INNOVATIVE DIRECTIONS FOR DEVELOPING LOGICAL THINKING IN MATHEMATICS EDUCATION BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGIES
Artificial intelligence (AI) technologies are fundamentally transforming many aspects of the education system, and their gradual integration into mathematics teaching is rapidly accelerating. This study analyzes the main directions and current scientific findings related to the implementation of AI technologies in the process of teaching mathematics. The research highlights the pedagogical significance of Data-Driven Learning (DDL), Natural Language Processing (NLP) systems in developing students’ logical and reflective thinking skills.
1. Ai, H. (2017). Providing graduated corrective feedback in an intelligent computer-assisted language learning environment. ReCALL, 29(3), 313 - 334.
2. Barrot, J. S. (2023). Using automated written corrective feedback in the writing classrooms: Effects on L2 writing accuracy. Computer Assisted Language Learning, 36(4), 584 - 607.
3. Bibauw, S., François, T., & Desmet, P. (2019). Discussing with a computer to practice a foreign language: Research synthesis and conceptual framework of dialogue-based CALL. Computer Assisted Language Learning, 32(8), 1 - 51.
4. Boulton, A., & Vyatkina, N. (2021). Thirty years of data-driven learning: Taking stock and charting new directions over time. Language Learning & Technology, 25(3), 66 - 89.
5. Chen, H. H. J., Yang, C. T. Y., & Lai, K. K. W. (2023). Investigating college EFL learners’ perceptions toward the use of Google Assistant for foreign language learning. Interactive Learning Environments, 31(3), 1335 - 1350.
6. Chinkina, M., Ruiz, S., & Meurers, D. (2020). Crowdsourcing evaluation of the quality of automatically generated questions for supporting computer-assisted language teaching. ReCALL, 32(2), 145 - 161.
7. Choi, I. C. (2016). Efficacy of an ICALL tutoring system and process-oriented corrective feedback. Computer Assisted Language Learning, 29(2), 334 - 364.
8. Crosthwaite, P., & Steeples, B. (2022). Data-driven learning with younger learners: Exploring corpus-assisted development of the passive voice for science writing with female secondary school students. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2022.2068615
9. Daniels, P., & Iwago, K. (2017). The suitability of cloud-based speech recognition engines for language learning. The JALT CALL Journal, 13(3), 229 - 239.
10. Ebadi, S., & Saeedian, A. (2015). The effects of computerized dynamic assessment on promoting at-risk advanced Iranian EFL students’ reading skills. Issues in Language Teaching, 4(2), 1 - 26.
11. Ebadi, S., & Rahimi, M. (2019). Mediating EFL learners’ academic writing skills in online dynamic assessment using Google Docs. Computer Assisted Language Learning, 32(5 - 6), 527 - 555.
12. Encyclopedia Britannica. (2021). Artificial intelligence. https://www.britannica.com/technology/artificial-intelligence.
13. Evers, K., & Chen, S. (2022). Effects of an automatic speech recognition system with peer feedback on pronunciation instruction for adults. Computer Assisted Language Learning, 35(8), 1869 – 1889.
14. Hadley, G., & Charles, M. (2017). Enhancing extensive reading with data-driven learning. Language Learning & Technology, 21(3), 131 - 152.
15. Han, T., & Sari, E. (2022). An investigation on the use of automated feedback in Turkish EFL students’ writing classes. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2022.2067179.
16. Liu, S., & Yu, G. (2022). L2 learners’ engagement with automated feedback: An eye-tracking study. Language Learning & Technology, 26(2), 78 - 105.
17. Mindzak, M., & Eaton, S. E. (2021). Artificial intelligence is getting better at writing, and universities should worry about plagiarism. The Conversation. https://theconversation.com/artificial-intelligence-is-getting-better-at-writing-and-universities-should-worry-about-plagiarism-160481.
18. Pérez-Paredes, P. (2022). A systematic review of the uses and spread of corpora and data-driven learning in CALL research during 2011 - 2015. Computer Assisted Language Learning, 35(1 - 2), 36 - 61.
19. Pérez-Paredes, P., Guillamón, C. O., & Jiménez, P. A. (2018). Language teachers’perceptions on the use of OER language processing technologies in MALL. Computer Assisted Language Learning, 31(5 - 6), 522 - 545.
20. Pikhart, M. (2020). Intelligent information processing for language education: The use of artificial intelligence in language learning apps. Procedia Computer Science, 176, 1412 - 1419.
Copyright (c) 2025 «ACTA NUUz»
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
.jpg)
1.png)
