Optimization of artificial intelligence in education through effective teaching strategies.
DOI:
https://doi.org/10.5281/zenodo.8078717Keywords:
artificial intelligence, education, teaching strategies, personalized learningAbstract
The project aimed to improve the use of artificial intelligence (AI) through effective teaching strategies. To achieve this, a methodology was implemented involving research and analysis of different pedagogical approaches, as well as the study of current applications of AI in education. Experiments and tests were conducted with AI systems, gathering information from students and teachers to assess their effectiveness in the teaching-learning process. The results demonstrated that the use of AI in education can significantly enhance the effectiveness of teaching strategies. AI provides additional tools and resources that allow for adapting educational content to the individual needs of each student, fostering more personalized and effective learning. Furthermore, AI can analyze large amounts of data and provide instant feedback, facilitating the identification of areas for improvement and the design of more precise educational intervention plans. In conclusion, this project highlighted the importance of optimizing the use of artificial intelligence in education through effective teaching strategies. AI has the potential to transform the way we teach and learn, offering opportunities for more personalized and effective education. However, it is essential for teachers to be prepared and trained to fully leverage the advantages of AI and adapt it appropriately to students' needs. With proper implementation and solid pedagogical strategies, AI can play a key role in improving the quality of education.
Downloads
References
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. Cambridge Handbook of the Learning Sciences, 2, 253-274.
Baker, R. S., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive– affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223-241.
Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445-459.
D'Mello, S. K., & Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145-157.
Fullan, M., & Langworthy, M. (2014). A rich seam: How new pedagogies find deep learning. Pearson.
Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19(3), 239-264.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. Piaget, J. (1977). The development of thought: Equilibration of cognitive structures. Viking Press.
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson. Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-32.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The journal is licensed under the Attribution License (CC BY), allowing the possibility of copying, distributing, displaying, and producing derivative works, as long as the author is acknowledged and cited.
