligence (OSINT) from publicly accessible digital platforms. The analytical framework synthesises content analysis and network analysis to systematically identify extremist rhetorical strategies and information dissemination pathways. Psychological theories of radicalisation, coupled with algorithmic media theory and social network analysis, inform the examination of how AI technologies exploit cognitive vulnerabilities and amplify ideological messaging across transnational digital networks. Temporal scope encompasses the period from the early 2010s to the present, with particular emphasis on developments within the preceding five years. The investigation reveals that AI-enabled technologies facilitate radicalisation through algorithmic amplification of emotionally provocative content, behavioural analytics enabling precision targeting of at-risk individuals, and generative systems producing synthetic media (deepfakes, AI-crafted audio) that circumvent content moderation mechanisms. Case studies of ISIS’s algorithmic recruitment strategies, Al-Qaeda’s generative AI-powered propaganda, Taliban’s encrypted-platform messaging, and far-right extremist memetic warfare demonstrate systematic weaponisation of AI technologies. Evidence from geopolitically significant regions, particularly Kashmir, Afghanistan, Syria, and Western democracies, illustrates how extremist organisations leverage AI to create personalised recruitment campaigns, establish echo chambers, and manufacture synthetic narrative content while maintaining operational security. The analysis identifies critical gaps between conventional counterterrorism approaches and the velocity of AI-driven radicalisation. The accelerating sophistication of AI-facilitated extremism necessitates comprehensive international cooperation frameworks, ethics-oriented regulatory architecture, and AI-powered countermeasures. Current legal structures remain fundamentally inadequate to address borderless, rapidly evolving threats. The paper advocates for integrated multilayered responses, including AI-enabled early detection systems, transparent algorithmic governance, international intelligence-sharing mechanisms, and digital literacy initiatives. Critical ethical considerations, surveillance implications, privacy protections, freedom of expression, and algorithmic bias must be balanced against security imperatives. Successful mitigation requires coordinated efforts among governments, technology platforms, international organisations, and civil society to develop ethically designed AI tools for counter-extremism, while establishing robust accountability mechanisms that safeguard human rights in the digital age.

This research study discusses the integration of artificial intelligence (AI) in Early Childhood Education (PAUD) units to improve play and game activities for children as well as innovation in implementing AI-based learning. AI technology has very good prospects and potential in changing teaching and learning activities in educational units with its ability to think and learn like humans, understand human feelings, find solutions in complex decision making, and so on. However, this is sometimes not in line with the application of AI in early childhood education units which are still in their early stages, especially in developing countries like Indonesia. The method used in this research is qualitative research, the data collection technique is from various journal articles about the benefits and impact of AI on early childhood play and play activities. With the support of AI, PAUD units can improve the results of their teaching and learning activities by facilitating more diverse play experiences, increasing operational efficiency, monitoring children's play and play results in real time. Although progress in the application of AI has been seen, there appear to be challenges and shortcomings, including limited AI research in fields outside of STEAM (Science, Technology, Engineering, Arts, and Mathematics), the lack of AI theories and models in play and gaming activities, and resistance in adopting AI in a child's play activities. It is important for PAUD units to integrate AI more optimally in the process of playing and playing activities for children, as well as providing training to educators, education staff and PAUD students to optimize the education system in the future

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.