Using Generative Artificial Intelligence to Facilitate Early STEM Learning: A Case Study in a Chinese Kindergarten

Background of Study: The integration of artificial intelligence (AI)—particularly generative AI (GenAI)—into early childhood education (ECE) is rapidly advancing. While it presents transformative opportunities for learning and teaching, it also raises significant ethical, regulatory, and humanistic concerns. The accelerated development of GenAI tools often outpaces the establishment of appropriate educational safeguards and policy responses. Aims and Scope of Paper: This study investigates the implications of GenAI on fundamental humanistic principles in ECE, specifically focusing on inclusivity, equity, and human agency. It aims to assess how current regulatory and institutional frameworks address the risks and responsibilities associated with GenAI in early learning environments. Methods: A qualitative methodology was employed, incorporating policy analysis and semi-structured interviews with experts in education, ethics, and technology. This approach was used to evaluate both the current state of national regulations and the readiness of educational institutions to manage GenAI integration responsibly. Result: The study found a significant misalignment between the rapid technological evolution of GenAI and the slow pace of regulatory adaptation across most countries. There is a widespread absence of clear, actionable guidelines regarding data privacy, ethical use, and accountability within educational contexts, especially for early learners. Conclusion: Without timely and thoughtful policy interventions, the adoption of GenAI in ECE may inadvertently erode core values that support equitable and inclusive education. The paper recommends that governments and institutions develop comprehensive policy frameworks, implement robust data governance mechanisms, and revise existing AI regulations to better address the unique challenges posed by GenAI in early childhood learning.

The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan & Tsi, 2024), including in providing feedback information to students. While GenAI offers unprecedented accessibility and immediacy, this presentation argues that teachers' expertise remains irreplaceable in productive feedback – i.e., processes in which students make sense of information about their performance and use it to improve the quality of their work or learning strategies (Henderson et al., 2019, p. 1402). Drawing on a large-scale, cross-institutional survey involving 6,960 Australian university students (Henderson et al., 2025), this Pecha Kucha highlights students' perceptions of GenAI versus teacher feedback. The quantitative analysis revealed that nearly half of them (49.7%) reported using GenAI for feedback. However, they rated teacher feedback as more helpful and significantly more trustworthy. While 83.9% found GenAI feedback helpful, only 60.1% considered it trustworthy, compared to 90.5% who trusted teacher feedback. This trust gap may reflect the inconsistent quality identified in GenAI's feedback comments (Venter et al., 2024). The thematic analysis of 5,736 open-ended responses from students who used GenAI for feedback yielded 8,498 coded instances, revealing four interrelated characteristics in which teacher feedback was perceived as outperforming GenAI. Contextualisation and Relevance: Teacher feedback was perceived as more sensitive to specific assignment contexts (95.2% of 669 instances rated GenAI as less contextualised than teacher feedback) and more relevant to learning objectives (84.6% of 123 instances rated GenAI as less relevant). This contextual awareness enables teachers to identify what matters within disciplinary and course-specific frameworks. Reliability and Accuracy: Students perceived teacher feedback as significantly more reliable and trustworthy (95.4% of 1143 instances), reflecting teachers' ability to provide more trustworthy and accurate guidance without the hallucinations and factual inaccuracies that can appear on GenAI outputs. Relational Significance: Teachers offered more personal, connected feedback experiences (93.8% of 471 instances), providing the interpersonal recognition essential for productive learning relationships. This relational dimension cannot be replicated by GenAI’s algorithmic responses. Expertise: Students recognised teachers as more authoritative sources (88.2% of 119 instances), valuing their disciplinary knowledge and pedagogical understanding of student development trajectories. Students' evaluation of feedback is fundamentally shaped by perceptions of source credibility (Bearman et al., 2024), which may explain why students perceive teacher feedback as more trustworthy than GenAI's. Research demonstrates this selective engagement: uptake of content-focused GenAI feedback was considerably lower than form-focused feedback(Ziqi et al., 2024), suggesting students recognise GenAI's limitations for substantive guidance requiring disciplinary expertise. This translates into learning outcomes, with students not only perceiving instructor feedback as more useful but also demonstrating significantly greater lab score improvements than those receiving GenAI feedback (Er et al., 2025). GenAI may create opportunities for educators to focus on what they do best: providing expert, contextualised, and relationally-grounded feedback within authentic learning relationships. This potentially positions teacher expertise as increasingly valuable, with educators prioritising higher-level pedagogical responsibilities, such as developmental guidance, facilitating critical thinking, and disciplinary enculturation, while GenAI supports lower-level feedback processes, like grammar correction and initial draft review. Students appear to already recognise this distinction, trusting teachers for more substantive, transformative feedback while appreciating GenAI's supplementary role for immediate, accessible guidance.

The integration of generative artificial intelligence (GAI) in education has shown the potential to improve learning outcomes, yet its impact on self‐regulated learning (SRL) in second language (L2) writing remains underexplored. This mixed‐methods study investigated the effects of a GAI chatbot tool on task strategy diversity, metacognitive awareness and writing performance among 40 undergraduate students in Eastern China over an 8‐week intervention. Participants were randomly assigned to an experimental group ( n = 20) using the GAI chatbot platform Tongyi.ai or a control group ( n = 20) relying on traditional resources. Data were collected using the Metacognitive Awareness Inventory (MAI), Strategy Inventory for Language Learning (SILL), writing performance assessments, participant interaction logs, reflective journals and semi‐structured interviews. Quantitative analysis revealed that the experimental group showed greater improvements in task strategy diversity, metacognitive awareness and writing performance than the control group. Qualitative analysis further indicated that GAI tools could facilitate task planning, promote adaptive strategy use and deepen metacognitive reflection. Despite these benefits, participants expressed concerns about the potential of over‐reliance on GAI and the accuracy of its generated content. The present study highlights the potential of GAI to enhance SRL in L2 writing by fostering adaptive task strategies and promoting metacognitive development, offering valuable implications for integrating GAI into L2 writing instruction. Practitioner notes What is already known about this topic Generative AI (GAI) tools have shown the potential to enhance various aspects of education, including personalized learning and feedback provision. Self‐regulated learning (SRL) is crucial for students' academic success, particularly in second language writing. Technology has been found to support the development of writing strategies and metacognitive skills. What this paper adds This study provides novel empirical evidence on how GAI tools influence undergraduate students' task strategies and metacognitive awareness in self‐regulated learning, specifically in L2 writing contexts. The research demonstrates that students using GAI tools developed more diverse and adaptive task strategies compared with those using traditional resources. The study reveals that GAI tool usage led to increased metacognitive awareness among students, enhancing their ability to plan, monitor and evaluate their writing processes. Implications for practice and policy Educators should consider integrating GAI tools into L2 writing instruction to support students' development of diverse task strategies and metacognitive skills. When implementing GAI in education, it is crucial to balance technology assistance with fostering students' independent thinking and creativity. Future research should explore the long‐term effects of GAI on self‐regulated learning and investigate its impact across different student populations and educational contexts. Educational institutions should develop guidelines for the ethical use of GAI tools in academic settings, addressing concerns about academic integrity and data privacy.

We investigate the integration of generative artificial intelligence (GAI), such as ChatGPT, into higher education courses and assignments to understand how GAI tools mediate learning and support the development of students’ subject and GAI literacy. By investigating the embedding of GAI into educational contexts, we address both the opportunities and challenges of GAI in higher education teaching. Utilizing technology-mediated learning (TML) theory, our case study explores how incorporating ChatGPT and other GAI tools into courses and assignments can enhance learning outcomes, foster interactive and collaborative learning, support critical thinking, and prepare students for professional use of GAI. We examine the role of GAI tools in facilitating learning and reflect on the implications for teachers and higher education institutions. Our findings demonstrate that GAI tools can mediate learning by bridging subject knowledge gaps, enabling adaptive and scalable support, and fostering GAI literacy through hands-on engagement while underscoring the continued importance of human educators in providing critical, ethical, and contextual guidance.

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

Purpose This study examines the integration of generative and predictive artificial intelligence (AI) models within smart cities, focusing on how user readiness and technology adoption influence their contribution to sustainable urban development and governance. Design/methodology/approach The study applies a systematic literature review following PRISMA guidelines and synthesizes evidence from 50 peer-reviewed studies (2018–2025) indexed in Scopus and Web of Science. It combines bibliometric mapping using VOSviewer with thematic analysis to examine the drivers, barriers and governance mechanisms shaping the adoption of generative, predictive and hybrid applications in urban contexts. Findings Generative AI fosters participatory engagement, citizen co-design and interactive simulations, advancing SDG 11 (Sustainable Cities and Communities) and SDG 4 (Quality Education) through enhanced digital literacy and inclusive planning. Predictive AI improves operational efficiency, forecasting accuracy and data-driven policymaking, supporting SDG 9 (Industry, Innovation and Infrastructure) and SDG 13 (Climate Action) by promoting sustainable resource use and climate-resilient management. Hybrid AI integrates these strengths, addressing both social and operational aspects of smart city development and aligning with SDG 17 (Partnerships for the Goals) through cross-sector collaboration and shared governance. Collectively, these models contribute to broader sustainability goals, including SDGs 3, 7 and 12. Research limitations/implications This review acknowledges several key limitations. Reliance on Scopus and Web of Science may exclude regionally significant or domain-specific studies not indexed in these databases. The focus on English-language publications introduces potential language bias, possibly overlooking relevant research from non-English-speaking regions. Restricting the timeframe to 2018–2025 captures recent developments but may omit earlier foundational work or the most recent studies not yet indexed. Differences in research design, policy contexts and sample characteristics also affect comparability and limit generalizability. Future research should broaden data sources, include multilingual literature and adopt mixed-methods and longitudinal approaches to enhance contextual diversity and empirical robustness. Practical implications The findings provide practical guidance for policymakers, urban planners and technology developers to design AI governance systems that are transparent, accountable and aligned with the SDGs. Integrating generative and predictive AI can enhance operational efficiency, support participatory planning and promote responsible decision-making. The findings inform the development of adaptive policy frameworks that advance SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) through digital literacy initiatives, cross-sector collaboration and data-informed management. Strengthening these practices enables cities to translate AI’s potential into tangible contributions to inclusive and sustainable urban transformation. Social implications Integrating user readiness and digital literacy into AI adoption is essential for building inclusive and trustworthy smart cities. These efforts support SDG 4 (Quality Education), SDG 10 (Reduced Inequalities) and SDG 16 (Peace, Justice and Strong Institutions). Generative AI encourages citizen participation and collaborative planning, while predictive AI improves service accessibility and data-informed governance. Promoting ethical awareness and community engagement helps narrow digital divides and address bias. Collectively, these elements advance SDG 11 (Sustainable Cities and Communities) and SDG 17 (Partnerships for the Goals) by fostering socially responsive and transparent AI-driven urban development. Originality/value This review is among the first to integrate perspectives on user readiness and technology adoption with comparative insights into generative and predictive AI in smart cities. It advances understanding of how AI-driven urban innovation supports inclusivity, efficiency and sustainability, while outlining policy directions and a future research agenda for equitable and transparent AI governance.

The integration of generative artificial intelligence (GenAI) into web-based individual formative e-assessments in higher education is a nascent field that warrants further exploration. This study investigated the use of GenAI within an 8-week undergraduate-level research methods course at a university in the United States of America, aiming to understand how students leverage GenAI tools during individual formative e-assessments questions. The research revealed that a significant majority of students initially preferred traditional study resources over GenAI. However, a gradual shift towards more balanced use of both resources was observed, particularly in formative e-assessments involving statistical analysis and calculation questions. In their interactions with GenAI, students primarily used it for multiple-choice and true/false questions, often by directly copying and pasting the question prompt into the GenAI interface. Students were able to discern and accept accurate responses generated by GenAI and reject those that were incorrect or contradicted their existing knowledge. Students’ reported primary motivations for turning to GenAI were to seek answers to assessment items as well as to corroborate the accuracy of their own responses. This study contributes to the growing body of literature empirically investigating actual usage behaviours with GenAI tools and the motivation behind these behaviours. We discuss the implications and limitations of these findings. Implications for practice or policy: Educators should develop AI literacy programmes and integrate them into pedagogy strategies. Educators and researchers need clear guidelines for ethical AI use in formative e-assessments. Educators should encourage students’ critical thinking and source evaluation on the information that GenAI provides.

The rapid integration of generative artificial intelligence (GenAI) into educational settings has raised new possibilities and challenges for second language acquisition, particularly in the domain of academic writing. While most existing applications emphasize lower-order concerns such as grammar correction, spelling, and vocabulary enhancement, less is known about how GenAI can be harnessed to cultivate higher-order thinking skills in English as a Foreign Language (EFL) writing contexts. This study addresses that gap by examining the extent to which GenAI tools can support analysis, evaluation, and creation with core dimensions of higher-order cognition as outlined in the Revised Bloom’s Taxonomy. Guided by Cognitive Learning Theory and Sociocultural perspectives on mediated learning, the study employed a mixed-methods, quasi-experimental design with 80 intermediate-level EFL university students. Participants were randomly assigned to an experimental group (n = 40), which engaged in GenAI-assisted writing and revision tasks, and a control group (n = 40), which received traditional instructor feedback with a focus on grammar and mechanics. Over the course of twelve weeks, students produced weekly essays, engaged in iterative revisions, and, for the experimental group, critically interacted with AI-generated feedback targeting content development, argument structure, and rhetorical clarity. Data sources included pre- and post-intervention writing assessments evaluated against a rubric emphasizing higher-order components (argumentation, coherence, and originality), weekly reflective journals, and semi-structured interviews with a subset of participants. Quantitative results revealed statistically significant gains in the experimental group’s higher-order writing dimensions compared to the control group, with notable improvements in argument development, logical coherence, and the generation of original insights. Qualitative findings further highlighted that students using GenAI engaged in deeper revision cycles, demonstrated increased metacognitive awareness, and reported heightened confidence in idea generation and critical evaluation of their own writing. Nevertheless, some risks were identified, including over-reliance on AI suggestions, occasional uncritical acceptance of machine-generated text, and uncertainties surrounding academic integrity. These findings underscore the dual role of GenAI as both a scaffold and a potential crutch in EFL writing pedagogy. The study concludes that when thoughtfully integrated into instructional design, GenAI can extend beyond its remedial function of grammar correction to become a catalyst for higher-order thinking. Pedagogical implications include the need for explicit training on critical engagement with AI outputs, structured reflective activities to reinforce metacognitive skills, and assessment frameworks that reward originality and critical reasoning rather than surface-level accuracy alone. By shifting the focus from linguistic correctness to cognitive depth, educators can leverage GenAI not merely as an editing tool but as an active partner in fostering analytical, evaluative, and creative writing competencies among EFL learners.

Objective: This study investigates the integration of generative artificial intelligence (GenAI) into educational activities in communication faculties across the three most populous cities in Türkiye: Istanbul, Ankara, and Izmir. Methods: Using a robust semi-structured in-depth interview technique, a qualitative research method, we conducted online interviews with 15 academics from communication faculties in state and private universities. Results: The findings, evaluated through the lens of the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory (DIT), are organized into several categories: "GenAI in the context of technology acceptance," "First encounter with GenAI," "Practices of use in academic activities," " Academics attitude towards students’ use of GenAI," "Potential benefits and challenges," "Institutional GenAI policies," "GenAI policies in Türkiye’s higher education," and "Future predictions." Conclusions: Academics reported using GenAI in both theoretical and practical courses, utilizing its creative capabilities. However, they also expressed a critical stance on ethical issues, such as inaccuracies, fabricated content, bias, potential loss of creativity, and copyright concerns. This critical perspective underscores their unwavering commitment to the ethical use of GenAI, reassuring about the responsible implementation of GenAI. Participants emphasized the importance of shifting from knowledge-based to skill-based education for the "Generation Prompt," and predicted a significant decline in media-related employment in the future.

Objective: This study aims to evaluate pharmacy students’ perceptions regarding the integration of generative artificial intelligence (GenAI) into pharmacy curricula, providing evidence to inform future curriculum development. Methods: A cross-sectional survey of Doctor of Pharmacy (PharmD) students at a single U.S. College of Pharmacy was conducted in April 2025. Students from all four professional years (P1–P4) were invited to participate. The 10-item survey assessed four domains: (1) General GenAI Use, (2) Knowledge and Experience with GenAI Tools, (3) Learning Preferences with GenAI, and (4) Perspectives on GenAI in the curriculum. Results: A total of 110 students responded (response rate = 12.4%). Most were P1 students (56/110, 50.9%). Many reported using GenAI tools for personal (65/110, 59.1%) and school-related purposes (64/110, 58.1%) sometimes, often, or frequently. ChatGPT was the most used tool. While 40% (40/99) agreed or strongly agreed that GenAI could enhance their learning, 62.6% (62/99) preferred traditional teaching methods. Open-ended responses (n = 25) reflected a mix of positive, neutral, and negative views on GenAI in education. Conclusions: Many pharmacy students in this cohort reported using GenAI tools and demonstrated a basic understanding of GenAI functions, yet students also reported that they preferred traditional learning methods and expressed mixed views on incorporating GenAI into teaching. These findings provide valuable insights for faculty and schools of pharmacy as they develop strategies to integrate GenAI into pharmacy education.

The rapid integration of generative artificial intelligence (GenAI) tools into academic settings, particularly graduate education, where research integrity is required, has raised questions about their ethical use. Adopting a scenario-based qualitative design, this study focuses on exploring language researchers’ perspectives on and reactions to potentially unethical GenAI practices in graduate research with an extended technology acceptance (E-TAM) perspective and thus providing a process-oriented framework including preventive strategies for the unethical uses of GenAI in graduate research. Data were collected through semi-structured interviews with 10 language researchers from different universities, focusing on hypothetical scenarios, and were analyzed thematically. The findings revealed that participants mostly conceptualized GenAI uses presented in scenarios as ethically suspect. However, they pointed out that they might tolerate the use of GenAI in data analysis and consider it ethically contingent, provided that transparency, responsibility, and accountability are ensured. Language researchers also proposed preventive strategies to avoid unethical uses of GenAI in academic contexts, and a framework was developed based on these suggestions. This framework, suggesting that ethical GenAI use in graduate research practices is a shared responsibility of graduate students, their supervisors and institutions, can be used as a guide to promote ethical GenAI use in graduate education. This study contributes a theoretically informed and empirically grounded framework for promoting ethical GenAI use in graduate research by integrating language researchers’ insights with an E-TAM perspective through a scenario-based approach that reveals context-dependent ethical reasoning.

This chapter explores the pioneering integration of generative artificial intelligence (GenAI) into teaching and assessment within a module at a UK university. This initiative represents a significant step in advancing AI literacy in higher education, aiming to equip students with essential employability skills. The innovative approach empowered students to use GenAI tools effectively, critically, ethically, and responsibly. The collaborative effort with students provided valuable insights into GenAI’s potential to enhance student learning and future career prospects. Key recommendations for educators include dispelling the notion that GenAI use equates to cheating and that its ethical and critical application should be promoted. Encouraging transparency in GenAI usage can mitigate student engagement issues, while continuous feedback from students ensures the module remains responsive to their needs and experiences. By making GenAI use explicit and teaching effective prompt engineering, the module fostered a transition from covert use to responsible application. Hands-on experiential learning sessions were pivotal in developing students’ GenAI proficiency, enhancing their engagement and skill development.

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student’s specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI’s knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Objective: this article explores the integration of generative artificial intelligence (GAI) into qualitative research, comparing its results with human-led analysis and traditional AI techniques. It provides empirical evidence on the strengths and limitations of GAI in thematic analysis while addressing ethical concerns and research integrity. Theoretical approach: the study situates itself within the debate on GAI in qualitative research, weighing utilitarian arguments such as efficiency and scalability against ethical concerns, such as the loss of human interpretive depth. It extends discussions on human-machine collaboration by incorporating a three-way comparison: human analysts, traditional AI (e.g., topic modeling), and GAI. Method: a qualitative secondary analysis (QSA) compares human-led analysis, traditional AI, and GAI using a dataset on women’s participation in the game development industry, aligning with Sustainable Development Goal (SDG) 5 on gender equality. The study evaluates outputs based on thematic accuracy, interpretive depth, and practical utility. Results: findings suggest that GAI excels in speed and scalability, particularly for deductive methodologies with predefined coding labels. However, human analysts outperform GAI in interpretive depth and theoretical connections. Traditional AI offers structured insights but lacks GAI’s adaptability or human researchers’ nuanced understanding. Conclusions: while GAI enhances efficiency and reduces costs, its limitations in replicating human interpretive capacity call for cautious integration. This study provides guidelines for leveraging GAI’s strengths while preserving human expertise, contributing to the broader discourse on technology’s role in qualitative research.

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G.