Inteligencia artificial generativa, pensamiento crítico e integridad académica en la evaluación universitaria

Leopoldo Vinicio  Venegas Loor; Paola Yadira  Moreira Aguayo; Liliana Maribel  Figueroa Soledispa; Jefferson Ricardo  Álvarez Indacochea

doi:10.59169/pentaciencias.v8i3.1891

Authors

Leopoldo Vinicio Venegas Loor Universidad Estatal del Sur de Manabí https://orcid.org/0000-0002-3100-6320
Paola Yadira Moreira Aguayo Universidad Estatal del Sur de Manabí https://orcid.org/0000-0002-6380-2794
Liliana Maribel Figueroa Soledispa Universidad Estatal del Sur de Manabí https://orcid.org/0000-0001-9785-2581
Jefferson Ricardo Álvarez Indacochea Universidad Estatal del Sur de Manabí https://orcid.org/0009-0006-3456-5589

DOI:

https://doi.org/10.59169/pentaciencias.v8i3.1891

Keywords:

generative artificial intelligence; higher education; assessment; critical thinking; academic integrity

Abstract

The expansion of generative artificial intelligence has changed the conditions under which universities interpret authorship, learning and assessment evidence. This article critically analyses its implications for critical thinking, academic integrity and assessment in higher education and proposes criteria for responsible integration. A critical and integrative documentary review of scientific literature and institutional documents published mainly between 2021 and 2026 was conducted. Findings show that educational effects depend on task design, teacher and student literacy, transparency of use and the ability to verify the intellectual process. Strategies focused exclusively on prohibition and detection are insufficient. As a conceptual contribution, the EVALUAR-AI Framework is proposed, consisting of seven dimensions: evidence of process, verification, demonstrable authorship, critical reading, ethical use, reflective learning and human responsibility. University assessment should therefore move beyond the final product toward comprehension, justification, verification and traceability of reasoning.

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References

Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 38. https://doi.org/10.1186/s41239-023-00408-3

Cong-Lem, N., & Thuy-Dung, N. T. (2026). Towards a framework for understanding and assessing critical thinking in generative AI-enhanced learning environments: A scoping review. European Journal of Psychology of Education, 41(2), Article 41. https://doi.org/10.1007/s10212-026-01089-y

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Farrokhnia, M., Banihashemi, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846

Helal, M. Y. I., Elgendy, I. A., Albashrawi, M. A., Dwivedi, Y. K., Al-Ahmadi, M. S., & Jeon, I. (2025). The impact of generative AI on critical thinking skills: A systematic review, conceptual framework and future research directions. Information Discovery and Delivery. https://doi.org/10.1108/IDD-05-2025-0125

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410

Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391105

Moorhouse, B. L., Yeo, M. A., & Wan, Y. W. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5, 100151. https://doi.org/10.1016/j.caeo.2023.100151

Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487

Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559–614. https://doi.org/10.1080/03043797.2023.2213169

Overono, A. L., & Ditta, A. S. (2023). The rise of artificial intelligence: A clarion call for higher education to redefine learning and reimagine assessment. College Teaching, 73(2), 123–126. https://doi.org/10.1080/87567555.2023.2233653

Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), Article 7. https://doi.org/10.53761/1.20.02.07

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 342–363. https://doi.org/10.37074/jalt.2023.6.1.9

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x

Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21, 40. https://doi.org/10.1186/s41239-024-00468-z