Your browser does not fully support modern features. Please upgrade for a smoother experience.
Academic Performance in Nursing Education Through Digital Competencies and AI Integration: A Systematic Review: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Abigail Zou and Version 1 by Lorena Espina-Romero.

Digital transformation and artificial intelligence are reshaping nursing education by changing how students access information, complete academic tasks, and engage with technology-mediated learning. However, evidence on digital competencies, AI-related constructs, mediating mechanisms, and academic performance remains fragmented and methodologically uneven. This systematic review of empirical studies synthesized how digital competencies and AI-related constructs are associated with academic performance and learning-related outcomes in nursing education. Following PRISMA 2020 guidelines adapted to social science research, searches were conducted in Scopus and Web of Science Core Collection in March 2026, covering 2022–2026. Twenty-five empirical studies were included: 18 quantitative, 4 qualitative, and 3 mixed-methods studies. The evidence was concentrated in the Middle East and North Africa, Asia, and Europe. Findings suggest that digital competencies are associated with academic and learning-related outcomes mainly through self-efficacy, academic motivation, cognitive presence, and learning flow. AI-related evidence remains emerging, mixed, and context-dependent. Although some AI-assisted interventions reported favorable outcomes, one experimental study found greater knowledge gains with traditional text-based study than with ChatGPT-assisted learning. Therefore, AI integration should not be considered universally beneficial, but contingent on pedagogical design, task type, teacher guidance, AI literacy, responsible use, and critical verification.

  • AI literacy
  • technology-mediated learning
  • academic achievement
  • student readiness
  • educational innovation
Digital transformation (DT) has reshaped higher education by changing how students access information, interact with learning environments, complete academic tasks, and prepare for professional practice. Recent bibliometric evidence on AI-assisted teaching and AI integration in higher education also shows that this transformation is associated with pedagogical innovation, institutional policies, faculty training, ethical challenges, and curriculum redesign [1,2][1][2].
Within this broader transformation, nursing education is especially relevant because students must develop academic knowledge while learning to operate in technology-rich educational and professional contexts. Digital competencies (DCs) are no longer limited to the technical use of devices or platforms; they include information management, digital communication, ethical use of technology, digital content creation, online learning autonomy, and critical engagement with emerging tools.
Technology adoption in educational contexts also depends on perceived usefulness, digital readiness, organizational culture, and the development of digital competencies [3,4][3][4]. At the same time, artificial intelligence (AI), particularly generative AI, chatbots, virtual simulations, and AI-supported learning systems, has introduced new possibilities for academic support, feedback, simulation, and personalized learning.
Together, these changes position DCs and AI integration as central educational constructs for understanding academic performance (AP) in nursing education. Previous studies suggest that DCs are positively associated with several academic and learning-related outcomes among nursing students. Ibrahim and Aldawsari [5] reported that digital capabilities predicted AP and that self-efficacy partially mediated this relationship, showing that digital competence may influence performance through students’ beliefs in their own academic abilities.
In the same line, Ha and Choi [6] found that digital literacy improved self-reported academic achievement in blended learning, with cognitive presence acting as a significant mediator. Ryu et al. [7] extended this line of evidence by showing that digital literacy moderated the indirect relationship between learning presence, learning flow, and academic achievement in non-face-to-face nursing classes.
These findings suggest that DCs interact with motivational, cognitive, and self-regulatory mechanisms that shape students’ academic engagement and performance. The literature also shows that DCs are multidimensional, although studies differ in how they define and measure them.
For example, some studies use broad frameworks, such as the JISC Digital Capabilities Framework or DigComp-related models, to capture digital independent learning, information and data management, communication and collaboration, digital creation, and digital identity [5,8][5][8]. Other studies focus on digital literacy as the ability to search, evaluate, use, share, create, and communicate information through digital technologies [9,10][9][10].
For this reason, this review treats digital competence and digital literacy as related but not fully synonymous concepts. Digital literacy refers mainly to the ability to access, evaluate, create, and communicate information through digital technologies. Digital competence is used as a broader construct that also includes ethical judgment, collaboration, digital identity, content creation, autonomous learning, and responsible participation in digital environments. When studies used these terms differently, their original terminology was preserved, but the synthesis interpreted them within the broader category of DCs.
Recent empirical and conceptual literature incorporates AI literacy as a specific form of digital competence, emphasizing knowledge of AI concepts, ethical evaluation, responsible use, critical judgment, governance, and the ability to assess algorithmic outputs [11,12,13][11][12][13]. In parallel, curriculum-oriented literature has emphasized the need to embed AI literacy progressively in nursing education, particularly through critical evaluation, responsible use, and alignment with professional competencies [14]. However, curriculum framework contributions were used only as contextual references and were not analyzed as part of the final empirical corpus. This diversity reflects the conceptual richness of the field, but it also creates fragmentation because studies often examine different dimensions, instruments, and outcomes under similar labels.
Alongside this conceptual expansion, AI integration has further expanded the debate on AP in nursing education. Experimental and quasi-experimental studies show that AI-supported tools can improve learning-related outcomes, although findings are not fully consistent. Abdelwahab et al. [15] found that an AI chatbot improved theoretical knowledge, interpretation skills, clinical reasoning confidence, and academic motivation in electronic fetal monitoring education.
Similarly, Song et al. [16] reported that generative AI-assisted teaching improved higher-order thinking skills and AI literacy among undergraduate nursing students. Park and Kim [17] also found that an AI tutor-assisted simulation improved nursing knowledge, clinical performance, and digital literacy.
However, Mayor-Silva et al. [18] reported that traditional text-based study produced higher knowledge gains than ChatGPT-assisted learning in occupational risk prevention law. These mixed findings suggest that AI does not automatically improve AP; rather, its contribution may depend on instructional design, students’ digital and AI literacy, task type, and the level of teacher guidance.
Despite these advances, the mechanisms linking DCs, AI integration, and AP remain insufficiently organized. Several studies point to mediating variables such as self-efficacy, academic motivation, learning flow, cognitive presence, psychological empowerment, AI anxiety, and digital literacy [9,12,19,20][9][12][19][20]. These mechanisms suggest that DCs may influence AP indirectly by strengthening confidence, motivation, engagement, resilience, and effective participation in digital learning environments.
At the same time, the evidence remains dispersed across different designs, contexts, and theoretical traditions. Some studies rely on Social Cognitive Theory, Self-Determination Theory, Self-Regulated Learning Theory, Technology Acceptance Model, or learning presence frameworks, while others do not report an explicit theoretical foundation. This uneven theoretical development limits the ability to explain why, how, and under what conditions DCs contribute to AP.
In methodological terms, there are also limitations that justify a systematic review. Much of the evidence is cross-sectional, self-reported, and based on single institutions or convenience samples. Several studies report limited generalizability, small samples, short intervention periods, lack of longitudinal follow-up, and insufficient assessment of objective AP.
Furthermore, many studies focus on related outcomes such as motivation, self-efficacy, learning satisfaction, academic resilience, critical thinking, or higher-order thinking skills rather than direct AP indicators such as GPA, course grades, or standardized academic assessments. Therefore, a synthesis is needed that distinguishes direct AP indicators from related or proxy learning outcomes.
Although scoping reviews have examined generative AI in nursing education [21] and educational technology-based formative assessment [22], these reviews do not fully address the specific relationship among DCs, AI integration, mediating mechanisms, theoretical frameworks, and AP in nursing education. Hardie et al. [21] focused mainly on applications, attitudes, and ethical considerations of generative AI, while Shin et al. [22] examined educational technology for formative assessment. These contributions are valuable, but they do not organize the evidence around academic performance, direct and proxy learning outcomes, theoretical mechanisms, and the specific role of DCs and AI literacy. This gap justifies a systematic review that connects these constructs within a social science and nursing education perspective.
Accordingly, such a review can identify which dimensions of DCs are most strongly associated with AP, which mediating mechanisms explain this relationship, how AI functions as a learning support or moderating condition, and which theoretical frameworks are most frequently used. It can also reveal gaps in measurement, research design, context, and outcome assessment.
By organizing the evidence around constructs and relationships rather than isolated technologies, this review contributes to a more coherent understanding of AP in nursing education under conditions of DT and AI integration. This study aims to systematically review empirical evidence on the associations among DCs, AI integration, AP, and learning-related outcomes within nursing education.
The review also offers practical guidance for curriculum design, faculty development, and responsible AI integration in nursing education. Based on this purpose, the review addresses the following research questions:
RQ1.
How are DCs associated with AP in nursing education?
RQ2.
What dimensions of DCs are most frequently associated with AP in nursing students?
RQ3.
What mechanisms are reported in the relationship between DCs and AP?
RQ4.
How is AI associated with or positioned within this relationship?
RQ5.
What theoretical frameworks are used to interpret this relationship?
RQ6.
What methodological approaches dominate this research field?

References

  1. Nava Chirinos, Á.A. Artificial Intelligence in Higher Education: Strategic Foundations, Practices, and Success Factors from a Bibliometric Perspective. Ceniiac 2025, 1, e0009.
  2. Medina, R.H. AI-Assisted Teaching in Higher Education: Challenges and Opportunities. Ceniiac 2025, 1, e0003.
  3. Noroño Sánchez, J.G. How Entrepreneurs Perceive Technology in the Digital Era: From Aversion to Adoption. Ceniiac 2025, 1, e0002.
  4. Rangel Luzuriaga, E.W. A Moderated Model of Digital Transformation in SMEs: Digital Competencies, Digital HRM, and Organizational Culture. Ceniiac 2025, 1, e0010.
  5. Ibrahim, R.K.; Aldawsari, A.N. Relationship between Digital Capabilities and Academic Performance: The Mediating Effect of Self-Efficacy. BMC Nurs. 2023, 22, 434.
  6. Ha, J.H.; Choi, E.J. The Influence of Nursing Students’ Digital Literacy on Academic Achievement in a Blended Learning Environment: Parallel Multiple Mediation Effects of Learning Presence. J. Korean Acad. Soc. Nurs. Educ. 2025, 31, 452–463.
  7. Ryu, E.J.; Jang, K.S.; Kim, E.A. Influence of Learning Presence of Non-Face-to-Face Class Experience in Nursing Students on Academic Achievement: Mediating Effect of Learning Flow and Moderated Mediation of Digital Literacy. J. Korean Acad. Nurs. 2022, 52, 278.
  8. Martzoukou, K.; Luders, E.S.; Henriques, M.P.; Kamaruzaman, K.N. Co-Designing and Evaluating a Digital Competencies Toolkit for Nursing Students. J. Adv. Nurs. 2025, 82, 7541–7561.
  9. Agaoglu, F.O.; Bas, M.; Tarsuslu, S.; Ekinci, L.O.; Agaoglu, N.B. The Mediating Digital Literacy and the Moderating Role of Academic Support in the Relationship between Artificial Intelligence Usage and Creative Thinking in Nursing Students. BMC Nurs. 2025, 24, 484.
  10. Amin, S.M.; Atta, M.H.R.; Abd Elmoaty, A.E.E.; Ismail, H.A.; Kotp, M.H.; Basyouny, H.A.A.; Aly, M.A.; El-Sayed, A.A.I. Bridging Virtual and Real Learning: The Role of Digital Literacy and Metaverse Perspectives in Enhancing Academic Motivation in Nursing Education. BMC Nurs. 2025, 24, 1113.
  11. El-Sayed, B.K.M.; El-Sayed, A.A.I.; Alsenany, S.A.; Asal, M.G.R. The Role of Artificial Intelligence Literacy and Innovation Mindset in Shaping Nursing Students’ Career and Talent Self-Efficacy. Nurse Educ. Pract. 2025, 82, 104208.
  12. Chen, Y.; Lv, Z.; Li, X.; Chen, Y.; Li, W. Artificial Intelligence Literacy and Academic Resilience in Undergraduate Nursing Students: The Mediating Role of Self-Efficacy and Artificial Intelligence Anxiety. BMC Nurs. 2026, 25, 276.
  13. Ibrahim, F.M.; Shahrour, G.; Abuijlan, I.A.M.; Abdallah, A.M.; Alsuwayed, A.; Alanazi, A.R. NAIL-G: A Conceptual Best-Fit Framework for Nursing Ai Literacy and Governance Anchored in Socio-Technical Safety Science. BMC Nurs. 2026, 25, 370.
  14. Tomlinson, E.; Schoch, M.; McDonall, J. A Curriculum Framework for Embedding Artificial Intelligence Literacies in Pre-Registration Nursing Education. Nurse Educ. Today 2026, 158, 106928.
  15. Abdelwahab, A.M.T.; Aboraiah, M.I.H.; Elsayed, H.E.M. Effect of Using Artificial Intelligence Chatbot about Electronic Fetal Monitoring on Maternity Nursing Students’ Performance. BMC Med. Educ. 2025, 26, 120.
  16. Song, D.; Zhang, P.; Zhu, Y.; Qi, S.; Yang, Y.; Gong, L.; Zhou, L. Effects of Generative Artificial Intelligence on Higher-Order Thinking Skills and Artificial Intelligence Literacy in Nursing Undergraduates: A Quasi-Experimental Study. Nurse Educ. Pract. 2025, 88, 104549.
  17. Park, S.-A.; Kim, H.Y. Development and Effects of a Scenario-Based Labor Nursing Simulation Education Program Using an Artificial Intelligence Tutor: A Quasi-Experimental Study. Women’s Health Nurs. 2025, 31, 143–154.
  18. Mayor-Silva, L.I.; Moreno-Pimentel, A.G.; Hernández-Martín, M.M.; Moreno, G.; Maté-Muñoz, J.L.; Meneses-Monroy, A. Comparison between the Traditional Study Method and AI Use in the Analysis of an Occupational Risk Prevention Law in Nursing Students: An Experimental Study. Teach. Learn. Nurs. 2026, 21, e102–e108.
  19. Alshowkan, A.; Shdaifat, E. Impact of AI Literacy on Well-Being Among Nursing Students—Mediating Roles of Empowerment and Anxiety: Cross-Sectional Study. JMIR Nurs. 2025, 8, e79789.
  20. Amin, S.M.; El-fattah Mahgoub, S.A.; Tawfik, A.F.; Khalil, D.E.; El-Sayed, A.A.I.; Atta, M.H.R.; Albzia, A.; Morsy Mohamed, S.R. Nursing Education in the Digital Era: The Role of Digital Competence in Enhancing Academic Motivation and Lifelong Learning among Nursing Students. BMC Nurs. 2025, 24, 571.
  21. Hardie, P.; Darley, A.; Derwin, R.; Eustace-Cook, J.; Kearns, S.; Mc Brien, B.; Siddiquee, A.; Zheng, D.; Mooney, M. Applications, Attitudes and Ethical Considerations of Generative Artificial Intelligence (Gen AI) in Nursing Education: A Scoping Review. BMC Nurs. 2026, 25, 148.
  22. Shin, S.; Hong, E.; Lee, M.; Lee, M. Utilizing Educational Technology for Formative Assessment in Nursing Education: A Scoping Review. Korean J. Adult Nurs. 2026, 38, 1–12.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Academic Video Service
Feedback