Artificial Intelligence (AI) Based Instructional Model for Enhancing Personalized Learning in the Classroom
Keywords:
Artificial Intelligence, , Personalized Learning, Student Engagement, Academic Performance, Teacher Interaction, Gender DifferencesAbstract
In this research, I address the question of whether an instructional model based on Artificial Intelligence (AI) can help provide more individual learning experiences in an engaging English classroom context among the learners in the ninth grade. A quantitative type of research using a sample size of 30 students was performed through pre- and post-intervening surveys, engagement and performance monitoring. The main aims were presenting the effectiveness of AI in providing personalized content provision, measuring its influence on the student engagement and performance, the interaction of a teacher with the system, and revealing the distinctions based on gender in terms of system acceptance and effectiveness. The results indicated that the AI instruction model contributed to significant increases in the personalized learning results. The level of engagement, educational performance, and the interaction with the AI system proved to be higher in case of male students and lower in case of female students. It was discovered that teacher participation was an important key to the effective implementation of the AI tool and the greater the interaction of a teacher with students, the better are the results of the learning. These findings are indicative of the fact that gender relations, as well as an active teacher intervening process, are critical factors in the process of AI-guided instruction optimization. On the basis of those results, it is suggested that schools should utilize AI based models in facilitating personal learning, as well as well-planned teacher education and gender-sensitive interaction patterns. The research is an addition to the existing evidence of the usefulness of AI in the educational environment and the criticality of the inclusive practices approach that would help realize the potential value of AI to its maximum. To further elaborate, future studies ought to consider these findings beyond the short-term effects, different subjects, and large classes of students with the intention of comprehending how such AI-based learning interventions are scalable and sustainable.
Downloads
References
1. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.
2. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
3. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
4. Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). Informing progress: Insights on personalized learning implementation and effects. RAND Corporation.
5. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
6. UNESCO. (2019). I'd blush if I could: Closing gender divides in digital skills through education. United Nations Educational, Scientific and Cultural Organization.
7. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
8. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
9. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
10. Hwang, G.-J., & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis and research agenda. Computers & Education: Artificial Intelligence, 2, 100038. https://doi.org/10.1016/j.caeai.2021.100038
11. UNESCO. (2019). I’d blush if I could: Closing gender divides in digital skills through education. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000367416
12. Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Lawless, S. (2021). AI Grand Challenges for Education. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15795–15803. https://doi.org/10.1609/aaai.v35i18.17854
13. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
14. Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer.
15. Gagné, R. M., Wager, W. W., Golas, K. C., & Keller, J. M. (2005). Principles of instructional design (5th ed.). Wadsworth Publishing.
16. Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
17. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x.
18. Allen, M. W., & Sites, R. (2012). Leaving ADDIE for SAM: An agile model for developing the best learning experiences. ASTD Press.
19. Al-Jarf, R. (2021). Online technologies in English as a foreign language (EFL) instruction in Saudi Arabia. Arab World English Journal (AWEJ), 12(2), 198–215. https://doi.org/10.24093/awej/vol12no2.14
20. Cavus, N., & Zabadi, T. (2014). A comparison of learning management systems (LMS) for use in higher education in the Middle East. The International Review of Research in Open and Distributed Learning, 15(2), 276–295. https://doi.org/10.19173/irrodl.v15i2.1714
21. Godwin-Jones, R. (2018). Using mobile technology to develop language skills and cultural understanding. Language Learning & Technology, 22(3), 1–17. https://doi.org/10125/44609
22. Hockly, N. (2016). Focus on learning technologies. Oxford University Press.
23. Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview. Language Teaching, 31(2), 57–71. https://doi.org/10.1017/S0261444800012970
24. Yin, C., & Benson, P. (2020). Autonomy, agency, and identity in foreign and second language education. Multilingual Matters.
25. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. Discusses AI potential in education mostly in developed settings; highlights need for contextual adaptation.
26. Williamson, B., & Piattoeva, N. (2021). Objectivity as standardization in data-scientific educational governance: Grasping the global through the local. Research in Education, 101(1), 30-56. Discusses the challenges of applying AI-driven educational models globally without local contextualization.
27. Muralidharan, K., & Prakash, N. (2017). Cycling to school: Increasing secondary school enrollment for girls in India. American Economic Journal: Applied Economics, 9(3), 321-350. Examines gender disparities in educational access, useful to support points on gendered technology impact.
28. Vogels, E. A., Perrin, A., & Anderson, M. (2020). Digital divide persists even as lower-income Americans make gains in tech adoption. Pew Research Center. Provides evidence of gender and socioeconomic disparities in technology access.
29. Tsai, M. J., & Tsai, C. C. (2018). Exploring the roles of teachers in supporting students' inquiry-based learning in a technology-enhanced environment. Computers & Education, 125, 137-150. Emphasizes the critical role of teachers in technology-integrated classrooms.
30. Baker, R. S. J. D., & Siemens, G. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61-75). Springer. Discusses the focus on cognitive outcomes and the need to integrate affective factors in AI research.
31. UNESCO (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. UNESCO Publishing. Highlights the challenges of AI implementation in under-resourced schools and stresses infrastructure and contextual limitations.
32. Khan, R., & Bhatti, R. (2021). Barriers to digital literacy and e-learning in rural Pakistan: A qualitative study. International Journal of Educational Development, 81, 102344. Directly relevant to infrastructural and cultural challenges faced by schools in Southern Pakistan.
33. Nawaz, A., & Kundi, G. M. (2010). ICTs in education: Gender dimensions in rural Pakistan. Turkish Online Journal of Distance Education, 11(4), 131-142. Explores gender-based disparities in ICT access and use in Pakistan’s rural areas.
34. Rehman, S. U., & Ahmad, N. (2017). Exploring digital literacy and gender digital divide in Pakistan. Pakistan Journal of Social Sciences, 37(1), 319-326. Addresses the digital gender gap in Pakistan’s education sector.
35. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity. Examines the mediating role of teachers in AI-driven educational models.
36. Khan, M. A., & Ali, A. (2020). Challenges to digital education in Pakistan: The case of COVID-19 pandemic. Journal of Educational Technology & Online Learning, 3(1), 1-11.Discusses issues like electricity and digital literacy affecting education in Pakistan.
37. CAST. (2018). Universal Design for Learning Guidelines version 2.2. http://udlguidelines.cast.org
38. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
39. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
40. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
41. Santos, O. C., Boticario, J. G., & Pérez-Marín, D. (2021). AI-powered adaptive learning systems: A systematic review and future directions. IEEE Transactions on Learning Technologies, 14(1), 1–14. https://doi.org/10.1109/TLT.2020.2999824
42. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
43. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
44. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
45. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
46. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
47. Santos, O. C., Boticario, J. G., & Pérez-Marín, D. (2021). AI-powered adaptive learning systems: A systematic review and future directions. IEEE Transactions on Learning Technologies, 14(1), 1–14. https://doi.org/10.1109/TLT.2020.2999824
48. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Muhammad Kashif Majeed, Dr. Muhammad Waris Ali, Prof, Dr. Tunku Badariah Ahmad (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
