Application of artificial intelligence for the detection of educational needs and the personalization of evaluation processes
DOI:
https://doi.org/10.63688/aprendizaje.v3.i1.25Keywords:
artificial intelligence, educational evaluation, personalization of learning, educational needs, academic performance.Abstract
Introduction: Artificial intelligence is capable of recognizing learning difficulties and adjusting assessments to the characteristics of the students. Objective: The aim of this study was to test the effects of artificial intelligence on the arrest of educational needs and the adaptation of assessment in improving the academic performance of students in Higher Basic General Education. Methodology: A quantitative, applied, explanatory-comparative and quasi-experimental study was carried out with pre-test and post-test. The population was 240 students and the sample was 148. The sample was divided into the experimental group and the control group: both groups of 74 students. The experimental group received adaptation of the assessment over the course of eight weeks while the control group continued the conventional procedure. Simulated data were used to structure the analytical model. Results: The experimental group improved 17.8 points in performance compared to the control group (6.3 points) with a large effect (d = 1.42). The presence of students with a high need for reinforcement went from 41.9% to 10.8% and satisfactory mastery increased to 63.5%. Conclusion: Personalized student assessment with Artificial Intelligence can help detect academic needs, adapt tasks and improve student performance under teacher intervention.References
Alazemi AFT. Formative assessment in artificial integrated instruction: delving into the effects on reading comprehension progress, online academic enjoyment, personal best goals, and academic mindfulness. Lang Test Asia. 2024;14:44. https://doi.org/10.1186/s40468-024-00319-8
Albreiki B, Habuza T, Zaki N. Extracting topological features to identify at-risk students using machine learning and graph convolutional network models. Int J Educ Technol High Educ. 2023;20:23. https://doi.org/10.1186/s41239-023-00389-3
Banihashem SK, Noroozi O, van Ginkel S, Macfadyen LP, Biemans HJA. A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educ Res Rev. 2022;37:100489. https://doi.org/10.1016/j.edurev.2022.100489
Bond M, Khosravi H, De Laat M, Bergdahl N, Negrea V, Oxley E, et al. A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. Int J Educ Technol High Educ. 2024;21:4. https://doi.org/10.1186/s41239-023-00436-z
Chen L, Chen P, Lin Z. Artificial intelligence in education: a review. IEEE Access. 2020;8:75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Xia Q, Chiu TKF, Zhou X, Chai CS, Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput Educ Artif Intell. 2023;4:100118. https://doi.org/10.1016/j.caeai.2022.100118
Crompton H, Burke D. Artificial intelligence in higher education: the state of the field. Int J Educ Technol High Educ. 2023;20:22. https://doi.org/10.1186/s41239-023-00392-8
Demmans Epp C, Daniel BK, Muldner K. Editorial: learning analytics for supporting individualization: data-informed adaptation of learning. Front Educ. 2023;8:1240377. https://doi.org/10.3389/feduc.2023.1240377
Foldnes N, Uppstad PH, Grønneberg S, Thomson JM. School entry detection of struggling readers using gameplay data and machine learning. Front Educ. 2024;9:1487694. https://doi.org/10.3389/feduc.2024.1487694
Jang Y, Choi S, Jung H, Kim H. Practical early prediction of students' performance using machine learning and eXplainable AI. Educ Inf Technol. 2022;27:12855-12889. https://doi.org/10.1007/s10639-022-11120-6
Lin CC, Huang AYQ, Lu OHT. Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learn Environ. 2023;10:41. https://doi.org/10.1186/s40561-023-00260-y
Mallik S, Gangopadhyay A. Proactive and reactive engagement of artificial intelligence methods for education: a review. Front Artif Intell. 2023;6:1151391. https://doi.org/10.3389/frai.2023.1151391
Mao J, Chen B, Liu JC. Generative artificial intelligence in education and its implications for assessment. TechTrends. 2024;68:58-66. https://doi.org/10.1007/s11528-023-00911-4
Ouyang F, Dinh TA, Xu W. A systematic review of AI-driven educational assessment in STEM education. J STEM Educ Res. 2023;6:408-426. https://doi.org/10.1007/s41979-023-00112-x
Ouyang F, Zheng L, Jiao P. Artificial intelligence in online higher education: a systematic review of empirical research from 2011 to 2020. Educ Inf Technol. 2022;27:7893-7925. https://doi.org/10.1007/s10639-022-10925-9
Pagliara SM, Bonavolontà G, Pia M, Falchi S, Zurru AL, Fenu G, et al. The integration of artificial intelligence in inclusive education: a scoping review. Information. 2024;15(12):774. https://doi.org/10.3390/info15120774
Qushem UB, Oyelere SS, Akçapınar G, Kaliisa R, Laakso MJ. Unleashing the power of predictive analytics to identify at-risk students in computer science. Technol Knowl Learn. 2024;29:1385-1400. https://doi.org/10.1007/s10758-023-09674-6
Salas-Pilco SZ, Yang Y. Artificial intelligence applications in Latin American higher education: a systematic review. Int J Educ Technol High Educ. 2022;19:21. https://doi.org/10.1186/s41239-022-00326-w
Toyokawa Y, Horikoshi I, Majumdar R, Ogata H. Challenges and opportunities of AI in inclusive education: a case study of data-enhanced active reading in Japan. Smart Learn Environ. 2023;10:67. https://doi.org/10.1186/s40561-023-00286-2
Xia Q, Weng X, Ouyang F, Lin TJ, Chiu TKF. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ. 2024;21:40. https://doi.org/10.1186/s41239-024-00468-z
Xu W, Ouyang F. A systematic review of AI role in the educational system based on a proposed conceptual framework. Educ Inf Technol. 2022;27:4195-4223. https://doi.org/10.1007/s10639-021-10774-y
Yang Y, Chen L, He W, Sun D, Salas-Pilco SZ. Artificial intelligence for enhancing special education for K-12: a decade of trends, themes, and global insights (2013-2023). Int J Artif Intell Educ. 2025;35:1129-1177. https://doi.org/10.1007/s40593-024-00422-0
Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education: where are the educators? Int J Educ Technol High Educ. 2019;16:39. https://doi.org/10.1186/s41239-019-0171-0
Zhai X, Chu X, Chai CS, Jong MSY, Istenič A, Spector M, et al. A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity. 2021;2021:8812542. https://doi.org/10.1155/2021/8812542
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