Generative AI in higher education psychology programs: a scoping review exploring the opportunities for its use in assessment methods

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.

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.

The rapid advancement of generative artificial intelligence (GenAI) has significantly impacted educational and professional practices, presenting both opportunities and challenges. This study explores the integration of GenAI into a systems engineering seminar, aiming to develop essential GenAI skills and enhance disciplinary knowledge. Two hypotheses guide this research: (H1) engaging with GenAI in research and design activities improves student proficiency in using GenAI, and (H2) engaging with GenAI in design activities related to advanced disciplinary knowledge improves their understanding and use. The study employs a case study approach combined with a survey, involving 26 graduate students in a systems engineering seminar. Students were encouraged to use GenAI tools for all tasks, including literature reviews, presentations, and a drone design challenge. Data was collected through recorded presentations and student interactions with GenAI tools. Data analysis involved systematic coding and thematic analysis of presentations, student–GenAI interactions, and survey responses, with triangulation across multiple data sources to ensure validity. The findings indicate that the students effectively learned about GenAI tools, demonstrated gradual improvements in using tools, criticized and selected among them, and even built a new GenAI tool. They demonstrated improved critical thinking and creativity, as evidenced by their ability to critically assess GenAI outputs and apply them to practical challenges like the drone design task. One student developed a custom GenAI tool by training ChatGPT-4o for specialized modeling tasks. The integration of GenAI in educational settings through self-directed learning, peer presentations, and design challenges appears to enhance learning experiences by fostering critical thinking and creativity. The evidence suggests that GenAI tools, when used with appropriate validation and critical assessment, may serve as valuable aids in developing engineering skills and addressing complex problems. Best practices in teaching about GenAI are provided.

Intelligent Tutoring Systems (ITS) are computer systems that mimic human tutoring behavior while providing immediate feedback. With the rise of Generative Artificial Intelligence (GenAI), numerous ITS integrated with GenAI have been developed. Student engagement is critical for improving learning processes and outcomes. Therefore, it is important to examine the effectiveness of ITS integrated with GenAI in promoting student engagement in educational practice. This paper presents an explanatory mixed-method case study involving 880 undergraduate students who used GPTutor, an ITS powered by GenAI. First, a survey research was conducted to investigate the relationship between students’ actual interaction with GPTutor and their self-reported student engagement from three dimensions: Behavioral Engagement, Cognitive Engagement, and Emotional Engagement. Next, focus groups were conducted with a subsample of survey participants to better understand how and under what circumstances GPTutor improved student engagement. The focus groups also explored potential design improvements for GPTutor and other ITS powered by GenAI. The results of the survey research revealed a complex relationship between feature usage and student engagement. Specifically, engagement with the chatbot is significantly and positively associated with behavioral and emotional engagement, but not cognitive engagement. The exercise generator feature had no significant associations with any of the three dimensions of student engagement. The results of the focus groups shed some light on these relationships, revealing how GPTutor was used only when it was perceived as useful, and this perceived usefulness was shaped by the students’ perception of the difficulty of the course and whether their support system could adequately address questions they may have. Its usefulness was found to increase as the course progressed, particularly as examinations approached. As the examinations approached, it was increasingly clear that the exercise generator was preferred over the chatbot. The participants also made this clear by expressing how GPTutor could be improved, notably by increasing the capabilities of the chatbot to include multimodal media, like video recordings of lectures. In general, leveraging survey data, interview data, and back-end trace data from GenAI, this research makes an original contribution to AI-supported effective learning environments and design strategies to optimize the educational experiences of higher education students.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Interest in using generative artificial intelligence (GenAI) in design education is growing, yet its impact on students’ creative processes and motivation remains underexplored, especially in advertising design. This qualitative exploratory case study examined how GenAI tools influenced 12 undergraduate graphic and multimedia design students during a 72-hour advertising challenge with GenAI at a private Malaysian university. Grounded in sociocultural theory’s zone of proximal development and self-determination theory, the study explored how GenAI functioned as an instructional support. Data were collected through three focus group discussions and analysed thematically. Findings suggest that GenAI enhanced ideation, visualisation and collaboration, supporting students’ sense of competence and relatedness. However, concerns emerged around creative autonomy and dependence on AI. The study positions GenAI as a conditional more knowledgeable other and highlights the importance of pedagogical strategies that foster critical reflection and ethical use. These insights contribute to the emerging discourse on AI in design education by connecting theory with empirical evidence and offering practical implications for curriculum development. Implications for practice or policy: Design educators should integrate GenAI tools thoughtfully to enhance creativity while ensuring students retain critical thinking and originality. Assessments should incorporate reflection to help students critically engage with AI-generated content and develop technical proficiency. Higher educational institutions must update curricula to align with evolving GenAI capabilities and industry needs. Ethical AI use should be emphasised to ensure responsible adoption in education and professional practice.

The rapid advancement of generative artificial intelligence (GenAI) has reshaped the landscape of research supervision in higher education, raising critical ethical, pedagogical, and methodological questions. This mixed-methods study, titled Generative AI as the “Third Eye” of Academic Advising: Ethical, Pedagogical, and Methodological Perspectives in Research Supervision, investigated how higher education research advisers integrate GenAI tools into the supervision of student research. Employing a convergent parallel mixed-methods design, the study gathered data from 30 research advisers across various academic disciplines in a private higher education institution in Laguna, Philippines. The quantitative component utilized a validated 41-item, four-point Likert scale questionnaire designed to measure the extent to which GenAI influences ethical standards, pedagogical practices, methodological decisions, and perceptions of authenticity and originality. The qualitative phase, on the other hand, involved semi-structured interviews with purposively selected participants to explore their lived experiences, ethical reflections, and policy perspectives regarding GenAI use. Quantitative results revealed that advisers uphold ethical principles, adopt transformative pedagogical practices, and integrate GenAI tools into methodological decision-making to a great extent (M = 3.54). Qualitative findings supported these results, generating themes such as enhanced efficiency and precision, heightened ethical vigilance, challenges in verifying originality and authorship, and the continued importance of human judgment in AI-assisted supervision. Despite these advancements, issues related to student overreliance, citation transparency, and unverifiable references were identified as persistent challenges. The study culminates in the development of a Proposed Institutional Policy Framework on the Ethical Use of Generative AI in Research Supervision, advocating disclosure protocols, AI literacy training, and verification mechanisms. This framework positions GenAI as a “third eye” that enhances rather than replaces human discernment. Overall, the study contributes actionable insights for policymakers, educators, and institutions, ensuring that AI-driven innovation aligns with ethical integrity, academic rigor, and responsible research mentorship.

Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain. This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature. Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques). In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care. This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

Identifying highly correlated determinants influencing student nurses' behavioral intention of using generative artificial intelligence (Generative AI): A network analysis.

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.

PurposeThe purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations.Design/methodology/approachThe study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI.FindingsThe study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly.Research limitations/implicationsThe small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding.Originality/valueThe study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.

The rapid advance of Generative Artificial Intelligence (GenAI) is reshaping the innovation logic of small and medium-sized enterprises (SMEs), driving a profound shift from “human-led and technology-assisted” processes toward “human-AI collaboration and algorithmic co-creation”. Grounded in the knowledge-based view and dynamic capability theory, this study develops and validates a systematic mechanism of GenAI-driven innovation model reconstruction. It identifies how GenAI facilitates knowledge creation, strengthens firms’ innovation capabilities, and ultimately enhances the formation of intelligent decision-making systems. Using a sample of 412 SMEs and Structural Equation Modelling (SEM) for empirical analysis, the results demonstrate that GenAI applications exert a significant positive influence on knowledge creation, which in turn substantially enhances innovation capability. Innovation capability functions as a key mediator between knowledge creation and intelligent decision-making. In addition to its indirect effects through knowledge and innovation mechanisms, GenAI also directly improves managerial strategic judgment through its reasoning, prediction, and solution-evaluation functions. The overall model confirms a robust sequential chain - “GenAI - knowledge creation - innovation capability - intelligent decision-making”. It exhibits strong explanatory power and stable path relationships. The study makes theoretical contributions by introducing the concepts of “algorithm-participatory knowledge creation” and “AI-enhanced dynamic capability”, thereby expanding research frontiers in digital innovation and organisational intelligence. Practically, it offers a structured pathway for SMEs seeking knowledge-driven innovation transformation and decision-making optimisation in the GenAI era, while also providing insights for policymakers aiming to promote AI-enabled SME development.

In the last few years, Generative Artificial Intelligence (GenAI) has transitioned from an experimental technology to a core strategic asset reshaping modern management. Since 2020, organizations worldwide have accelerated the adoption of GenAI tools—ranging from large language models (LLMs) to automated content-generation systems—to enhance manager-level productivity, decision accuracy, and overall organizational performance. Recent global surveys conducted in 2023 and 2024 indicate that nearly 78% of organizations have either implemented GenAI in at least one managerial function or plan to do so within a 2-year horizon. This marks a sharp rise from only 24% adoption in 2019, demonstrating a significant shift in digital transformation priorities. At the managerial level, GenAI has evolved into a performance multiplier by automating cognitively heavy tasks, reducing manual workloads, and enabling real-time strategic insights. Studies published between 2022–2024 reveal that managers spend approximately 35–45% less time on repetitive tasks such as report writing, documentation, information summarization, and email drafting when GenAI systems are integrated into everyday workflows. For example, organizations using AI-powered decision- support dashboards reported a 32% improvement in decision-making speed and a 29% reduction in operational delays caused by human bottlenecks. These improvements are particularly visible in sectors such as healthcare, finance, logistics, and education, where complex data-driven decisions are essential. GenAI also plays a crucial role in improving organizational performance by boosting innovation capacity, collaboration quality, and knowledge retention. Between 2020 and 2024, companies investing in GenAI-driven innovation ecosystems reported an average 22% growth in new product development speed and a 31% increase in internal process innovation. These gains arise from AI’s ability to generate new ideas, prototype conceptual frameworks, and synthesize cross-functional knowledge within seconds. Moreover, GenAI reduces communication friction by translating complex ideas into simple, actionable narratives, improving team alignment by 28%, as indicated in a 2023 workforce collaboration study. From a financial standpoint, early adopters of GenAI have observed significant operational savings. A 2024 industry-wide analysis recorded that organizations integrating generative AI into managerial workflows saved between $2.8 million to $8.7 million annually depending on company size and sector. These savings largely stem from productivity acceleration, reduction in rework, automation of managerial reporting, and optimization of human resource allocation. The return on investment (ROI) in GenAI systems has averaged 162% within the first year of deployment, particularly in data-intensive environments. Even small and medium enterprises (SMEs) reported measurable productivity spikes, with 61% achieving break-even ROI on GenAI tools within 9–14 months.

Throughout history, humans have experienced numerous transformative events. Currently, we are experiencing a new disruptive event, generative artificial intelligence (GenAI). The research objectives of this article are to analyse and discuss how communication professionals use GenAI in their work, to analyse and discuss to what extent communication professionals have the required knowledge and skills to use GenAI optimally, and finally to analyse and discuss what type of help communication professionals need in their GenAI communication practices. The article draws on quantitative and qualitative data from an online survey of communication professionals and on qualitative interview data from eight semi-structured research interviews. The analysis was framed by a specially developed human-machine communication network model, referred to as the HMC network model, and a thematic analysis. The analysis showed that GenAI tools are widely known by communication professionals, but that they still do not seem to use GenAI optimally when researching and writing. The analysis also indicated that communication professionals need GenAI competencies and training, and that they need models, frameworks, and guidelines on how to use GenAI. The article presents the HMC network model and three GenAI support tools designed to help communication professionals use GenAI in their communication practices.

This opinion paper discusses the rapid development of generative artificial intelligence (GenAI) tools, which have significantly impacted educational research. This study examines the diverse applications of GenAI in educational settings, highlighting how it can enhance data analysis, automate literature reviews using generative AI tools, and facilitate personalized learning. By incorporating GenAI techniques such as automated content generation, researchers may expedite the data collecting process, generate insights from large-scale datasets, and develop adaptive learning materials that respond to student needs individually. This study also demonstrates methodological improvements made possible by GenAI, such as enhanced research design and the promotion of collaboration across disciplines. The researchers highlight best practices and potential pitfalls related to using GenAI tools in education research through a review of recent literature and case studies. As generative AI continues to impact the educational system, researchers and educators must exercise caution to maximize its potential. This paper's ultimate goal is to give researchers and educators a framework for efficiently utilizing GenAI technology, stressing the value of data integrity and ethical issues in promoting creative research approaches.