Research Article

Does generative artificial intelligence transform university learning? A correlational meta-analysis of educational outcomes

Alejandro Valencia-Arias 1 * , Erick Oswaldo Salazar Montoya 2 , Lelis Grabiel Palacios Silva 3 , Gustavo Adolfo Moreno López 4 , Sebastián Arias García 5 , Paula Andrea Rodríguez-Correa 5 , Wilmer Londoño-Celis 6
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1 Escuela de Ingeniería Industrial, Universidad Señor de Sipán, Chiclayo, PERU2 Ciencias Económicas, Universidad Señor de Sipán, Chiclayo, PERU3 Ciencias de la Salud, Universidad Señor de Sipán, Chiclayo, PERU4 Institución Universitaria Marco Fidel Suárez, Bello, COLOMBIA5 Facultad de Ciencias Económicas y Administrativas, Instituto Tecnológico Metropolitano, Antioquia, COLOMBIA6 Facultad de Humanidades y Ciencias Sociales, Corporación Universitaria Americana, Medellín, COLOMBIA* Corresponding Author
Online Journal of Communication and Media Technologies, 16(2), April 2026, e202634, https://doi.org/10.30935/ojcmt/18593
Published: 23 May 2026
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ABSTRACT

Generative artificial intelligence (GenAI) refers to systems capable of producing original content from large volumes of data and deep learning models. There has been an increasing adoption of GenAI in higher education as cognitive support, a tool for academic production, and a resource for personalized learning. However, the extant empirical evidence demonstrates a lack of consensus and, in certain instances, a paucity of concordance, impeding a holistic comprehension of its educational impact. In this context, the objective of this research is to estimate the overall relationship between GenAI and university educational outcomes through a correlational meta-analysis. The study employs a quantitative approach and is conducted in accordance with the PRISMA 2020 guidelines as an international reporting standard. This ensures transparency, reproducibility, and rigor in the identification, selection, evaluation, and synthesis of evidence. The findings confirm that GenAI constitutes a relevant educational phenomenon whose impact cannot be interpreted from simplistic or deterministic perspectives. Whilst the relationship with educational outcomes is consistent and methodologically stable, it is strongly influenced by context, pedagogical decisions and usage patterns. The absence of homogeneous patterns that would permit direct generalizations is a salient finding, underscoring the necessity for nuanced and critical analyses. This demonstrates that the educational transformation associated with GenAI is contingent not on the technology itself, but rather on its pedagogical and institutional integration.
Keywords: generative artificial intelligence, university learning, educational outcomes, correlational meta-analysis, higher education

CITATION (APA)

Valencia-Arias, A., Salazar Montoya, E. O., Palacios Silva, L. G., Moreno López, G. A., Arias García, S., Rodríguez-Correa, P. A., & Londoño-Celis, W. (2026). Does generative artificial intelligence transform university learning? A correlational meta-analysis of educational outcomes. Online Journal of Communication and Media Technologies, 16(2), e202634. https://doi.org/10.30935/ojcmt/18593

REFERENCES

  1. Agus, A. R. R. (2022). Meta-analysis: Correlation study between science process skills and learning outcomes. Journal of Digital Learning and Education, 2(3), 196-202. https://doi.org/10.52562/jdle.v2i3.427
  2. Alotaibi, N. (2026). Faculty acceptance of generative AI in higher education: A meta-analysis of TAM and UTAUT studies (2021-2025). International Journal of Higher Education, 15(1), 1-14. https://doi.org/10.5430/ijhe.v15n1p1
  3. Ardito, C. G. (2025). Generative AI detection in higher education assessments. New Directions for Teaching and Learning, 2025(182), 11-28. https://doi.org/10.1002/tl.20624
  4. Bamasoud, D. M., Mohammad, R., & Bilal, S. (2025). Adopting generative AI in higher education: A dual-perspective study of students and lecturers in Saudi universities. Big Data and Cognitive Computing, 9(10), Article 264. https://doi.org/10.3390/bdcc9100264
  5. Beauvais, A. M., Stewart, J. G., DeNisco, S., & Beauvais, J. E. (2014). Factors related to academic success among nursing students: A descriptive correlational research study. Nurse Education Today, 34(6), 918-923. https://doi.org/10.1016/j.nedt.2013.12.005
  6. Cacho, R. M. (2024). Integrating generative AI in university teaching and learning: A model for balanced guidelines. Online Learning, 28(3), 55-81. https://doi.org/10.24059/olj.v28i3.4508
  7. Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: The sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 44(9), 7991-8000. https://doi.org/10.1007/s12144-025-07433-7
  8. Chow, J. C., & Wehby, J. H. (2018). Associations between language and problem behavior: A systematic review and correlational meta-analysis. Educational Psychology Review, 30(1), 61-82. https://doi.org/10.1007/s10648-016-9385-z
  9. Culbert, J. H., Hobert, A., Jahn, N., Haupka, N., Schmidt, M., Donner, P., & Mayr, P. (2025). Reference coverage analysis of OpenAlex compared to Web of Science and Scopus. Scientometrics, 130(4), 2475-2492. https://doi.org/10.1007/s11192-025-05293-3
  10. Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11, Article 1006. https://doi.org/10.1057/s41599-024-03526-z
  11. de Fine Licht, K. (2024). Generative artificial intelligence in higher education: Why the ‘banning approach’ to student use is sometimes morally justified. Philosophy & Technology, 37, Article 113. https://doi.org/10.1007/s13347-024-00799-9
  12. Driessens, O., & Pischetola, M. (2024). Danish university policies on generative AI: Problems, assumptions and sustainability blind spots. MedieKultur: Journal of Media and Communication Research, 40(76), 31-52. https://doi.org/10.7146/mk.v40i76.143595
  13. Ermold, C. M. (2011). A correlational study of student achievement and school climate [PhD thesis, Walden University]. https://www.proquest.com/openview/7b43afb7abee4c171aa0ae0e2a373355/1?pq-origsite=gscholar&cbl=18750
  14. Fern, J. (2024). A more-than-human ecology: Evolving generative artificial intelligence in higher education. Education Sciences, 14(10), Article 1102. https://doi.org/10.3390/educsci14101102
  15. Haider, S. Z., & Hussain, A. (2014). Relationship between teacher factors and student achievement: A correlational study of secondary schools. US-China Education Review A, 4(7), 465-480. https://www.davidpublisher.com/Public/uploads/Contribute/551233e421b66.pdf
  16. Haroud, S., & Saqri, N. (2025). Generative AI in higher education: Teachers’ and students’ perspectives on support, replacement, and digital literacy. Education Sciences, 15(4), Article 396. https://doi.org/10.3390/educsci15040396
  17. Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., & Dwivedi, Y. K. (2025). Reimagining higher education: Navigating the challenges of generative AI adoption. Information Systems Frontiers. https://doi.org/10.1007/s10796-025-10582-6
  18. Ilieva, G., Yankova, T., Ruseva, M., & Kabaivanov, S. (2025). A framework for generative AI-driven assessment in higher education. Information, 16(6), Article 472. https://doi.org/10.3390/info16060472
  19. Iqbal, S., Akram, M., & Mushtaq, I. (2021). Relationship between stress and educational performance of university students: A correlational research study. Review of Education, Administration & Law, 4(4), 805-811. https://doi.org/10.47067/real.v4i4.198
  20. Ishak, Okilanda, A., Permadi, A. A., Tjahyanto, T., Prabowo, T. A., Rozi, M. F., Suganda, M. A., & Suryadi, D. (2023). Correlational study: Sports Students’ special test results and basic athletic training learning outcomes. Retos, 49, 519–524. https://doi.org/10.47197/retos.v49.98820
  21. Jensen, L. X., Buhl, A., Sharma, A., & Bearman, M. (2025). Generative AI and higher education: A review of claims from the first months of ChatGPT. Higher Education, 89(4), 1145-1161. https://doi.org/10.1007/s10734-024-01265-3
  22. Jeynes, W. H. (2019). A meta-analysis on the relationship between character education and student achievement and behavioral outcomes. Education and Urban Society, 51(1), 33-71. https://doi.org/10.1177/0013124517747681
  23. Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024). University teachers’ views on the adoption and integration of generative AI tools for student assessment in higher education. Education Sciences, 14(10), Article 1090. https://doi.org/10.3390/educsci14101090
  24. Korpershoek, H., Canrinus, E. T., Fokkens-Bruinsma, M., & De Boer, H. (2020). The relationships between school belonging and students’ motivational, social-emotional, behavioural, and academic outcomes in secondary education: A meta-analytic review. Research Papers in Education, 35(6), 641-680. https://doi.org/10.1080/02671522.2019.1615116
  25. Lee, S. S., & Moore, R. L. (2024). Harnessing generative AI (GenAI) for automated feedback in higher education: A systematic review. Online Learning, 28(3), 82-106. https://doi.org/10.24059/olj.v28i3.4593
  26. Lei, H., Cui, Y., & Zhou, W. (2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: An International Journal, 46(3), 517-528. https://doi.org/10.2224/sbp.7054
  27. Ma, X., Shen, J., Krenn, H. Y., Hu, S., & Yuan, J. (2016). A meta-analysis of the relationship between learning outcomes and parental involvement during early childhood education and early elementary education. Educational Psychology Review, 28(4), 771-801. https://doi.org/10.1007/s10648-015-9351-1
  28. Meakin, L. (2024). Exploring the impact of generative artificial intelligence on higher education students’ utilization of library resources: A critical examination. Information Technology and Libraries, 43(3), 1-13. https://doi.org/10.5860/ital.v43i3.17246
  29. O’Dea, X., Tsz Kit Ng, D., O’Dea, M., & Shkuratskyy, V. (2026). Factors affecting university students’ generative AI literacy: Evidence and evaluation in the UK and Hong Kong contexts. Policy Futures in Education, 24(1), 13-34. https://doi.org/10.1177/14782103241287401
  30. Oc, Y., Gonsalves, C., & Quamina, L. T. (2025). Generative AI in higher education assessments: Examining risk and tech-savviness on student’s adoption. Journal of Marketing Education, 47(2), 138-155. https://doi.org/10.1177/02734753241302459
  31. Orhan, A. (2022). The relationship between critical thinking and academic achievement: A meta-analysis study. Psycho-Educational Research Reviews, 11(1), 283-299. https://doi.org/10.52963/PERR_Biruni_V11.N1.18
  32. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
  33. Perezchica-Vega, J. E., Sepúlveda-Rodríguez, J. A., & Román-Méndez, A. D. (2024). Inteligencia artificial generativa en la educación superior: Usos y opiniones de los profesores [Generative artificial intelligence in higher education: Uses and opinions of professors]. European Public & Social Innovation Review, 9, 1-20. https://doi.org/10.31637/epsir-2024-593
  34. Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69, 1105-1120. https://doi.org/10.1007/s11528-025-01100-1
  35. Reicher, H., Frenkel, Y., Lavi, M. J., Nasser, R., Ran-milo, Y., Sheinin, R., Shtaif, M., & Milo, T. (2025). A generative AI-empowered digital tutor for higher education courses. Information, 16(4), Article 264. https://doi.org/10.3390/info16040264
  36. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387. https://doi.org/10.1037/a0026838
  37. Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 358, Article j4008. https://doi.org/10.1136/bmj.j4008
  38. Smith, S. M., Tate, M., Freeman, K., Walsh, A., Ballsun-Stanton, B., & Lane, M. (2026). A university framework for the responsible use of generative AI in research. Journal of Higher Education Policy and Management, 48(1), 17-36. https://doi.org/10.1080/1360080X.2025.2509187
  39. Tbaishat, D., Amoudi, G., & Elfadel, M. (2025). Adapting teaching and learning with existing generative AI by higher education students: Comparative study of Zayed University and King Abdulaziz University. Computers and Education: Artificial Intelligence, 8, Article 100421. https://doi.org/10.1016/j.caeai.2025.100421
  40. Tong, A., Zainol, Z., Chong, T. S., & Renganathan, K. (2025). AI governance on young consumers in higher education: A content analysis of policies for generative AI. Young Consumers: Insight and Ideas for Responsible Marketers, 26(5), 865-885. https://doi.org/10.1108/YC-10-2024-2303
  41. Waluyo, B., & Kusumastuti, S. (2024). Generative AI in student English learning in Thai higher education: More engagement, better outcomes? Social Sciences & Humanities Open, 10, Article 101146. https://doi.org/10.1016/j.ssaho.2024.101146
  42. Weng, X., Qi, X. I. A., Gu, M., Rajaram, K., & Chiu, T. K. (2024). Assessment and learning outcomes for generative AI in higher education: A scoping review on current research status and trends. Australasian Journal of Educational Technology, 40(6), 37-55. https://doi.org/10.14742/ajet.9540
  43. Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, Article 21. https://doi.org/10.1186/s41239-024-00453-6