Use of IA to improve the process of teaching-learning of mathematics in students of Engineering
Uso de IA para mejorar el proceso de enseñanza-aprendizaje de matemáticas en estudiantes de Ingeniería
Main Article Content
This article analyzes the use of the artificial intelligence (IA) to improve the process of teaching-learning of mathematics in students of Engineering, using a focus of documental revision. The investigation was centered in identifying the main tendencies and current focuses in the application of IA in the mathematical education. Through a critical analysis of the literature, he/she stood out the potential of the IA to personalize the learning, to provide immediate feedback and to improve the educational quality by means of the analysis of data. Also, the challenges and ethical considerations are discussed that accompany the implementation of these technologies in educational contexts, underlining the importance of a careful and equal adoption. This study provides an integral vision of the current state of the investigation in this field, delineating as much the opportunities as the challenges that it faces the education in Engineering when integrating IA in its pedagogic methodologies.
Downloads
Article Details
Abichandani, P., Iaboni, C., Lobo, D., & Kelly, T. (2023). Artificial intelligence and computer vision education: Codifying student learning gains and attitudes. Computers and Education: Artificial Intelligence, 5, 100159. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100159
Ahaidous, K., Tabaa, M., & Hachimi, H. (2023). Towards IoT-Big Data architecture for future education. Procedia Computer Science, 220, 348-355. https://doi.org/https://doi.org/10.1016/j.procs.2023.03.045
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100215
Alnasyan, B., Basheri, M., & Alassafi, M. (2024). The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100231. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100231
Archambault, S. G., Ramachandran, S., Acosta, E., & Fu, S. (2024). Ethical dimensions of algorithmic literacy for college students: Case studies and cross-disciplinary connections. The Journal of Academic Librarianship, 50(3), 102865. https://doi.org/https://doi.org/10.1016/j.acalib.2024.102865
Balsano, C., Burra, P., Duvoux, C., Alisi, A., Piscaglia, F., Gerussi, A., Brunetto, M. R., Bonino, F., Montalti, R., Campanile, S., Persico, M., Alvaro, D., Santini, S., Invernizzi, P., Carbone, M., Masarone, M., Eccher, A., Siciliano, B., Vento, M., Ficuciello, F., Cabitza, F., Penasa, S., & Donatelli, P. (2023). Artificial Intelligence and liver: Opportunities and barriers. Digestive and Liver Disease, 55(11), 1455-1461. https://doi.org/https://doi.org/10.1016/j.dld.2023.08.048
Bankins, S., Jooss, S., Restubog, S. L. D., Marrone, M., Ocampo, A. C., & Shoss, M. (2024). Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research. Journal of Vocational Behavior, 153, 104011. https://doi.org/https://doi.org/10.1016/j.jvb.2024.104011
Bartra, R., Pinedo, L. P. & Navarro, J. R. (2024). Incorporación de las TIC en la promoción de destinos turísticos: una revisión sistemática. Región Científica, 3(2). https://doi.org/10.58763/rc2024281
Benvenuti, M., Cangelosi, A., Weinberger, A., Mazzoni, E., Benassi, M., Barbaresi, M., & Orsoni, M. (2023). Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Computers in Human Behavior, 148, 107903. https://doi.org/https://doi.org/10.1016/j.chb.2023.107903
Bernabei, M., Colabianchi, S., Falegnami, A., & Costantino, F. (2023). Students’ use of large language models in engineering education: A case study on technology acceptance, perceptions, efficacy, and detection chances. Computers and Education: Artificial Intelligence, 5, 100172. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100172
Bingley, W. J., Curtis, C., Lockey, S., Bialkowski, A., Gillespie, N., Haslam, S. A., Ko, R. K. L., Steffens, N., Wiles, J., & Worthy, P. (2023). Where is the human in human-centered AI? Insights from developer priorities and user experiences. Computers in Human Behavior, 141, 107617. https://doi.org/https://doi.org/10.1016/j.chb.2022.107617
Bingley, W. J., Haslam, S. A., Steffens, N. K., Gillespie, N., Worthy, P., Curtis, C., Lockey, S., Bialkowski, A., Ko, R. K. L., & Wiles, J. (2023). Enlarging the model of the human at the heart of human-centered AI: A social self-determination model of AI system impact. New Ideas in Psychology, 70, 101025. https://doi.org/https://doi.org/10.1016/j.newideapsych.2023.101025
Bressane, A., Zwirn, D., Essiptchouk, A., Saraiva, A. C. V., Carvalho, F. L. d. C., Formiga, J. K. S., Medeiros, L. C. d. C., & Negri, R. G. (2024). Understanding the role of study strategies and learning disabilities on student academic performance to enhance educational approaches: A proposal using artificial intelligence. Computers and Education: Artificial Intelligence, 6, 100196. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100196
Caccavale, F., Gargalo, C. L., Gernaey, K. V., & Krühne, U. (2023). SPyCE: A structured and tailored series of Python courses for (bio)chemical engineers. Education for Chemical Engineers, 45, 90-103. https://doi.org/https://doi.org/10.1016/j.ece.2023.08.003
Caccavale, F., Gargalo, C. L., Gernaey, K. V., & Krühne, U. (2024). Towards Education 4.0: The role of Large Language Models as virtual tutors in chemical engineering. Education for Chemical Engineers, 49, 1-11. https://doi.org/https://doi.org/10.1016/j.ece.2024.07.002
Cardeño, N., Cardeño, E. J. & Bonilla, E. (2023). TIC y transformación académica en las universidades. Región Científica, 2(2). https://doi.org/10.58763/rc202370
Carroll, A. J., & Borycz, J. (2024). Integrating large language models and generative artificial intelligence tools into information literacy instruction. The Journal of Academic Librarianship, 50(4), 102899. https://doi.org/https://doi.org/10.1016/j.acalib.2024.102899
Carter, R., & Shea, K. G. (2024). Panel Discussion: Profiles in Surgical Innovation & Entrepreneurship. Journal of the Pediatric Orthopaedic Society of North America, 8, 100098. https://doi.org/https://doi.org/10.1016/j.jposna.2024.100098
Chiu, T. K. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100197
Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/https://doi.org/10.1016/j.caeai.2022.100118
Daza, A., Miranda, J. C. H., Cornelio, J. B., López Carranza, A. R., & Ponce Sánchez, C. F. (2023). Predicting the depression in university students using stacking ensemble techniques over oversampling method. Informatics in Medicine Unlocked, 41, 101295. https://doi.org/https://doi.org/10.1016/j.imu.2023.101295
Galindo, F. J. (2024). Critique on STEM activities for heat transfer learning. Education for Chemical Engineers. https://doi.org/https://doi.org/10.1016/j.ece.2024.06.002
Gómez, C. A., Sánchez, V. & Eslava, R. (2024). Bibliometric analysis of the main applications of digital technologies to business management. Data and Metadata, 3. https://dm.saludcyt.ar/index.php/dm/article/view/321
Gonzales Tito, Y. M., Quintanilla López, L. N., & Pérez Gamboa, A. J. (2023). Metaverse and education: A complex space for the next educational revolution. Metaverse Basic and Applied Research, 2, 56. https://doi.org/10.56294/mr202356
Guerra, D. D., Gamboa, A. J. & Cano, C. A. (2023). Social network analysis in virtual educational environments: Implications for collaborative learning and academic community development. AWARI, 4, 1-12. https://awari.pro-metrics.org/index.php/a/article/view/59
Hu, T. (2023). Software Engineering Classification Model and Algorithm Based on Big Data Technology. Procedia Computer Science, 228, 119-128. https://doi.org/https://doi.org/10.1016/j.procs.2023.11.015
Khan, M. S., Salele, N., Hasan, M., & Abdou, B. O. (2023). Factors affecting student readiness towards OBE implementation in engineering education: Evidence from a developing country. Heliyon, 9(10), e20905. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e20905
Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100225
Laplagne, C. & Urnicia, J. J. (2023). Protocolos de B-learning para la alfabetización informacional en la Educación Superior. Región Científica, 2(2). https://doi.org/10.58763/rc202373
Lepore, M. (2024). A holistic framework to model student's cognitive process in mathematics education through fuzzy cognitive maps. Heliyon, 10(16), e35863. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e35863
Lin, C., Cheng, E. S. J., Huang, A. Y. Q., & Yang, S. J. H. (2024). DNA of learning behaviors: A novel approach of learning performance prediction by NLP. Computers and Education: Artificial Intelligence, 6, 100227. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100227
Liu, Y. (2024). Large language models for air transportation: A critical review. Journal of the Air Transport Research Society, 2, 100024. https://doi.org/https://doi.org/10.1016/j.jatrs.2024.100024
Lohakan, M., & Seetao, C. (2024). Large-scale experiment in STEM education for high school students using artificial intelligence kit based on computer vision and Python. Heliyon, 10(10), e31366. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e31366
López, Y. Y. (2023). Aptitud digital del profesorado frente a las competencias TIC en el siglo XXI: una evaluación de su desarrollo. Región Científica, 2(2). https://doi.org/10.58763/rc2023119
Márquez, M. A., Martínez-Quezada, M., Calderón-Suárez, R., Rodríguez, A., & Ortega-Mendoza, R. M. (2023). Microcontrollers programming for control and automation in undergraduate biotechnology engineering education. Digital Chemical Engineering, 9, 100122. https://doi.org/https://doi.org/10.1016/j.dche.2023.100122
Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022. https://doi.org/https://doi.org/10.1016/j.chbah.2023.100022
Miranda, V. M., & Sandoval, E. (2024). La educación expandida en contextos educativos formales e informales. Región Científica, 3(2), 2024321. https://doi.org/10.58763/rc2024321
Mosleh, S. M., Alsaadi, F. A., Alnaqbi, F. K., Alkhzaimi, M. A., Alnaqbi, S. W., & Alsereidi, W. M. (2024). Examining the association between emotional intelligence and chatbot utilization in education: A cross-sectional examination of undergraduate students in the UAE. Heliyon, 10(11), e31952. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e31952
Mosquera, D., Ruiz, M., Pastor, O., & Spielberger, J. (2024). Understanding the landscape of software modelling assistants for MDSE tools: A systematic mapping. Information and Software Technology, 173, 107492. https://doi.org/https://doi.org/10.1016/j.infsof.2024.107492
Nachouki, M., Mohamed, E. A., Mehdi, R., & Abou Naaj, M. (2023). Student course grade prediction using the random forest algorithm: Analysis of predictors' importance. Trends in Neuroscience and Education, 33, 100214. https://doi.org/https://doi.org/10.1016/j.tine.2023.100214
Naser, M. Z. (2023). Machine learning for all! Benchmarking automated, explainable, and coding-free platforms on civil and environmental engineering problems. Journal of Infrastructure Intelligence and Resilience, 2(1), 100028. https://doi.org/https://doi.org/10.1016/j.iintel.2023.100028
Ng, F. Y., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., Wong, T. Y., Kwek, K., Doshi-Velez, F., Lucey, C., Coffman, T., & Ting, D. S. W. (2023). Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Reports Medicine, 4(10), 101230. https://doi.org/https://doi.org/10.1016/j.xcrm.2023.101230
Núñez, E. G. & Espinosa, J. F. (2024). Liderazgo ético y comportamiento de los empleados. Análisis cienciométrico en la producción científica. Región Científica, 3(2). https://doi.org/10.58763/rc2024295
Pérez Gamboa, A. J., & Díaz-Guerra, D. D. (2023). Artificial Intelligence for the development of qualitative studies. LatIA, 1, 4. https://doi.org/10.62486/latia20234
Petrescu, M. A., Pop, E. L., & Dan Mihoc, T. (2023). Students’ interest in knowledge acquisition in Artificial Intelligence. Procedia Computer Science, 225, 1028-1036. https://doi.org/https://doi.org/10.1016/j.procs.2023.10.090
Pinto, M., Garcia, J., Caballero, D., Manso, R., Uribe, A., & Gomez, C. (2024). Assessing information, media and data literacy in academic libraries: Approaches and challenges in the research literature on the topic. The Journal of Academic Librarianship, 50(5), 102920. https://doi.org/https://doi.org/10.1016/j.acalib.2024.102920
Pursnani, V., Sermet, Y., Kurt, M., & Demir, I. (2023). Performance of ChatGPT on the US fundamentals of engineering exam: Comprehensive assessment of proficiency and potential implications for professional environmental engineering practice. Computers and Education: Artificial Intelligence, 5, 100183. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100183
Raudales, E. V., Acosta, J. V. & Aguilar, P. A. (2024). Economía circular: una revisión bibliométrica y sistemática. Región Científica, 3(1). https://doi.org/10.58763/rc2024192
Ravi, M. (2023). Evolving trends in student assessment in chemical engineering education. Education for Chemical Engineers, 45, 151-160. https://doi.org/https://doi.org/10.1016/j.ece.2023.09.003
Ray, P. P. (2024). ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions. Earthquake Science, 37(4), 352-367. https://doi.org/https://doi.org/10.1016/j.eqs.2024.04.003
Rocha, R. G., Paço, A. d., & Alves, H. (2024). Entrepreneurship education for non-business students: A social learning perspective. The International Journal of Management Education, 22(2), 100974. https://doi.org/https://doi.org/10.1016/j.ijme.2024.100974
Roman-Acosta, D., Rodríguez Torres, E., Baquedano Montoya, M. B., López Zavala, L. C., & Pérez Gamboa, A. J. (2024). ChatGPT y su uso para perfeccionar la escritura académica en educandos de posgrado. Praxis Pedagógica, 24(36), 53–75. https://doi.org/10.26620/uniminuto.praxis.24.36.2024.53-75
Rufai, A. A., Hossain Khan, M. S., & Hasan, M. (2024). An exploration of pedagogical approaches in teaching artificial intelligence courses: Experience from undergraduates students of Bangladesh. Social Sciences & Humanities Open, 10, 101075. https://doi.org/https://doi.org/10.1016/j.ssaho.2024.101075
Sánchez, V., Pérez, A. J. & Gómez, C. A. (2024). Trends and evolution of Scientometric and Bibliometric research in the SCOPUS database. Bibliotecas. Anales de Investigación, 20(1), 1-22. http://revistas.bnjm.sld.cu/index.php/BAI/article/view/834
Scholes, C. A. (2023). Utilising forensic tools to assist in chemical engineering capstone assessment grading. Education for Chemical Engineers, 45, 61-67. https://doi.org/https://doi.org/10.1016/j.ece.2023.08.001
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100259
Tabuenca, B., Moreno-Sancho, J.-L., Arquero-Gallego, J., Greller, W., & Hernández-Leo, D. (2023). Generating an environmental awareness system for learning using IoT technology. Internet of Things, 22, 100756. https://doi.org/https://doi.org/10.1016/j.iot.2023.100756
Velásquez, L. A. & Paredes, J. A. (2024). Revisión sistemática sobre los desafíos que enfrenta el desarrollo e integración de las tecnologías digitales en el contexto escolar chileno, desde la docencia. Región Científica, 3(1). https://doi.org/10.58763/rc2024226
Wainwright, H. M., Powell, B. A., Hoover, M. E., Ayoub, A., Atz, M., Benson, C., Borrelli, R. A., Djokic, D., Eddy-Dilek, C. A., Ermakova, D., Hayes, R., Higley, K., Krahn, S., Lagos, L., Landsberger, S., Leggett, C., Regalbuto, M., Roy, W., Shuller-Nickles, L., & Ewing, R. C. (2023). Nuclear waste Educator's workshop: What and how do we teach about nuclear waste? Journal of Environmental Radioactivity, 270, 107288. https://doi.org/https://doi.org/10.1016/j.jenvrad.2023.107288
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/https://doi.org/10.1016/j.eswa.2024.124167
Wei, Z., Calautit, J. K., Wei, S., & Tien, P. W. (2024). Real-time clothing insulation level classification based on model transfer learning and computer vision for PMV-based heating system optimization through piecewise linearization. Building and Environment, 253, 111277. https://doi.org/https://doi.org/10.1016/j.buildenv.2024.111277
Xiao, R., Wang, Y., Wang, X., Liu, A., & Zhang, J. (2023). Deep reinforcement learning-driven smart and dynamic mass personalization. Procedia CIRP, 119, 97-102. https://doi.org/https://doi.org/10.1016/j.procir.2023.04.004
Xu, X., Su, Y., Zhang, Y., Wu, Y., & Xu, X. (2024). Understanding learners’ perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China. Heliyon, 10(4), e26239. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e26239
Yang, Y., Deb, S., He, M., & Kobir, M. H. (2023). The use of virtual reality in manufacturing education: State-of-the-art and future directions. Manufacturing Letters, 35, 1214-1221. https://doi.org/https://doi.org/10.1016/j.mfglet.2023.07.023