Inteligencia artificial generativa para la enseñanza de matemáticas y programación en educación superior: Revisión sistemática

• Víctor Alfonso Erazo-Arteaga https://orcid.org/0000-0001-5915-1864
• Santiago Marcelo Vacas Palacios https://orcid.org/0000-0003-3304-3843
• Marcelo Bayardo Cisneros Rúales https://orcid.org/0000-0002-8818-6502

DOI: https://doi.org/10.31876/rcs.v32i2.45578

Abstract

La rápida expansión de la inteligencia artificial generativa ha despertado un creciente interés en su aplicación educativa, especialmente en áreas que exigen razonamiento lógico y abstracción. El objetivo de esta revisión sistemática fue analizar el impacto de la inteligencia artificial generativa en el aprendizaje de estudiantes universitarios, así como en la preparación de clases por parte de los docentes. Se aplicaron las directrices PRISMA, definiendo criterios de inclusión y exclusión con base a preguntas PICO y PCC. La búsqueda en Scopus, Web of Science e IEEE Xplore permitió analizar 710 registros, de los cuales 67 cumplieron con los criterios establecidos. Los resultados muestran una literatura dominada por estudios descriptivos y con predominio de ChatGPT, donde se reportan beneficios en motivación y productividad, aunque sin evidencias concluyentes de mejoras en el rendimiento académico. La investigación se concentra en pocos países con ausencia de producción latinoamericana. En conclusión, el uso de inteligencia artificial generativa ofrece un potencial pedagógico prometedor, pero su efectividad depende de la integración crítica con la labor docente y de la generación de estudios experimentales más robustos que permitan establecer evidencias sólidas y generalizables.

References

Abdulla, S., Ismail, S., Fawzy, Y., y Elhaj, A. (2024). Using ChatGPT in Teaching Computer Programming and Studying its Impact on Students Performance. Electronic Journal of E-Learning, 22(6), 66-81. https://doi.org/10.34190/EJEL.22.6.3380

Adamopoulou, E., y Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006

Al Naqbi, H., Bahroun, Z., y Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166

Azaiz, I., Deckarm, O., y Strickroth, S. (2023). AI-Enhanced auto-correction of programming exercises: How effective is GPT-3.5? International Journal of Engineering Pedagogy, 13(8), 67-83. https://doi.org/10.3991/ijep.v13i8.45621

Azaiz, I., Kiesler, N., y Strickroth, S. (2024). Feedback-Generation for programming exercises with GPT-4. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1, 31-37). https://doi.org/10.1145/3649217.3653594

Barker, T. H., Stone, J. C., Sears, K., Klugar, M., Leonardi-Bee, J., Tufanaru, C., Aromataris, E., y Munn, Z. (2022). Revising the JBI quantitative critical appraisal tools to improve their applicability: An overview of methods and the development process. JBI Evidence Synthesis, 21(3), 478-493. https://doi.org/10.11124/JBIES-22-00125

Berrezueta-Guzman, J., y Krusche, S. (2023). Recommendations to Create Programming Exercises to Overcome ChatGPT. IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T), Tokyo, Japan, 2023, pp. 147-151. https://doi.org/10.1109/CSEET58097.2023.00031

Bikanga, M. (2024). It helps with crap lecturers and their low effort: Investigating computer science students’ perceptions of using ChatGPT for learning. Education Sciences, 14(10), 1106. https://doi.org/10.3390/educsci14101106

Birillo, A., Artser, E., Potriasaeva, A., Vlasov, I., Dzialets, K., Golubev, Y., Gerasimov, I., Keuning, H., y Bryksin, T. (2024). One step at a time: Combining LLMs and static analysis to generate next-step hints for programming tasks. Proceedings of the 24th Koli Calling International Conference on Computing Education Research, 9. https://doi.org/10.1145/3699538.3699556

Budhiraja, R., Joshi, I., Challa, J. S., Akolekar, H. D., y Kumar, D. (2024). “It’s not like Jarvis, but it’s pretty close!” - Examining ChatGPT’s usage among undergraduate students in computer science. ACM International Conference Proceeding Series, pp. 124-133. https://doi.org/10.1145/3636243.3636257

Carrasco-Barrionuevo, J. W., Allauca-Pancho, F. R., Mantilla-Cabrera, C. E., y Santillán-Lima, J. C. (2024). Estrategia didáctica para el aprendizaje de cálculo en estudiantes universitarios con el software Derive. Revista de Ciencias Sociales (Ve), XXX(E-10), 222-242. https://doi.org/10.31876/rcs.v30i.42840

Cipriano, B. P., y Alves, P. (2023). GPT-3 vs Object Oriented Programming Assignments: An experience report. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1, 61-67. https://doi.org/10.1145/3587102.3588814

Cipriano, B. P., y Alves, P. (2024). LLMs Still Can’t Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard’s Capacity to Handle Object-Oriented Programming Assignments. Proceedings - International Conference on Software Engineering, 162-169. https://doi.org/10.1145/3639474.3640052

Cochrane Methods Group (2024). The Risk of Bias In Non-randomized Studies – of Interventions, Version 2 (ROBINS-I V2) assessment tool (for follow-up studies). ROBINS-I Development Group. https://www.riskofbias.info/welcome/robins-i-v2

Cristofaro, M., y Giardino, P. L. (2025). Surfing the AI waves: the historical evolution of artificial intelligence in management and organizational studies and practices. Journal of Management History. https://doi.org/10.1108/JMH-01-2025-0002

Dasari, D., Hendriyanto, A., Sahara, S., Suryadi, D., Muhaimin, L. H., Chao, T., y Fitriana, L. (2023). ChatGPT in didactical tetrahedron, does it make an exception? A case study in mathematics teaching and learning. Frontiers in Education, 8, 1295413. https://doi.org/10.3389/feduc.2023.1295413

Da Silva, C. A. G., Ramos, F. N., De Moraes, R. V., y Dos Santos, E. L. (2024). ChatGPT: Challenges and benefits in software programming for higher education. Sustainability, 16(3), 1245. https://doi.org/10.3390/su16031245

Delima, N., Kusuma, D. A., y Paulus, E. (2024). The students’ mathematics self-regulated learning and mathematics anxiety based on the use of ChatGPT, music, study program, and academic achievement. Infinity Journal, 13(2), 349-362. https://doi.org/10.22460/infinity.v13i2.p349-362

Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., y Reeves, B. N. (2024). Prompt Problems: A New Programming Exercise for the Generative AI Era. SIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 1, 296-302. https://doi.org/10.1145/3626252.3630909

Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A., y Sarsa, S. (2024). Computing Education in the Era of Generative AI. Communications of the ACM, 67(2), 56-67. https://doi.org/10.1145/3624720

Dilling, F., y Herrmann, M. (2024). Using large language models to support pre-service teachers mathematical reasoning—an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry. Frontiers in Artificial Intelligence, 7, 1460337. https://doi.org/10.3389/frai.2024.1460337

Dingle, A., y Krulis, M. (2024). Tackling students’ coding assignments with LLMs. Proceedings - 2024 International Workshop on Large Language Models for Code, LLM4Code 2024, 94-101. https://doi.org/10.1145/3643795.3648389

Doughty, J., Wan, Z., Bompelli, A., Qayum, J., Wang, T., Zhang, J., Zheng, Y., Doyle, A., Sridhar, P., Agarwal, A., Bogart, C., Keylor, E., Kultur, C., Savelka, J., y Sakr, M. (2024). A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education. ACM International Conference Proceeding Series, 114-123. https://doi.org/10.1145/3636243.3636256

Dunder, N., Lundborg, S., Wong, J., y Viberg, O. (2024). Kattis vs ChatGPT: Assessment and evaluation of programming tasks in the age of artificial intelligence. ACM International Conference Proceeding Series, 821-827. https://doi.org/10.1145/3636555.3636882

Espina-Romero Romero, L. C. (2022). Procesos de Enseñanza-Aprendizaje Virtual durante la COVID-19: Una revisión bibliométrica. Revista de Ciencias Sociales (Ve), XXVIII(3), 345-361. https://doi.org/10.31876/rcs.v28i3.38479

Frankford, E., Sauerwein, C., Bassner, P., Krusche, S., y Breu, R. (2024). AI-Tutoring in software engineering education experiences with large language models in programming assessments. Proceedings - International Conference on Software Engineering, 309-319. https://doi.org/10.1145/3639474.3640061

Galindo, A. (2023). Integración de la inteligencia artificial en la enseñanza de las artes plásticas. Revista de Ciencias Sociales (Ve), XXIX(4), 17-29. https://doi.org/10.31876/rcs.v29i4.41256

Getenet, S. (2024). Pre-service teachers and ChatGPT in multistrategy problem-solving: Implications for mathematics teaching in primary schools. International Electronic Journal of Mathematics Education, 19(1), em0766. https://doi.org/10.29333/iejme/14141

González, G. A., Martínez, L. E., Muegues, W. S., y Verdecía, L. J. (2025). Inteligencia Artificial y su impacto sobre la gerencia estratégica y la cultura investigativa. Revista de Ciencias Sociales (Ve), XXXI(E-11), 312-322. https://doi.org/10.31876/rcs.v31i.44003

Gouia-Zarrad, R., y Gunn, C. (2024). Enhancing students’ learning experience in mathematics class through ChatGPT. International Electronic Journal of Mathematics Education, 19(3), em0781. https://doi.org/10.29333/iejme/14614

Haindl, P., y Weinberger, G. (2024). Students’ experiences of using ChatGPT in an undergraduate programming course. IEEE Access, 12, 43519-43529. https://doi.org/10.1109/ACCESS.2024.3380909

He, Z., Nguyen, T., Miari, T., Aliasgari, M., Rafatirad, S., y Sayadi, H. (2024). The AI Companion in Education: Analyzing the Pedagogical Potential of ChatGPT in Computer Science and Engineering. IEEE Global Engineering Education Conference, EDUCON, Kos Island, Greece, 1-10. https://doi.org/10.1109/EDUCON60312.2024.10578820

Hou, I., Mettille, S., Man, O., Li, Z., Zastudil, C., y MacNeil, S. (2024). The effects of Generative AI on computing students’ help-seeking preferences. ACM International Conference Proceeding Series, 39-48. https://doi.org/10.1145/3636243.3636248

Hou, X., Wu, Z., Wang, X., y Ericson, B. J. (2024). CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming. L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale, 51-62. https://doi.org/10.1145/3657604.3662032

Humble, N., Boustedt, J., Holmgren, H., Milutinovic, G., Seipel, S., y Östberg, A.-S. (2023). Cheaters or AI-Enhanced Learners: Consequences of ChatGPT for Programming Education. Electronic Journal of E-Learning, 22(2), 16-29. https://doi.org/10.34190/ejel.21.5.3154

Husain, A. J. A. (2024). Potentials of ChatGPT in computer programming: insights from programming instructors. Journal of Information Technology Education: Research, 23, 2. https://doi.org/10.28945/5240

Jacobs, S., y Jaschke, S. (2024). Evaluating the Application of Large Language Models to Generate Feedback in Programming Education. IEEE Global Engineering Education Conference, EDUCON, Kos Island, Greece, 1-5. https://doi.org/10.1109/EDUCON60312.2024.10578838

Jin, J., y Kim, M. (2023). GPT-Empowered Personalized eLearning System for Programming Languages. Applied Sciences (Switzerland), 13(23), 12773. https://doi.org/10.3390/app132312773

Jordan, M., Ly, K., y Raj, A. G. S. (2024). Need a Programming Exercise Generated in Your Native Language? ChatGPT’s Got Your Back: Automatic Generation of Non-English Programming Exercises Using OpenAI GPT-3.5. SIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 1, 618-624. https://doi.org/10.1145/3626252.3630897

Joshi, I., Budhiraja, R., Dev, H., Kadia, J., Ataullah, M. O., Mitra, S., Akolekar, H. D., y Kumar, D. (2024). ChatGPT in the classroom: An analysis of its strengths and weaknesses for solving undergraduate computer science questions. SIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 1, 625-631. https://doi.org/10.1145/3626252.3630803

Jošt, G., Taneski, V., y Karakatič, S. (2024). The Impact of Large Language Models on Programming Education and Student Learning Outcomes. Applied Sciences, 14(10), 4115. https://doi.org/10.3390/app14104115

Karjanto, N. (2023). Investigating difficulties and enhancing understanding in linear algebra: Leveraging SageMath and ChatGPT for (orthogonal) diagonalization and singular value decomposition. Mathematical Biosciences and Engineering, 20(9), 16551-16595. https://doi.org/10.3934/mbe.2023738

Kazemitabaar, M., Ye, R., Wang, X., Henley, A. Z., Denny, P., Craig, M., y Grossman, T. (May 11, 2024). CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs. Conference on Human Factors in Computing Systems – Proceedings, 650. https://doi.org/10.1145/3613904.3642773

Kimmel, B., L, A., Yaro, L., Gipson, B., Taylor, R., Osae-Asante, S. K., Vaught, H., Wininger, G., y Yamaguchi, C. (May 11, 2024). Enhancing Programming Error Messages in Real Time with Generative AI. Conference on Human Factors in Computing Systems – Proceedings, 608. https://doi.org/10.1145/3613905.3647967

Kosar, T., Ostojić, D., Liu, Y. D., y Mernik, M. (2024). Computer science education in ChatGPT era: Experiences from an experiment in a programming course for novice programmers. Mathematics, 12(5), 629. https://doi.org/10.3390/math12050629

Lee, J. Y. (2023). Can an artificial intelligence chatbot be the author of a scholarly article? Science Editing, 10(1), 7-12. https://doi.org/10.6087/kcse.292

Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., y Xie, T. (2024). Scaffolding computational thinking with ChatGPT. IEEE Transactions on Learning Technologies, 17, 1668-1682. https://doi.org/10.1109/TLT.2024.3392896

Liffiton, M., Sheese, B. E., Savelka, J., y Denny, P. (November 13, 2023). CodeHelp: Using large language models with guardrails for scalable support in programming classes. ACM International Conference Proceeding Series, 8. https://doi.org/10.1145/3631802.3631830

Logacheva, E., Hellas, A., Prather, J., Sarsa, S., y Leinonen, J. (2024). Evaluating Contextually Personalized Programming Exercises Created with Generative AI. ICER 2024 - ACM Conference on International Computing Education Research, 1, 95-113. https://doi.org/10.1145/3632620.3671103

López-Fernández, D., y Vergaz, R. (2024). Adoption and impact of ChatGPT in computer science education: A case study on a database administration course. AI, 5(4), 2321-2337. https://doi.org/10.3390/ai5040114

MacNeil, S., Denny, P., Tran, A., Leinonen, J., Bernstein, S., Hellas, A., Sarsa, S., y Kim, J. (2024). Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models. ACM International Conference Proceeding Series, 11-18. https://doi.org/10.1145/3636243.3636245

Marín-González Cristina, F., Cuba-Romero, G. C., y Larios-Ariza, M. A. (2025). Artificial intelligence applied to the management of dyslexia cases in university settings: A systematic review. Revista de Ciencias Sociales (Ve), XXXI(3), 38-56. https://doi.org/10.31876/rcs.v31i3.44268

Meissner, R., Pögelt, A., Ihsberner, K., Grüttmüller, M., Tornack, S., Thor, A., Pengel, N., Wollersheim, H.-W., y Hardt, W. (2024). LLM-generated competence-based e-assessment items for higher education mathematics: methodology and evaluation. Frontiers in Education, 9, 1427502. https://doi.org/10.3389/feduc.2024.1427502

Mendonça, N. C. (2024). Evaluating ChatGPT-4 Vision on Brazil’s National Undergraduate Computer Science Exam. ACM Transaction on Computing Education, 24(3), 37. https://doi.org/10.1145/3674149

Montella, R., De Vita, C. G., Mellone, G., Ciricillo, T., Caramiello, D., Di Luccio, D., Kosta, S., Damaševičius, R., Maskeliūnas, R., Queirós, R., y Swacha, J. (2024). Leveraging large language models to support authoring gamified programming exercises. Applied Sciences, 14(18), 8344. https://doi.org/10.3390/app14188344

Morales, G. P., Quispe, A., Guía, T., y Quispe, S. M. T. (2025). Realidad Aumentada y aprendizaje interactivo en estudiantes universitarios de informática. Revista de Ciencias Sociales (Ve), XXXI(E-11), 436-444. https://doi.org/10.31876/rcs.v31i.44011

Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, López-Pernas, S., y Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learning Environments, 11, 59. https://doi.org/10.1186/s40561-024-00350-5

Neumann, A. T., Yin, Y., Sowe, S., Decker, S., y Jarke, M. (2025). An LLM-Driven Chatbot in Higher Education for databases and information systems. IEEE Transactions on Education, 68(1), 103-116. https://doi.org/10.1109/TE.2024.3467912

Ouh, E. L., Gan, B. K. S., Shim, K. J., y Wlodkowski, S. (2023). ChatGPT, can you generate solutions for my coding exercises? An evaluation on its effectiveness in an undergraduate java programming course. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1, 54-60. https://doi.org/10.1145/3587102.3588794

Pabreja, K., y Pabreja, N. (2024). Understanding college students’ satisfaction with ChatGPT: An exploratory and predictive machine learning approach using feature engineering. MIER Journal of Educational Studies Trends & Practices, 14(1), 37-63. https://doi.org/10.52634/mier/2024/v14/i1/2568

Padiyath, A., Hou, X., Pang, A., Viramontes, D., Gu, X., Nelson-Fromm, T., Wu, Z., Guzdial, M., y Ericson, B. (2024). Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course. Proceedings of the 2024 ACM Conference on International Computing Education Research, 1, 114-130. https://doi.org/10.1145/3632620.3671098

Pankiewicz, M., y Baker, R. S. (2024). Navigating Compiler Errors with AI Assistance-A Study of GPT Hints in an Introductory Programming Course. ITiCSE 2024: Proceedings of the 2024 on Innovation and Technology in Computer Science Education, 1, 94-100. https://doi.org/10.1145/3649217.3653608

Perezchica-Vega, J. E., Sepúlveda-Rodríguez, J. A., y Román-Méndez, A. D. (2024). Generative artificial intelligence in higher education: uses and opinions of teachers. European Public & Social Innovation Review, 9, 1-20. https://doi.org/10.31637/epsir-2024-593

Phung, T., Pǎdurean, V.-A., Singh, A., Brooks, C., Cambronero, J., Gulwani, S., Singla, A., y Soares, G. (2024). Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 tutor model for hint generation and GPT-3.5 student model for hint validation. ACM International Conference Proceeding Series, 12-23. https://doi.org/10.1145/3636555.3636846

Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., MacNeil, S., Petersen, A., Pettit, R., Reeves, B. N., y Savelka, J. (2023). The Robots are Here: Navigating the Generative AI Revolution in Computing Education. ITiCSE-WGR 2023 - Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, 108-159. https://doi.org/10.1145/3623762.3633499

Quiñones, A. J., y Huiman, H. E. (2022). Resolución de problemas con el método matemático de Polya: La aventura de aprender. Revista de Ciencias Sociales (Ve), XXVIII(E-5), 75-86. https://doi.org/10.31876/rcs.v28i.38146

Rajala, J., Hukkanen, J., Hartikainen, M., y Niemelä, P. (2023). “Call me Kiran” ChatGPT as a tutoring Chatbot in a computer science course. ACM International Conference Proceeding Series: Mindtrek ‘23: Proceedings of the 26th International Academic Mindtrek Conferenc, 83-94. https://doi.org/10.1145/3616961.3616974

Rasnayaka, S., Wang, G., Shariffdeen, R., e Iyer, G. N. (2024). An Empirical study on usage and perceptions of LLMs in a software engineering project. Proceedings - 2024 International Workshop on Large Language Models for Code, LLM4Code 2024, 111-118. https://doi.org/10.1145/3643795.3648379

Richards, M., Waugh, K., Slaymaker, M., Petre, M., Woodthorpe, J., y Gooch, D. (2024). Bob or Bot: Exploring ChatGPT’s Answers to University Computer Science Assessment. ACM Transactions on Computing Education, 24(1), 5. https://doi.org/10.1145/3633287

Robleda, G. (2019). How to analyze and write the results of a systematic review. Enfermeria Intensiva, 30(4), 192-195. https://doi.org/10.1016/j.enfi.2019.09.001

Roest, L., Keuning, H., y Jeuring, J. (2023). Next-Step Hint Generation for Introductory Programming Using Large Language Models. arXiv:2312.10055.
https://doi.org/10.48550/arXiv.2312.10055

Sánchez-Ruiz, J., y Larrea-Silva, J. (2022). Factores que determinan la satisfacción estudiantil en tiempos de pandemia Covid-19: Revisión sistemática. Revista de Ciencias Sociales (Ve), XXVIII(E-6), 32-44. https://doi.org/10.31876/rcs.v28i.38817

Sánchez-Ruiz, L. M., Moll-López, S., Nuñez-Pérez, A., Moraño-Fernández, J. A., y Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13(10), 6039. https://doi.org/10.3390/app13106039

Sarsa, S., Denny, P., Hellas, A., y Leinonen, J. (2022). Automatic generation of programming exercises and code explanations using large language models. ICER 2022 - Proceedings of the 2022 ACM Conference on International Computing Education Research, 1, 27-43. https://doi.org/10.1145/3501385.3543957

Savelka, J., Agarwal, A., An, M., Bogart, C., y Sakr, M. (2023). Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses. ICER 2023 - Proceedings of the 2023 ACM Conference on International Computing Education Research, 1, 78-92. https://doi.org/10.1145/3568813.3600142

Savelka, J., Ashley, K. D., Gray, M. A., Westermann, H., y Xu, H. (June 2023). Can GPT-4 support analysis of textual data in tasks requiring highly specialized domain expertise? Proceedings of the Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL), 23, Braga, Portugal, 1-12. https://ceur-ws.org/Vol-3441/paper1.pdf

Segal, R., y Biton, Y. (2024). The contribution that utilizing Generative AI for problem posing makes to pre-service high school mathematics teachers’ TPACK. International Journal of Education in Mathematics, Science and Technology, 12(6), 1559-1582. https://doi.org/10.46328/ijemst.4591

Shen, Y., Ai, X., Soosai, A. G., Leo, R. J., y Syamkumar, M. (2024). Implications of ChatGPT for Data Science Education. SIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 1, 1230-1236. https://doi.org/10.1145/3626252.3630874

Singh, H., Tayarani-Najaran, M.-H., y Yaqoob, M. (2023). Exploring computer science students’ perception of ChatGPT in Higher Education: A descriptive and correlation study. Education Sciences, 13(9), 924. https://doi.org/10.3390/educsci13090924

Sterne, J. A. C., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H.-Y., Corbett, M. S., Eldridge, S. M., Emberson, J. R., Hernán, M. A., Hopewell, S., Hróbjartsson, A., Junqueira, D. R., Jüni, P., Kirkham, J. J., Lasserson, T., Li, T., … Higgins, J. P. T. (2019). RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. https://doi.org/10.1136/bmj.l4898

Sun, D., Boudouaia, A., Zhu, C., y Li, Y. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21, 14. https://doi.org/10.1186/s41239-024-00446-5

Udias, A., Alonso-Ayuso, A., Alfaro, C., Algar, M. J., Cuesta, M., Fernández-Isabel, A., Gómez, J., Lancho, C., Cano, E. L., Martín, I., y Ortega, F. (2024). ChatGPT’s performance in university admissions tests in mathematics. International Electronic Journal of Mathematics Education, 19(4), em0795. https://doi.org/10.29333/iejme/15517

Wahba, F., Ajlouni, A. O., y Abumosa, M. A. (2024). The impact of ChatGPT-based learning statistics on undergraduates’ statistical reasoning and attitudes toward statistics. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), em2468. https://doi.org/10.29333/ejmste/14726

Wilson, D. B. (2019). Systematic coding for research synthesis. In H. Cooper, L. V. Hedges y J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysi (3 ed., pp. 154-174). Russell Sage Foundation.

Xue, Y., Chen, H., Bai, G. R., Tairas, R., y Huang, Y. (2024). Does ChatGPT help with introductory programming? An experiment of students using ChatGPT in CS1. Proceedings - International Conference on Software Engineering, 331-341. https://doi.org/10.1145/3639474.3640076

Yi, L., Liu, D., Jiang, T., y Xian, Y. (2025). The Effectiveness of AI on K-12 Students’ Mathematics Learning: A Systematic Review and Meta-Analysis. International Journal of Science and Mathematics Education, 23(4), 1105-1126. https://doi.org/10.1007/s10763-024-10499-7

Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., y MacNeil, S. (2023). Generative AI in Computing Education: Perspectives of Students and Instructors. arXiv:2308.04309.
https://doi.org/10.48550/arXiv.2308.04309