Mapping the Research Landscape of AI Literacy and Instructional Innovation in Higher Education: A Bibliometric Study

Mapping the Research Landscape of AI Literacy and Instructional Innovation in Higher Education: A Bibliometric Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Mapping the Research Landscape of AI Literacy and Instructional Innovation in Higher Education: A Bibliometric Study Teresita Agsimon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9614045/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid emergence of generative artificial intelligence has significantly transformed the landscape of higher education, particularly in relation to AI literacy, digital competence, and instructional innovation. This study aimed to map the research landscape of generative artificial intelligence and AI literacy in higher education through a bibliometric analysis of global scholarly publications indexed in the Scopus database. Using a PRISMA-guided screening process, relevant publications published between 2020 and 2025 were retrieved, filtered, and analyzed using the Bibliometrix package and Biblioshiny interface in RStudio. A total of 584 peer-reviewed publications indexed in Scopus were analyzed using Bibliometrix and Biblioshiny in RStudio. The analysis examined annual scientific production, most relevant publication sources, country scientific production, international collaboration networks, keyword co-occurrence structures, and thematic development patterns. The findings revealed a substantial increase in scholarly production beginning in 2023, with publication growth accelerating rapidly following the widespread emergence of generative AI technologies such as ChatGPT. Educational technology journals emerged as the dominant publication outlets, highlighting the interdisciplinary integration of artificial intelligence, pedagogy, and digital learning research. The United States and China demonstrated the highest levels of scientific productivity and international collaboration, indicating their strong influence in shaping global AI literacy discourse. Keyword co-occurrence and thematic analyses identified artificial intelligence, AI literacy, higher education, and ChatGPT as central themes within the evolving research landscape. Emerging themes related to self-efficacy and critical thinking further suggested a growing shift toward human-centered and pedagogically oriented AI literacy frameworks. The study contributes to the growing body of knowledge on generative artificial intelligence in higher education by providing a comprehensive overview of research trends, thematic developments, and global scholarly collaboration patterns. The findings highlight the increasing institutional importance of AI literacy in preparing educators and learners for AI-integrated educational environments and underscore the need for future research focusing on ethical, pedagogical, and competency-based dimensions of generative artificial intelligence in higher education. Artificial Intelligence and Machine Learning Educational Philosophy and Theory Behavioral Economics generative artificial intelligence AI literacy higher education bibliometric analysis ChatGPT instructional innovation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction The rapid advancement of generative artificial intelligence (AI) technologies has significantly reshaped educational environments, research practices, and institutional approaches to teaching and learning within higher education. The emergence of large language models and AI-powered systems such as ChatGPT has accelerated the integration of artificial intelligence into academic activities, including content generation, assessment support, curriculum development, and instructional delivery. According to Zawacki-Richter, Olaf et al. (2019), artificial intelligence applications in higher education have increasingly influenced administrative systems, adaptive learning environments, and intelligent tutoring processes, demonstrating the expanding role of AI technologies within educational institutions. As universities continue adopting AI-assisted technologies, concerns regarding digital competence, ethical usage, academic integrity, and instructional preparedness have become central issues in educational discourse. Consequently, AI literacy has emerged as a critical competency required for both educators and students to effectively navigate AI-enabled academic environments. AI literacy extends beyond basic technological familiarity and encompasses the knowledge, skills, ethical awareness, and critical understanding necessary to interact responsibly and effectively with artificial intelligence systems. Ng, D. T. K. et al. (2021) defined AI literacy as a multidimensional competency involving technical understanding, critical evaluation, ethical awareness, and practical engagement with AI technologies. Within higher education, AI literacy has become closely associated with instructional innovation, faculty readiness, digital transformation, and future workforce preparation. Educational institutions are increasingly expected not only to integrate AI technologies into learning environments but also to develop pedagogical frameworks that promote responsible and meaningful AI utilization. Similarly, Crompton, Helen et al. (2023) emphasized that artificial intelligence in higher education has rapidly evolved into a major educational research domain involving teaching enhancement, learner support, assessment innovation, and institutional transformation. This growing institutional emphasis has contributed to the rapid expansion of scholarly research examining the implications of generative AI in education, particularly in relation to teaching effectiveness, learner engagement, critical thinking, and professional development. The increasing volume of publications related to generative artificial intelligence and AI literacy demonstrates the growing academic interest in understanding the opportunities and challenges associated with AI integration in higher education. However, despite the rapid growth of the literature, the intellectual structure, thematic evolution, and global research patterns within this emerging field remain fragmented and insufficiently synthesized. Existing studies frequently focus on specific technological applications or localized educational contexts, limiting broader understanding of how the field has evolved conceptually and institutionally over time. Kasneci, Enkelejda et al. (2023) highlighted that the emergence of large language models such as ChatGPT has introduced both transformative opportunities and complex challenges related to pedagogy, assessment reliability, academic integrity, and responsible AI integration within educational systems. These rapidly evolving developments further emphasize the need for systematic investigations capable of identifying dominant research themes, collaborative structures, and conceptual directions shaping AI literacy scholarship within higher education. Bibliometric analysis provides a systematic and quantitative approach for examining scholarly production, conceptual structures, collaboration networks, and thematic trends within a research field. Through bibliometric techniques, researchers can identify influential publication sources, dominant research themes, emerging scholarly directions, and global patterns of scientific collaboration. In the context of generative artificial intelligence and AI literacy, bibliometric analysis offers valuable insights into how higher education institutions and researchers are responding to the transformative impact of AI technologies on teaching and learning processes. Therefore, this study aimed to map the research landscape of artificial intelligence literacy and instructional innovation in higher education through a bibliometric analysis of publications indexed in the Scopus database. Specifically, the study examined annual scientific production, dominant publication sources, country scientific productivity, international collaboration networks, keyword co-occurrence structures, and thematic development patterns. By providing a comprehensive overview of the intellectual and thematic foundations of AI literacy research, the study contributes to the growing discourse on educational innovation, instructional transformation, and digital competence development within higher education institutions. 2 Methods This study employed a quantitative bibliometric research approach to systematically examine the global research landscape of artificial intelligence literacy and instructional innovation within higher education. Bibliometric analysis was utilized to evaluate scientific productivity, publication trends, collaboration structures, conceptual development, and thematic relationships within the emerging field. The study adopted performance analysis and science mapping techniques using bibliographic data retrieved from the Scopus database and analyzed through the Bibliometrix R-package and Biblioshiny platform in RStudio. 2.1 Study Design The study utilized a quantitative bibliometric research design to investigate the intellectual structure and scholarly development of research related to AI literacy and instructional innovation. Bibliometric analysis is widely recognized as an effective method for examining the evolution of scientific fields through quantitative assessment of publication outputs, citation structures, collaborative networks, and conceptual patterns. In the present study, both performance analysis and science mapping techniques were applied to identify influential publication sources, productive countries, collaborative relationships, thematic structures, and emerging research trends associated with artificial intelligence literacy in higher education. 2.2 Data Source The Scopus database served as the primary source of bibliographic data for this study. Scopus was selected because it is among the largest and most comprehensive multidisciplinary databases containing peer-reviewed scholarly publications from diverse academic disciplines. The database provides extensive bibliographic information suitable for bibliometric analysis, including citation records, publication metadata, author affiliations, abstracts, keywords, and source information. Its broad international coverage and structured indexing system make it highly appropriate for large-scale bibliometric investigations involving emerging interdisciplinary topics such as AI literacy and instructional innovation. 2.3 Data Search and Extraction The PRISMA-based screening procedure in Fig. 1 illustrates the systematic process employed in identifying, screening, and selecting relevant studies included in the bibliometric analysis. An initial retrieval of 1,454 documents from the Scopus database was conducted using keywords associated with artificial intelligence literacy and instructional innovation within higher education. Publications indexed in 2026 were excluded to avoid incomplete indexing records, resulting in 1,028 screened documents. Additional exclusions were applied to non-eligible document types including conference papers, conference reviews, book chapters, notes, editorials, and other non-peer-reviewed materials. After the screening and eligibility assessment, 584 studies were retained for the final bibliometric analysis. The screening process reflects the growing scholarly interest in artificial intelligence literacy while simultaneously emphasizing the importance of methodological rigor in bibliometric investigations. The substantial reduction in records after eligibility screening demonstrates that much of the early literature surrounding AI literacy remains concentrated in conference-based and exploratory outputs rather than mature peer-reviewed journal publications. This observation suggests that the field is still undergoing rapid intellectual expansion and conceptual stabilization as researchers continue to explore the pedagogical, technological, and institutional implications of artificial intelligence integration in higher education. The final dataset provides a focused and reliable foundation for examining the intellectual structure and thematic evolution of AI literacy research. By retaining only peer-reviewed articles and reviews, the study ensures greater consistency and analytical validity in the succeeding bibliometric analyses. The refined dataset subsequently enables a clearer examination of publication growth trends, influential publication sources, collaborative networks, and conceptual developments within the emerging research domain. 2.4 Data Pre-processing and Data Cleaning Prior to analysis, the retrieved dataset underwent preprocessing and data cleaning procedures to improve data consistency, reliability, and analytical accuracy. Duplicate records were examined and removed; however, no duplicate documents were identified within the final dataset. Metadata normalization and reference matching procedures were also performed using the Biblioshiny interface to standardize author names, institutional affiliations, citation formats, and keyword variations. These preprocessing procedures minimized inconsistencies in bibliographic metadata and improved the reliability of subsequent bibliometric mapping and network analyses. 2.5 Data Analysis The bibliometric analysis was conducted using the Bibliometrix R-package and the Biblioshiny web interface. Several bibliometric indicators and visualization techniques were employed to examine scientific productivity, collaborative relationships, conceptual structures, and thematic development within the field of AI literacy and instructional innovation. Performance analysis techniques included annual scientific production, most relevant publication sources, country scientific production, and international collaboration analysis. Science mapping techniques included keyword co-occurrence network analysis and thematic mapping to identify conceptual relationships and emerging thematic structures within the literature. Visualization outputs generated through Biblioshiny were exported as PNG figures and integrated into the manuscript to support the presentation, interpretation, and discussion of findings. The analyses collectively provided a comprehensive overview of the intellectual, conceptual, and collaborative development of AI literacy and instructional innovation research within higher education contexts. 3 Results The bibliometric analysis revealed the rapid expansion and increasing scholarly interest in artificial intelligence literacy and instructional innovation research within higher education. Although publication activity remained relatively limited during the early years of the study period, scientific production increased substantially beginning in 2023, reflecting the accelerating integration of generative artificial intelligence technologies into educational environments. The emergence of large language models and publicly accessible AI systems significantly intensified academic attention toward AI literacy, transforming it into one of the fastest-growing areas within educational technology and higher education research. The findings further indicate that AI literacy research has evolved into a highly interdisciplinary field involving contributions from education, computer science, educational technology, psychology, digital learning, instructional design, and management studies. The increasing adoption of artificial intelligence in teaching, assessment, curriculum development, and institutional operations has encouraged researchers from multiple disciplines to investigate the pedagogical, ethical, cognitive, and organizational implications of AI integration in higher education. Consequently, the scholarly landscape surrounding AI literacy and instructional innovation has become increasingly diverse, collaborative, and globally interconnected. Moreover, the bibliometric indicators revealed important patterns related to scientific productivity, thematic development, collaboration structures, conceptual relationships, and publication source distribution. Strong international collaboration networks were evident among technologically advanced countries, emphasizing the importance of cross-border cooperation in advancing AI literacy research and educational innovation. The analysis likewise highlighted influential publication outlets, dominant conceptual themes, and emerging scholarly directions that collectively shape the intellectual structure and continuing development of AI literacy and instructional innovation research within higher education. Figure 2 presents the annual scientific production related to artificial intelligence literacy and instructional innovation in higher education between 2020 and 2025. During the early years of the observed period, publication output remained relatively limited, reflecting the nascent stage of AI literacy discourse within educational research. However, scientific production increased substantially beginning in 2023 and accelerated sharply through 2024 and 2025. The steep upward trajectory demonstrates the rapid emergence of AI literacy as a major interdisciplinary research priority within higher education and educational technology studies. The dramatic increase in publication output may be associated with the global emergence of generative artificial intelligence platforms, particularly the widespread adoption of large language models and publicly accessible AI systems such as ChatGPT. The integration of generative AI into educational environments introduced significant opportunities and challenges related to teaching, assessment, academic integrity, instructional innovation, and digital competence. Consequently, universities and researchers increasingly recognized the urgent need to examine AI literacy as an essential competency for both educators and students operating within technology-driven learning environments. The observed publication growth further suggests that AI literacy research has transitioned from an exploratory topic into a rapidly institutionalizing field of inquiry. The acceleration of scientific production reflects growing concerns regarding responsible AI integration, ethical implementation, and the transformation of instructional practices in higher education. Increasing scholarly attention has consequently contributed to the development of specialized publication outlets and intellectual communities dedicated to artificial intelligence and educational innovation research. Following the observed increase in publication output, Fig. 3 identifies the most relevant publication sources contributing to the development of AI literacy and instructional innovation research. Journals such as Computers and Education: Artificial Intelligence , Education and Information Technologies , and Frontiers in Education emerged as the most productive publication outlets within the dataset. The concentration of publications within these journals demonstrates the growing integration of artificial intelligence research within educational and pedagogical scholarship. The dominance of educational technology journals indicates that AI literacy is increasingly being framed not solely as a technical competency but also as an instructional, institutional, and pedagogical concern. Scholarly attention has gradually expanded beyond computational capabilities toward broader discussions involving student preparedness, educator adaptation, curriculum innovation, and responsible AI utilization in academic settings. This interdisciplinary orientation highlights the convergence of technology studies, educational management, digital pedagogy, and higher education research in addressing the transformative impact of artificial intelligence. The distribution of productive publication sources also reflects the intellectual consolidation of AI literacy research within specialized academic communities. As publication activity continues to expand, journals focusing on educational innovation and technology-enhanced learning are likely to play increasingly influential roles in shaping future scholarly discourse. The concentration of publications within established educational technology outlets further reinforces the legitimacy and growing maturity of AI literacy as a recognized field of educational research. Beyond publication source distribution, Fig. 4 illustrates the global geographical distribution of scientific production related to AI literacy and instructional innovation. The United States and China emerged as the leading contributors in terms of publication output, followed by several European and Asian countries with strong digital infrastructure and higher education research systems. The global distribution of publications indicates that AI literacy has become an internationally recognized educational concern across diverse academic and institutional contexts. The dominance of highly industrialized countries reflects disparities in technological readiness, research funding capacity, digital infrastructure, and institutional adoption of artificial intelligence technologies. Countries with advanced AI ecosystems are more likely to invest in educational innovation, digital transformation initiatives, and interdisciplinary AI-related research programs. Consequently, these nations have assumed central roles in shaping scholarly discourse surrounding AI literacy, instructional adaptation, and responsible technology integration within higher education environments. At the same time, the growing participation of developing and emerging economies suggests increasing global awareness regarding the importance of AI literacy competencies in education. The international expansion of research activity demonstrates that concerns regarding AI integration are no longer limited to technologically dominant countries but are increasingly recognized as global educational priorities. This broadening geographical participation subsequently contributes to more diverse perspectives concerning instructional innovation, digital inclusion, and educational transformation in the era of generative artificial intelligence. In addition to country productivity, Fig. 5 presents the international collaboration network among countries engaged in AI literacy and instructional innovation research. Strong collaborative relationships were observed among major research-producing countries, particularly between the United States, China, European nations, and several Asia-Pacific institutions. The network structure demonstrates that AI literacy research has evolved into a globally collaborative field characterized by cross-border scholarly partnerships and knowledge-sharing activities. The increasing level of international collaboration reflects the inherently global nature of artificial intelligence integration within higher education systems. Educational institutions worldwide are simultaneously confronting challenges related to digital transformation, ethical AI utilization, academic integrity, curriculum redesign, and technological preparedness. As a result, researchers from multiple countries increasingly engage in collaborative studies to exchange expertise, develop shared frameworks, and examine the broader implications of generative artificial intelligence in teaching and learning environments. The collaboration network further highlights the role of transnational research partnerships in accelerating scientific development within emerging educational fields. International cooperation facilitates access to diverse educational contexts, interdisciplinary perspectives, and comparative institutional experiences that enrich scholarly understanding of AI literacy implementation. The strengthening of global research networks may therefore contribute to the development of more inclusive, adaptive, and internationally relevant approaches to AI literacy and instructional innovation in higher education. To further examine the intellectual structure of the field, Fig. 6 presents the keyword co-occurrence network underlying AI literacy and instructional innovation research. Central keywords such as “AI literacy,” “artificial intelligence,” “higher education,” and “ChatGPT” occupied dominant positions within the network, indicating their strong conceptual interconnectedness across the literature. Additional thematic clusters involving self-efficacy, critical thinking, instructional technology, and digital competence further demonstrate the multidimensional nature of AI literacy research. The prominence of AI literacy and higher education within the network suggests that scholarly attention has increasingly shifted toward understanding how educational institutions can prepare students and educators for AI-integrated learning environments. The emergence of ChatGPT-related clusters reflects the transformative influence of generative artificial intelligence technologies on teaching practices, assessment systems, and student engagement. Simultaneously, the appearance of themes associated with critical thinking and self-efficacy indicates growing concerns regarding learners’ ability to critically evaluate AI-generated content and adapt confidently to rapidly evolving digital environments. The network structure also demonstrates that AI literacy research extends beyond technical knowledge acquisition and increasingly incorporates pedagogical, cognitive, and ethical dimensions of educational transformation. The interconnected relationships among keywords suggest that the field is evolving toward a more holistic understanding of AI literacy that encompasses responsible AI use, instructional adaptation, digital competence, and human-centered learning approaches. These conceptual relationships provide a foundation for understanding the thematic development and intellectual maturity of the research domain. . Building upon the conceptual relationships identified in the keyword network, Fig. 7 illustrates the thematic map representing the developmental structure and conceptual maturity of research themes associated with AI literacy and instructional innovation. Core themes such as “AI literacy,” “artificial intelligence,” and “higher education” appeared within the basic and motor theme quadrants, indicating their high relevance and centrality within the field. Emerging themes involving critical thinking, self-efficacy, and ChatGPT also appeared across the thematic structure, reflecting the expanding intellectual scope of AI literacy research. The positioning of AI literacy within the central thematic quadrants suggests that the concept has become a foundational component of contemporary educational technology discourse. Its strong centrality indicates that AI literacy functions as a connecting framework linking diverse research areas including instructional innovation, digital pedagogy, ethical AI use, and educational transformation. Meanwhile, themes related to critical thinking and self-efficacy demonstrate increasing recognition of the human-centered competencies required for effective participation in AI-supported educational environments. The thematic distribution further suggests that AI literacy research is transitioning toward greater conceptual sophistication and interdisciplinary integration. The emergence of pedagogical, cognitive, and ethical themes indicates that future research may increasingly focus on the long-term educational implications of generative artificial intelligence adoption in higher education. As the field continues to evolve, AI literacy is likely to remain a central research priority shaping instructional innovation, institutional policy development, and digital transformation initiatives within global higher education systems. 4 Discussion The findings of the study indicate that artificial intelligence literacy and instructional innovation have rapidly emerged as important areas of scholarly inquiry within higher education. The substantial increase in publication activity after 2023 reflects the transformative influence of generative artificial intelligence technologies on teaching, learning, and institutional practices. The rapid expansion of the field demonstrates the growing recognition that AI literacy has become an essential competency for educators and students operating within increasingly digital and AI-supported academic environments. These findings are consistent with the observations of Kasneci, Enkelejda et al. (2023), who emphasized that large language models such as ChatGPT are significantly reshaping educational processes and creating new demands for responsible AI integration within academic settings. The results also highlight the interdisciplinary and globally collaborative nature of AI literacy research. The dominance of technologically advanced countries and educational technology journals suggests that institutional readiness, digital infrastructure, and research capacity significantly influence scholarly engagement with AI-related educational transformation. Similarly, Crompton, Helen et al. (2023) noted that artificial intelligence research in higher education increasingly intersects with instructional design, digital pedagogy, and institutional innovation. At the same time, the thematic and conceptual analyses reveal that current scholarship extends beyond technical AI adoption toward broader concerns involving critical thinking, ethical AI usage, instructional adaptation, and learner preparedness. These patterns align with the findings of Ng, D. T. K. et al. (2023), who argued that AI literacy encompasses not only technical competencies but also ethical awareness, critical evaluation, and responsible engagement with artificial intelligence systems. The study further demonstrates that AI literacy and instructional innovation research remains in a rapidly developing stage characterized by strong publication growth, expanding international collaboration, and increasing conceptual diversification. The emergence of themes associated with self-efficacy, critical thinking, and ChatGPT suggests that future educational research may increasingly focus on the human-centered implications of AI integration in teaching and learning environments. In support of this perspective, Tlili, Ahmed et al. (2023) emphasized that generative AI technologies present both opportunities and challenges related to learner engagement, pedagogical adaptation, and responsible educational implementation. Overall, the findings underscore the strategic role of higher education institutions in shaping effective, ethical, and sustainable approaches to AI literacy development within contemporary academic environments. 5 Conclusion This study mapped the research landscape of artificial intelligence literacy and instructional innovation in higher education through bibliometric analysis of publications indexed in the Scopus database. The findings revealed a rapidly expanding and globally interconnected research field driven by the increasing adoption of generative artificial intelligence technologies within academic environments. AI literacy, higher education, and artificial intelligence emerged as dominant and foundational themes shaping the intellectual structure of the field. The study also demonstrated that AI literacy research has become increasingly interdisciplinary, collaborative, and pedagogically oriented. Thematic and conceptual analyses indicated growing scholarly attention toward instructional adaptation, critical thinking, ethical AI usage, and digital competence development within higher education institutions. These findings highlight the important role of universities in preparing educators and students for AI-integrated learning environments. Overall, the study contributes to the growing body of knowledge on educational innovation and digital transformation by providing a comprehensive overview of the scientific development, thematic structures, and collaborative dynamics of AI literacy research. The findings may serve as a useful reference for researchers, educators, policymakers, and higher education institutions seeking to strengthen AI literacy and instructional innovation in the evolving landscape of higher education. Declarations Competing Interests The author declares that there are no competing interests related to this study. Ethical Approval This study utilized bibliographic data obtained from the Scopus database and did not involve human participants, personal data, or animal subjects. Therefore, ethical approval was not required. Funding No funding was received for the conduct of this study. Author Contributions The author solely conceptualized the study, conducted data collection and analysis, interpreted the findings, and prepared the manuscript. Data Availability The bibliographic dataset analyzed during the current study was retrieved from the Scopus database and is available from the corresponding author upon reasonable request. References Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2 (3), 431–440. https://doi.org/10.1007/s43681-021-00096-7 Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability, 13 (2), 800. https://doi.org/10.3390/su13020800 Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. 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Process\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/6587f7b5c3a8c342bcf31a79.png"},{"id":108804607,"identity":"39d6b2ad-fe94-4274-ae5e-e2d0aeb9e0a2","added_by":"auto","created_at":"2026-05-08 15:22:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158529,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Scientific Production\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/cdd8d92b71a58fae270f1cfb.png"},{"id":108580827,"identity":"71ac7711-3b68-4e1a-a84e-1d16cc585a8c","added_by":"auto","created_at":"2026-05-06 08:07:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":445983,"visible":true,"origin":"","legend":"\u003cp\u003eMost Relevant Sources\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/19c3ca4e8902260107637218.png"},{"id":108805448,"identity":"c1af3fdf-c5e4-4d52-a6cb-47f183149450","added_by":"auto","created_at":"2026-05-08 15:26:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269271,"visible":true,"origin":"","legend":"\u003cp\u003eCountry Scientific Production\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/01049fd9aa99944ff2a7a272.png"},{"id":108805555,"identity":"e8700718-142b-42ad-8e33-5d8b2b65a1bd","added_by":"auto","created_at":"2026-05-08 15:26:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":307124,"visible":true,"origin":"","legend":"\u003cp\u003eInternational Collaboration Network\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/149e093f98d8795edd5ad585.png"},{"id":108580829,"identity":"6aefa561-ec35-4c9c-9a83-1a22addd2e45","added_by":"auto","created_at":"2026-05-06 08:07:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289237,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword Co-occurrence Network\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/fb323ef8a9f3d9854dc2230d.png"},{"id":108580831,"identity":"6b359cd4-990c-4cd0-91e5-99fc079af87b","added_by":"auto","created_at":"2026-05-06 08:07:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":264589,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Map\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/db7a92adb9ac6fe737117689.png"},{"id":108977089,"identity":"613f59c0-35df-4f6e-8b6e-4561d111a64f","added_by":"auto","created_at":"2026-05-11 11:30:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2718723,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9614045/v1/c47e0a1a-2807-4656-8b6c-8edc3095a623.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping the Research Landscape of AI Literacy and Instructional Innovation in Higher Education: A Bibliometric Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe rapid advancement of generative artificial intelligence (AI) technologies has significantly reshaped educational environments, research practices, and institutional approaches to teaching and learning within higher education. The emergence of large language models and AI-powered systems such as ChatGPT has accelerated the integration of artificial intelligence into academic activities, including content generation, assessment support, curriculum development, and instructional delivery. According to Zawacki-Richter, Olaf et al. (2019), artificial intelligence applications in higher education have increasingly influenced administrative systems, adaptive learning environments, and intelligent tutoring processes, demonstrating the expanding role of AI technologies within educational institutions. As universities continue adopting AI-assisted technologies, concerns regarding digital competence, ethical usage, academic integrity, and instructional preparedness have become central issues in educational discourse. Consequently, AI literacy has emerged as a critical competency required for both educators and students to effectively navigate AI-enabled academic environments.\u003c/p\u003e \u003cp\u003eAI literacy extends beyond basic technological familiarity and encompasses the knowledge, skills, ethical awareness, and critical understanding necessary to interact responsibly and effectively with artificial intelligence systems. Ng, D. T. K. et al. (2021) defined AI literacy as a multidimensional competency involving technical understanding, critical evaluation, ethical awareness, and practical engagement with AI technologies. Within higher education, AI literacy has become closely associated with instructional innovation, faculty readiness, digital transformation, and future workforce preparation. Educational institutions are increasingly expected not only to integrate AI technologies into learning environments but also to develop pedagogical frameworks that promote responsible and meaningful AI utilization. Similarly, Crompton, Helen et al. (2023) emphasized that artificial intelligence in higher education has rapidly evolved into a major educational research domain involving teaching enhancement, learner support, assessment innovation, and institutional transformation. This growing institutional emphasis has contributed to the rapid expansion of scholarly research examining the implications of generative AI in education, particularly in relation to teaching effectiveness, learner engagement, critical thinking, and professional development.\u003c/p\u003e \u003cp\u003eThe increasing volume of publications related to generative artificial intelligence and AI literacy demonstrates the growing academic interest in understanding the opportunities and challenges associated with AI integration in higher education. However, despite the rapid growth of the literature, the intellectual structure, thematic evolution, and global research patterns within this emerging field remain fragmented and insufficiently synthesized. Existing studies frequently focus on specific technological applications or localized educational contexts, limiting broader understanding of how the field has evolved conceptually and institutionally over time. Kasneci, Enkelejda et al. (2023) highlighted that the emergence of large language models such as ChatGPT has introduced both transformative opportunities and complex challenges related to pedagogy, assessment reliability, academic integrity, and responsible AI integration within educational systems. These rapidly evolving developments further emphasize the need for systematic investigations capable of identifying dominant research themes, collaborative structures, and conceptual directions shaping AI literacy scholarship within higher education.\u003c/p\u003e \u003cp\u003eBibliometric analysis provides a systematic and quantitative approach for examining scholarly production, conceptual structures, collaboration networks, and thematic trends within a research field. Through bibliometric techniques, researchers can identify influential publication sources, dominant research themes, emerging scholarly directions, and global patterns of scientific collaboration. In the context of generative artificial intelligence and AI literacy, bibliometric analysis offers valuable insights into how higher education institutions and researchers are responding to the transformative impact of AI technologies on teaching and learning processes.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to map the research landscape of artificial intelligence literacy and instructional innovation in higher education through a bibliometric analysis of publications indexed in the Scopus database. Specifically, the study examined annual scientific production, dominant publication sources, country scientific productivity, international collaboration networks, keyword co-occurrence structures, and thematic development patterns. By providing a comprehensive overview of the intellectual and thematic foundations of AI literacy research, the study contributes to the growing discourse on educational innovation, instructional transformation, and digital competence development within higher education institutions.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThis study employed a quantitative bibliometric research approach to systematically examine the global research landscape of artificial intelligence literacy and instructional innovation within higher education. Bibliometric analysis was utilized to evaluate scientific productivity, publication trends, collaboration structures, conceptual development, and thematic relationships within the emerging field. The study adopted performance analysis and science mapping techniques using bibliographic data retrieved from the Scopus database and analyzed through the Bibliometrix R-package and Biblioshiny platform in RStudio.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThe study utilized a quantitative bibliometric research design to investigate the intellectual structure and scholarly development of research related to AI literacy and instructional innovation. Bibliometric analysis is widely recognized as an effective method for examining the evolution of scientific fields through quantitative assessment of publication outputs, citation structures, collaborative networks, and conceptual patterns. In the present study, both performance analysis and science mapping techniques were applied to identify influential publication sources, productive countries, collaborative relationships, thematic structures, and emerging research trends associated with artificial intelligence literacy in higher education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source\u003c/h2\u003e \u003cp\u003eThe Scopus database served as the primary source of bibliographic data for this study. Scopus was selected because it is among the largest and most comprehensive multidisciplinary databases containing peer-reviewed scholarly publications from diverse academic disciplines. The database provides extensive bibliographic information suitable for bibliometric analysis, including citation records, publication metadata, author affiliations, abstracts, keywords, and source information. Its broad international coverage and structured indexing system make it highly appropriate for large-scale bibliometric investigations involving emerging interdisciplinary topics such as AI literacy and instructional innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Search and Extraction\u003c/h2\u003e \u003cp\u003eThe PRISMA-based screening procedure in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the systematic process employed in identifying, screening, and selecting relevant studies included in the bibliometric analysis. An initial retrieval of 1,454 documents from the Scopus database was conducted using keywords associated with artificial intelligence literacy and instructional innovation within higher education. Publications indexed in 2026 were excluded to avoid incomplete indexing records, resulting in 1,028 screened documents. Additional exclusions were applied to non-eligible document types including conference papers, conference reviews, book chapters, notes, editorials, and other non-peer-reviewed materials. After the screening and eligibility assessment, 584 studies were retained for the final bibliometric analysis.\u003c/p\u003e \u003cp\u003eThe screening process reflects the growing scholarly interest in artificial intelligence literacy while simultaneously emphasizing the importance of methodological rigor in bibliometric investigations. The substantial reduction in records after eligibility screening demonstrates that much of the early literature surrounding AI literacy remains concentrated in conference-based and exploratory outputs rather than mature peer-reviewed journal publications. This observation suggests that the field is still undergoing rapid intellectual expansion and conceptual stabilization as researchers continue to explore the pedagogical, technological, and institutional implications of artificial intelligence integration in higher education.\u003c/p\u003e \u003cp\u003eThe final dataset provides a focused and reliable foundation for examining the intellectual structure and thematic evolution of AI literacy research. By retaining only peer-reviewed articles and reviews, the study ensures greater consistency and analytical validity in the succeeding bibliometric analyses. The refined dataset subsequently enables a clearer examination of publication growth trends, influential publication sources, collaborative networks, and conceptual developments within the emerging research domain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Pre-processing and Data Cleaning\u003c/h2\u003e \u003cp\u003ePrior to analysis, the retrieved dataset underwent preprocessing and data cleaning procedures to improve data consistency, reliability, and analytical accuracy. Duplicate records were examined and removed; however, no duplicate documents were identified within the final dataset. Metadata normalization and reference matching procedures were also performed using the Biblioshiny interface to standardize author names, institutional affiliations, citation formats, and keyword variations. These preprocessing procedures minimized inconsistencies in bibliographic metadata and improved the reliability of subsequent bibliometric mapping and network analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003eThe bibliometric analysis was conducted using the Bibliometrix R-package and the Biblioshiny web interface. Several bibliometric indicators and visualization techniques were employed to examine scientific productivity, collaborative relationships, conceptual structures, and thematic development within the field of AI literacy and instructional innovation. Performance analysis techniques included annual scientific production, most relevant publication sources, country scientific production, and international collaboration analysis. Science mapping techniques included keyword co-occurrence network analysis and thematic mapping to identify conceptual relationships and emerging thematic structures within the literature.\u003c/p\u003e \u003cp\u003eVisualization outputs generated through Biblioshiny were exported as PNG figures and integrated into the manuscript to support the presentation, interpretation, and discussion of findings. The analyses collectively provided a comprehensive overview of the intellectual, conceptual, and collaborative development of AI literacy and instructional innovation research within higher education contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe bibliometric analysis revealed the rapid expansion and increasing scholarly interest in artificial intelligence literacy and instructional innovation research within higher education. Although publication activity remained relatively limited during the early years of the study period, scientific production increased substantially beginning in 2023, reflecting the accelerating integration of generative artificial intelligence technologies into educational environments. The emergence of large language models and publicly accessible AI systems significantly intensified academic attention toward AI literacy, transforming it into one of the fastest-growing areas within educational technology and higher education research.\u003c/p\u003e \u003cp\u003eThe findings further indicate that AI literacy research has evolved into a highly interdisciplinary field involving contributions from education, computer science, educational technology, psychology, digital learning, instructional design, and management studies. The increasing adoption of artificial intelligence in teaching, assessment, curriculum development, and institutional operations has encouraged researchers from multiple disciplines to investigate the pedagogical, ethical, cognitive, and organizational implications of AI integration in higher education. Consequently, the scholarly landscape surrounding AI literacy and instructional innovation has become increasingly diverse, collaborative, and globally interconnected.\u003c/p\u003e \u003cp\u003eMoreover, the bibliometric indicators revealed important patterns related to scientific productivity, thematic development, collaboration structures, conceptual relationships, and publication source distribution. Strong international collaboration networks were evident among technologically advanced countries, emphasizing the importance of cross-border cooperation in advancing AI literacy research and educational innovation. The analysis likewise highlighted influential publication outlets, dominant conceptual themes, and emerging scholarly directions that collectively shape the intellectual structure and continuing development of AI literacy and instructional innovation research within higher education.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the annual scientific production related to artificial intelligence literacy and instructional innovation in higher education between 2020 and 2025. During the early years of the observed period, publication output remained relatively limited, reflecting the nascent stage of AI literacy discourse within educational research. However, scientific production increased substantially beginning in 2023 and accelerated sharply through 2024 and 2025. The steep upward trajectory demonstrates the rapid emergence of AI literacy as a major interdisciplinary research priority within higher education and educational technology studies.\u003c/p\u003e \u003cp\u003eThe dramatic increase in publication output may be associated with the global emergence of generative artificial intelligence platforms, particularly the widespread adoption of large language models and publicly accessible AI systems such as ChatGPT. The integration of generative AI into educational environments introduced significant opportunities and challenges related to teaching, assessment, academic integrity, instructional innovation, and digital competence. Consequently, universities and researchers increasingly recognized the urgent need to examine AI literacy as an essential competency for both educators and students operating within technology-driven learning environments.\u003c/p\u003e \u003cp\u003eThe observed publication growth further suggests that AI literacy research has transitioned from an exploratory topic into a rapidly institutionalizing field of inquiry. The acceleration of scientific production reflects growing concerns regarding responsible AI integration, ethical implementation, and the transformation of instructional practices in higher education. Increasing scholarly attention has consequently contributed to the development of specialized publication outlets and intellectual communities dedicated to artificial intelligence and educational innovation research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the observed increase in publication output, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e identifies the most relevant publication sources contributing to the development of AI literacy and instructional innovation research. Journals such as \u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e, \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, and \u003cem\u003eFrontiers in Education\u003c/em\u003e emerged as the most productive publication outlets within the dataset. The concentration of publications within these journals demonstrates the growing integration of artificial intelligence research within educational and pedagogical scholarship.\u003c/p\u003e \u003cp\u003eThe dominance of educational technology journals indicates that AI literacy is increasingly being framed not solely as a technical competency but also as an instructional, institutional, and pedagogical concern. Scholarly attention has gradually expanded beyond computational capabilities toward broader discussions involving student preparedness, educator adaptation, curriculum innovation, and responsible AI utilization in academic settings. This interdisciplinary orientation highlights the convergence of technology studies, educational management, digital pedagogy, and higher education research in addressing the transformative impact of artificial intelligence.\u003c/p\u003e \u003cp\u003eThe distribution of productive publication sources also reflects the intellectual consolidation of AI literacy research within specialized academic communities. As publication activity continues to expand, journals focusing on educational innovation and technology-enhanced learning are likely to play increasingly influential roles in shaping future scholarly discourse. The concentration of publications within established educational technology outlets further reinforces the legitimacy and growing maturity of AI literacy as a recognized field of educational research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond publication source distribution, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the global geographical distribution of scientific production related to AI literacy and instructional innovation. The United States and China emerged as the leading contributors in terms of publication output, followed by several European and Asian countries with strong digital infrastructure and higher education research systems. The global distribution of publications indicates that AI literacy has become an internationally recognized educational concern across diverse academic and institutional contexts.\u003c/p\u003e \u003cp\u003eThe dominance of highly industrialized countries reflects disparities in technological readiness, research funding capacity, digital infrastructure, and institutional adoption of artificial intelligence technologies. Countries with advanced AI ecosystems are more likely to invest in educational innovation, digital transformation initiatives, and interdisciplinary AI-related research programs. Consequently, these nations have assumed central roles in shaping scholarly discourse surrounding AI literacy, instructional adaptation, and responsible technology integration within higher education environments.\u003c/p\u003e \u003cp\u003eAt the same time, the growing participation of developing and emerging economies suggests increasing global awareness regarding the importance of AI literacy competencies in education. The international expansion of research activity demonstrates that concerns regarding AI integration are no longer limited to technologically dominant countries but are increasingly recognized as global educational priorities. This broadening geographical participation subsequently contributes to more diverse perspectives concerning instructional innovation, digital inclusion, and educational transformation in the era of generative artificial intelligence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to country productivity, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the international collaboration network among countries engaged in AI literacy and instructional innovation research. Strong collaborative relationships were observed among major research-producing countries, particularly between the United States, China, European nations, and several Asia-Pacific institutions. The network structure demonstrates that AI literacy research has evolved into a globally collaborative field characterized by cross-border scholarly partnerships and knowledge-sharing activities.\u003c/p\u003e \u003cp\u003eThe increasing level of international collaboration reflects the inherently global nature of artificial intelligence integration within higher education systems. Educational institutions worldwide are simultaneously confronting challenges related to digital transformation, ethical AI utilization, academic integrity, curriculum redesign, and technological preparedness. As a result, researchers from multiple countries increasingly engage in collaborative studies to exchange expertise, develop shared frameworks, and examine the broader implications of generative artificial intelligence in teaching and learning environments.\u003c/p\u003e \u003cp\u003eThe collaboration network further highlights the role of transnational research partnerships in accelerating scientific development within emerging educational fields. International cooperation facilitates access to diverse educational contexts, interdisciplinary perspectives, and comparative institutional experiences that enrich scholarly understanding of AI literacy implementation. The strengthening of global research networks may therefore contribute to the development of more inclusive, adaptive, and internationally relevant approaches to AI literacy and instructional innovation in higher education.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further examine the intellectual structure of the field, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the keyword co-occurrence network underlying AI literacy and instructional innovation research. Central keywords such as \u0026ldquo;AI literacy,\u0026rdquo; \u0026ldquo;artificial intelligence,\u0026rdquo; \u0026ldquo;higher education,\u0026rdquo; and \u0026ldquo;ChatGPT\u0026rdquo; occupied dominant positions within the network, indicating their strong conceptual interconnectedness across the literature. Additional thematic clusters involving self-efficacy, critical thinking, instructional technology, and digital competence further demonstrate the multidimensional nature of AI literacy research.\u003c/p\u003e \u003cp\u003eThe prominence of AI literacy and higher education within the network suggests that scholarly attention has increasingly shifted toward understanding how educational institutions can prepare students and educators for AI-integrated learning environments. The emergence of ChatGPT-related clusters reflects the transformative influence of generative artificial intelligence technologies on teaching practices, assessment systems, and student engagement. Simultaneously, the appearance of themes associated with critical thinking and self-efficacy indicates growing concerns regarding learners\u0026rsquo; ability to critically evaluate AI-generated content and adapt confidently to rapidly evolving digital environments.\u003c/p\u003e \u003cp\u003eThe network structure also demonstrates that AI literacy research extends beyond technical knowledge acquisition and increasingly incorporates pedagogical, cognitive, and ethical dimensions of educational transformation. The interconnected relationships among keywords suggest that the field is evolving toward a more holistic understanding of AI literacy that encompasses responsible AI use, instructional adaptation, digital competence, and human-centered learning approaches. These conceptual relationships provide a foundation for understanding the thematic development and intellectual maturity of the research domain.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBuilding upon the conceptual relationships identified in the keyword network, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the thematic map representing the developmental structure and conceptual maturity of research themes associated with AI literacy and instructional innovation. Core themes such as \u0026ldquo;AI literacy,\u0026rdquo; \u0026ldquo;artificial intelligence,\u0026rdquo; and \u0026ldquo;higher education\u0026rdquo; appeared within the basic and motor theme quadrants, indicating their high relevance and centrality within the field. Emerging themes involving critical thinking, self-efficacy, and ChatGPT also appeared across the thematic structure, reflecting the expanding intellectual scope of AI literacy research.\u003c/p\u003e \u003cp\u003eThe positioning of AI literacy within the central thematic quadrants suggests that the concept has become a foundational component of contemporary educational technology discourse. Its strong centrality indicates that AI literacy functions as a connecting framework linking diverse research areas including instructional innovation, digital pedagogy, ethical AI use, and educational transformation. Meanwhile, themes related to critical thinking and self-efficacy demonstrate increasing recognition of the human-centered competencies required for effective participation in AI-supported educational environments.\u003c/p\u003e \u003cp\u003eThe thematic distribution further suggests that AI literacy research is transitioning toward greater conceptual sophistication and interdisciplinary integration. The emergence of pedagogical, cognitive, and ethical themes indicates that future research may increasingly focus on the long-term educational implications of generative artificial intelligence adoption in higher education. As the field continues to evolve, AI literacy is likely to remain a central research priority shaping instructional innovation, institutional policy development, and digital transformation initiatives within global higher education systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe findings of the study indicate that artificial intelligence literacy and instructional innovation have rapidly emerged as important areas of scholarly inquiry within higher education. The substantial increase in publication activity after 2023 reflects the transformative influence of generative artificial intelligence technologies on teaching, learning, and institutional practices. The rapid expansion of the field demonstrates the growing recognition that AI literacy has become an essential competency for educators and students operating within increasingly digital and AI-supported academic environments. These findings are consistent with the observations of Kasneci, Enkelejda et al. (2023), who emphasized that large language models such as ChatGPT are significantly reshaping educational processes and creating new demands for responsible AI integration within academic settings.\u003c/p\u003e \u003cp\u003eThe results also highlight the interdisciplinary and globally collaborative nature of AI literacy research. The dominance of technologically advanced countries and educational technology journals suggests that institutional readiness, digital infrastructure, and research capacity significantly influence scholarly engagement with AI-related educational transformation. Similarly, Crompton, Helen et al. (2023) noted that artificial intelligence research in higher education increasingly intersects with instructional design, digital pedagogy, and institutional innovation. At the same time, the thematic and conceptual analyses reveal that current scholarship extends beyond technical AI adoption toward broader concerns involving critical thinking, ethical AI usage, instructional adaptation, and learner preparedness. These patterns align with the findings of Ng, D. T. K. et al. (2023), who argued that AI literacy encompasses not only technical competencies but also ethical awareness, critical evaluation, and responsible engagement with artificial intelligence systems.\u003c/p\u003e \u003cp\u003eThe study further demonstrates that AI literacy and instructional innovation research remains in a rapidly developing stage characterized by strong publication growth, expanding international collaboration, and increasing conceptual diversification. The emergence of themes associated with self-efficacy, critical thinking, and ChatGPT suggests that future educational research may increasingly focus on the human-centered implications of AI integration in teaching and learning environments. In support of this perspective, Tlili, Ahmed et al. (2023) emphasized that generative AI technologies present both opportunities and challenges related to learner engagement, pedagogical adaptation, and responsible educational implementation. Overall, the findings underscore the strategic role of higher education institutions in shaping effective, ethical, and sustainable approaches to AI literacy development within contemporary academic environments.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study mapped the research landscape of artificial intelligence literacy and instructional innovation in higher education through bibliometric analysis of publications indexed in the Scopus database. The findings revealed a rapidly expanding and globally interconnected research field driven by the increasing adoption of generative artificial intelligence technologies within academic environments. AI literacy, higher education, and artificial intelligence emerged as dominant and foundational themes shaping the intellectual structure of the field.\u003c/p\u003e \u003cp\u003eThe study also demonstrated that AI literacy research has become increasingly interdisciplinary, collaborative, and pedagogically oriented. Thematic and conceptual analyses indicated growing scholarly attention toward instructional adaptation, critical thinking, ethical AI usage, and digital competence development within higher education institutions. These findings highlight the important role of universities in preparing educators and students for AI-integrated learning environments.\u003c/p\u003e \u003cp\u003eOverall, the study contributes to the growing body of knowledge on educational innovation and digital transformation by providing a comprehensive overview of the scientific development, thematic structures, and collaborative dynamics of AI literacy research. The findings may serve as a useful reference for researchers, educators, policymakers, and higher education institutions seeking to strengthen AI literacy and instructional innovation in the evolving landscape of higher education.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe author declares that there are no competing interests related to this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThis study utilized bibliographic data obtained from the Scopus database and did not involve human participants, personal data, or animal subjects. Therefore, ethical approval was not required.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for the conduct of this study.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eThe author solely conceptualized the study, conducted data collection and analysis, interpreted the findings, and prepared the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe bibliographic dataset analyzed during the current study was retrieved from the Scopus database and is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkgun, S., \u0026amp; Greenhow, C. (2022). 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AI and the future of education. \u003cem\u003ePolity Press\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eTlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., \u0026amp; Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. \u003cem\u003eSmart Learning Environments, 10\u003c/em\u003e(1), 15. https://doi.org/10.1186/s40561-023-00237-x\u003c/li\u003e\n\u003cli\u003eZawacki-Richter, O., Mar\u0026iacute;n, V. I., Bond, M., \u0026amp; Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. \u003cem\u003eInternational Journal of Educational Technology in Higher Education, 16\u003c/em\u003e(1), 39. https://doi.org/10.1186/s41239-019-0171-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bukidnon State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative artificial intelligence, AI literacy, higher education, bibliometric analysis, ChatGPT, instructional innovation","lastPublishedDoi":"10.21203/rs.3.rs-9614045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9614045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid emergence of generative artificial intelligence has significantly transformed the landscape of higher education, particularly in relation to AI literacy, digital competence, and instructional innovation. This study aimed to map the research landscape of generative artificial intelligence and AI literacy in higher education through a bibliometric analysis of global scholarly publications indexed in the Scopus database. Using a PRISMA-guided screening process, relevant publications published between 2020 and 2025 were retrieved, filtered, and analyzed using the Bibliometrix package and Biblioshiny interface in RStudio. A total of 584 peer-reviewed publications indexed in Scopus were analyzed using Bibliometrix and Biblioshiny in RStudio. The analysis examined annual scientific production, most relevant publication sources, country scientific production, international collaboration networks, keyword co-occurrence structures, and thematic development patterns.\u003c/p\u003e \u003cp\u003eThe findings revealed a substantial increase in scholarly production beginning in 2023, with publication growth accelerating rapidly following the widespread emergence of generative AI technologies such as ChatGPT. Educational technology journals emerged as the dominant publication outlets, highlighting the interdisciplinary integration of artificial intelligence, pedagogy, and digital learning research. The United States and China demonstrated the highest levels of scientific productivity and international collaboration, indicating their strong influence in shaping global AI literacy discourse. Keyword co-occurrence and thematic analyses identified artificial intelligence, AI literacy, higher education, and ChatGPT as central themes within the evolving research landscape. Emerging themes related to self-efficacy and critical thinking further suggested a growing shift toward human-centered and pedagogically oriented AI literacy frameworks.\u003c/p\u003e \u003cp\u003eThe study contributes to the growing body of knowledge on generative artificial intelligence in higher education by providing a comprehensive overview of research trends, thematic developments, and global scholarly collaboration patterns. The findings highlight the increasing institutional importance of AI literacy in preparing educators and learners for AI-integrated educational environments and underscore the need for future research focusing on ethical, pedagogical, and competency-based dimensions of generative artificial intelligence in higher education.\u003c/p\u003e","manuscriptTitle":"Mapping the Research Landscape of AI Literacy and Instructional Innovation in Higher Education: A Bibliometric Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 08:07:18","doi":"10.21203/rs.3.rs-9614045/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1aa28219-9f28-4a39-b914-8afc036e2979","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67524731,"name":"Artificial Intelligence and Machine Learning"},{"id":67524732,"name":"Educational Philosophy and Theory"},{"id":67524733,"name":"Behavioral Economics"}],"tags":[],"updatedAt":"2026-05-06T08:07:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 08:07:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9614045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9614045","identity":"rs-9614045","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}