PREDICTIVE LEARNING POWERED BY AI AND SOPHISTICATED STUDENT ENGAGEMENT TECHNIQUES

Year : 2026 | Volume : 3 | 01 | Page :
    By

    Mohd Bilal Khan,

  • Ruchika Aggarwal,

  1. Assistant Professor, Department of Computer Science & Engineering, EIT, Faridabad, Haryana, India
  2. Assistant Professor, Department of Computer Science & Engineering, EIT, Faridabad, Haryana, India

Abstract

The contemporary landscape of education has witnessed a paradigm shift in integrating advanced technologies that have revolutionized the learning experience. Innovative methodologies have emerged to address longstanding challenges, such as enhancing student engagement, accurately predicting academic performance, and personalizing the learning journey. However, despite the numerous benefits that technology brings to education, there remains a crucial hurdle – sustaining student motivation and engagement. Traditional teaching methodologies often struggle to generate consistent interest and involvement among learners. To address this challenge, this research endeavors to examine a series of pioneering frameworks and algorithms that are designed to assess and improve student engagement and academic success. The research will focus on cutting-edge approaches that leverage artificial intelligence (AI), machine learning (ML), and gamification strategies. These technologies enable the creation of personalized learning experiences that can cater to the unique needs and preferences of individual learners. By analyzing learner data, such as their learning style, pace, and progress, these approaches can provide targeted feedback and recommendations to help learners achieve their academic goals. This research aims to contribute to the ongoing efforts to enhance the quality of education by exploring state-of-the-art approaches that can enhance student engagement, predict academic performance accurately, and provide personalized learning experiences.The interdisciplinary exploration in this research covers several key aspects. Firstly, it looks at hybrid gamification, AI tutoring frameworks, and the Adaptive Neuro-Fuzzy Inference System (ANFIS). These methodologies aim to create an immersive and personalized learning ecosystem by using AI-driven mechanisms to track, analyze and reward student performance and interactions, thus enhancing motivation and engagement levels. Hybrid gamification is a technique that combines game elements with traditional teaching methods. It uses game mechanics like points, badges, and leader boards to create a more engaging learning experience.

Keywords: Student Engagement, Artificial Intelligence in Education, Personalized Learning, Hybrid Gamification, Academic Performance Prediction

How to cite this article:
Mohd Bilal Khan, Ruchika Aggarwal. PREDICTIVE LEARNING POWERED BY AI AND SOPHISTICATED STUDENT ENGAGEMENT TECHNIQUES. International Journal of Behavioral Sciences. 2026; 03(01):-.
How to cite this URL:
Mohd Bilal Khan, Ruchika Aggarwal. PREDICTIVE LEARNING POWERED BY AI AND SOPHISTICATED STUDENT ENGAGEMENT TECHNIQUES. International Journal of Behavioral Sciences. 2026; 03(01):-. Available from: https://journals.stmjournals.com/ijbsc/article=2026/view=242257


References

1. Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017).
Education technology: An evidence-based review.
2. Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., &; Kerres, M.
(2020). Mapping research in student engagement and educational technology in higher
education: A systematic evidence map. International journal of educational technology in
higher education, 17(1), 1-30.
3. Morel, G. M., & Spector, J. M. (2022). Foundations of educational
technology: Integrative approaches and interdisciplinary perspectives. Taylor &; Francis.
4. Cloete, A. L. (2017). Technology and education: Challenges and opportunities.
HTS: Theological Studies, 73(3), 1-7.
5. Castañeda, L., &; Selwyn, N. (2018). More than tools? Making sense of the
ongoing digitizations of higher education. International Journal of Educational Technology
in Higher Education, 15(1), 1-10.
6. Raja, R., &; Nagasubramani, P. C. (2018). Impact of modern technology in
education. Journal of Applied and Advanced Research, 3(1), 33-35.
7. Sivarajah, R. T., Curci, N. E., Johnson, E. M., Lam, D. L., Lee, J. T., &
Richardson, M. L. (2019). A review of innovative teaching methods. Academic
radiology, 26(1), 101-113.
8. Smith, R. C., Schaper, M. M., Tamashiro, M. A., Van Mechelen, M., Petersen,
M. G., &; Iversen, O. S. (2023). A research agenda for computational empowerment for
emerging technology education. International Journal of Child-Computer Interaction,
100616.
9. Garzón Artacho, E., Martínez, T. S., Ortega Martin, J. L., Marin Marin, J. A.,
&; Gomez Garcia, G. (2020). Teacher training in lifelong learning—The importance of
digital competence in the encouragement of teaching innovation. Sustainability, 12(7),
2852.
10. Forbes, 2022,
https://www.forbes.com/sites/emmawhitford/2022/04/19/cyberattacks-pose existential-risk-to-
colleges-and-sealed-one-small-colleges- fate/?sh=627f483353c2

19
11. The Guardian,
2021,https://www.theguardian.com/education/2022/nov/06/woeful-dfe-blamed-as-betting-
firms-gain-access-to-childrens-data
12. The Times of India, 2023,
https://timesofindia.indiatimes.com/education/news/ncf-2023-advocates- personalized-
learning-for-student-success/articleshow/103023786.cms
13. The New York Times, 2022,
https://www.nytimes.com/2022/12/27/business/ai-education-app-riiid.html
14. ​Cyber security Report, 2023, https://www.wipro.com/cybersecurity/state-of-
cybersecurity-report-2023/
15. Tyagi, A. K. (Ed.). (2023). Privacy Preservation and Secured Data Storage in
Cloud Computing. IGI Global.
16. Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence
in education: Challenges and opportunities for sustainable development.
17. Chawla, D., & Mehra, P. S. (2023). A roadmap from classical cryptography to
post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and
solutions. Internet of Things, 100950.
18. Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., … &
Khan, S. U. (2021). Predicting at-risk students at different percentages of course length for
early intervention using machine learning models. Ieee Access, 9, 7519-7539.
19. Zawacki-Richter, O., & Latchem, C. (2018). Exploring four decades of
research in Computers & Education. Computers & Education, 122, 136-152.
20. Trelease, R. B. (2016). From chalkboard, slides, and paper to e‐learning: How
computing technologies have transformed anatomical sciences education. Anatomical
sciences education, 9(6), 583-602.
21. Lister, M. (2015). Gamification: The effect on student motivation and
performance at the post-secondary level. Issues and Trends in Educational Techology, 3(2).
22. Kinshuk, Chen, N. S., Cheng, I. L., & Chew, S. W. (2016). Evolution is not
enough: Revolutionizing current learning environments to smart learning environments.
International Journal of Artificial Intelligence in Education, 26, 561-581.
23. Hamari, J., Koivisto, J., & Sarsa, H. (2014, January). Does gamification
work?–a literature review of empirical studies on gamification. In 2014 47th Hawaii
international conference on system sciences (pp. 3025-3034). IEEE.
24. Landers, R. N., Bauer, K. N., Callan, R. C., & Armstrong, M. B. (2015).
Psychological theory and the gamification of learning. Gamification in education and
business, 165-186.

20
25. Nacke, L. E., & Deterding, S. (2017). The maturing of gamification
research. Computers in Human Behavior, 71, 450-454.
26. Koivisto, J., & Hamari, J. (2014). Demographic differences in perceived
benefits from gamification. Computers in Human Behavior, 35, 179-188.
27. Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey.
International Journal of human-computer studies, 74, 14-31.
28. Lee, B. C. (2019). The effect of gamification on psychological and behavioral
outcomes: Implications for cruise tourism destinations. Sustainability, 11(11), 3002.
29. Adams, S. P., & Du Preez, R. (2022). Supporting student engagement through
the gamification of learning activities: A design-based research approach. Technology,
Knowledge and Learning, 1-20.
30. Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I.
(2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902.
31. Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need
for AI ethics education. AI and Ethics, 1, 61-65.
32. Alyammahi, A. (2020). Investigating the Impact of AI-Powered Digital
Educational Platforms on Students’ Learning and Teachers’ Practice in Abu Dhabi Schools
(Doctoral dissertation, The British University in Dubai).
33. Maghsudi, S., Lan, A., Xu, J., & van Der Schaar, M. (2021). Personalized
education in the artificial intelligence era: what to expect next. IEEE Signal Processing
Magazine, 38(3), 37-50.
34. Kurni, M., Mohammed, M. S., & Srinivasa, K. G. (2023). Chatbots for
Education. In A Beginner’s Guide to Introduce Artificial Intelligence in Teaching and
Learning (pp. 173-198). Cham: Springer International Publishing.
35. Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further
education: Identification of drivers, barriers, and business models of educational technology
companies. International Journal of Educational Technology in Higher Education, 17(1)
36. Eshbayev, O. A., Mirzaliev, S. M., Rozikov, R. U., Kuzikulova, D. M., &
Shakirova, G. A. (2022, June). NLP and ML based approach of increasing the efficiency of
environmental management operations and engineering practices. In IOP Conference
Series: Earth and Environmental Science (Vol. 1045, No. 1, p. 012058). IOP Publishing.
37. Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E.
(2019). Virtual and augmented reality effects on K-12, higher and tertiary education
students’ twenty-first century skills. Virtual Reality, 23, 425-436.
38. Ranjeeth, S., Latchoumi, T. P., & Paul, P. V. (2020). A survey on predictive
models of learning analytics. Procedia Computer Science, 167, 37-46.
39. Lantz, B. (2019). Machine learning with R: expert techniques for predictive
modeling. Packt publishing ltd.

21
40. Chauhan, N., Shah, K., Karn, D., & Dalal, J. (2019, April). Prediction of
student’s performance using machine learning. In 2nd International Conference on
Advances in Science & Technology (ICAST).
41. Obsie, E. Y., & Adem, S. A. (2018). Prediction of student academic
performance using neural network, linear regression and support vector regression: a case
study. International Journal of Computer Applications, 180(40), 39-47.
42. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification. In Proceedings
of the IEEE international conference on computer vision (pp. 1026-1034).
43. Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and
educational data mining in practice: A systematic literature review of empirical evidence.
Journal of Educational Technology & Society, 17(4), 49- 64.
44. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S.,
Barbado, A., … & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts,
taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-
115.
45. Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H. (2020).
Large-scale machine learning systems in real-world industrial settings: A review of
challenges and solutions. Information and software technology, 127, 106368.
46. Wendlandt, L., Mihalcea, R., Boyd, R. L., & Pennebaker, J. W. (2017,
September). Multimodal analysis and prediction of latent user dimensions. In
International Conference on Social Informatics (pp. 323-340). Cham: Springer International
Publishing.
47. Chan, T. W., Looi, C. K., Chen, W., Wong, L. H., Chang, B., Liao, C. C., … &
Ogata, H. (2018). Interest-driven creator theory: Towards a theory of learning design for
Asia in the twenty-first century. Journal of Computers in Education, 5, 435-461.
48. Rights, F. E., & Act, P. (2014). Family educational rights and privacy act
(FERPA).
49. Regulation, G. D. P. (2018). General data protection regulation
(GDPR). Intersoft Consulting, Accessed in October, 24(1).
50. Alier, M., Casañ Guerrero, M. J., Amo, D., Severance, C., & Fonseca, D.
(2021). Privacy and e-learning: A pending task. Sustainability, 13(16), 9206.
51. Thapa, S., & Mailewa, A. (2020, April). The role of intrusion
detection/prevention systems in modern computer networks: A review. In Conference:
Midwest Instruction and Computing Symposium (MICS) (Vol. 53, pp. 1-14).


Ahead of Print Subscription Review Article
Volume 03
01
Received 23/03/2026
Accepted 24/02/2026
Published 30/04/2026
Publication Time 38 Days


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