Optimizing Student Engagement and Performance usingAI-Enabled Educational Tools


  • Khaizure Mirdad Pandawan incorporation Author
  • Ora Plane Maria Daeli University of Raharja Author
  • Nanda Septiani University of Raharja Author
  • Anita Ekawati Bank Negara Indonesia Tbk Author
  • Umi Rusilowati Pamulang University Author


Artificial Intelligence (AI) , Technology-Based Learning , Student Engagement, Personalized Learning , Learning Efficiency


Education is the primary pillar in the progress of modern society. With the development of artificial intelligence (AI) technology, its potential to advance the learning process has become a major focus. This research focuses on the integration of AI-based educational tools to enhance student engagement and academic performance. Through experimental design with a control group, students were divided into two groups: one using AI tools while the other followed conventional methods. Students from various educational levels participated in this research. Data were collected through questionnaires and academic evaluations to compare the outcomes between the two groups. Data analysis was conducted using SmartPLS, enabling the evaluation of the impact of AI tools on student learning. The results indicate that AI integration enables a more personalized and responsive approach to the unique needs of students. It is expected that AI technology in education will bring significant changes in how students engage and achieve academic success. This research expands the understanding of the potential of AI in improving the education process. The integration of AI technology in learning is a progressive step toward a more adaptive and effective education system, preparing students for success in an increasingly connected and complex world


A. Korinek and J. E. Stiglitz, “Artificial intelligence, globalization, and strategies for economic development,” National Bureau of Economic Research, Tech. Rep., 2021.

A. Sestino and A. De Mauro, “Leveraging artificial intelligence in business: Implications, applications

and methods,” Technology Analysis & Strategic Management, vol. 34, no. 1, pp. 16–29, 2022.

G. Zeba, M. Dabic, M. ´ Ci ˇ cak, T. Daim, and H. Yalcin, “Technology mining: Artificial intelligence in ˇ

manufacturing,” Technological Forecasting and Social Change, vol. 171, p. 120971, 2021.

A. Becue, I. Prac¸a, and J. Gama, “Artificial intelligence, cyber-threats and industry 4.0: Challenges and ´

opportunities,” Artificial Intelligence Review, vol. 54, no. 5, pp. 3849–3886, 2021.

V. Galaz, M. A. Centeno, P. W. Callahan, A. Causevic, T. Patterson, I. Brass, S. Baum, D. Farber, J. Fischer, D. Garcia et al., “Artificial intelligence, systemic risks, and sustainability,” Technology in Society,

vol. 67, p. 101741, 2021.

L. N. Maulidia, S. Suparno, and U. J. Rosyidah, “A systematic literature review on technology-based

learning media in ece to face society 5.0 era,” Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, vol. 7,

no. 5, pp. 5181–5195, 2023.

Y. Liu, V. Sathishkumar, and A. Manickam, “Augmented reality technology based on school physical

education training,” Computers and Electrical Engineering, vol. 99, p. 107807, 2022.

T. M. L. Tran and T. T. H. Nguyen, “The impacts of technology-based communication on efl students’

writing,” AsiaCALL Online Journal, vol. 12, no. 5, pp. 54–76, 2021.

S.-U. Park, “Analysis of the status of natural language processing technology based on deep learning,”

The Journal of Bigdata, vol. 6, no. 1, pp. 63–81, 2021.

D. Immaniar, N. Azizah, D. Supriyanti, N. Septiani, and M. Hardini, “Pots: Proof of tunnel signature for

certificate based on blockchain technology,” International Journal of Cyber and IT Service Management,

vol. 1, no. 1, pp. 101–114, 2021.

G.-Y. Ban and N. B. Keskin, “Personalized dynamic pricing with machine learning: High-dimensional

features and heterogeneous elasticity,” Management Science, vol. 67, no. 9, pp. 5549–5568, 2021.

U. Rahardja, M. Ngadi, R. Budiarto, Q. Aini, M. Hardini, and F. P. Oganda, “Education exchange storage

protocol: Transformation into decentralized learning platform,” in Frontiers in Education. Frontiers,

, p. 477.

E. Olivier, B. Galand, A. J. Morin, and V. Hospel, “Need-supportive teaching and student engagement in

the classroom: Comparing the additive, synergistic, and global contributions,” Learning and Instruction,

vol. 71, p. 101389, 2021.

N. Septiani, A. S. Bist, C. S. Bangun, and E. Dolan, “Digital business student development for entrepreneurs with software,” Startupreneur Bisnis Digital, vol. 1, no. 1 April, pp. 33–43, 2022.

I. Bouchrika, N. Harrati, V. Wanick, and G. Wills, “Exploring the impact of gamification on student

engagement and involvement with e-learning systems,” Interactive Learning Environments, vol. 29, no. 8,

pp. 1244–1257, 2021.

T. Zacharias, Y. Yusriadi, H. Firman, and M. Rianti, “Poverty alleviation through entrepreneurship,” Journal of Legal, Ethical and Regulatory Issues, vol. 24, pp. 1–5, 2021.

S. A. Raza, W. Qazi, and S. Q. Yousufi, “The influence of psychological, motivational, and behavioral

factors on university students’ achievements: the mediating effect of academic adjustment,” Journal of

Applied Research in Higher Education, vol. 13, no. 3, pp. 849–870, 2021.

O. Marfoq, G. Neglia, R. Vidal, and L. Kameni, “Personalized federated learning through local memorization,” in International Conference on Machine Learning. PMLR, 2022, pp. 15 070–15 092.

B. Sun, H. Huo, Y. Yang, and B. Bai, “Partialfed: Cross-domain personalized federated learning via

partial initialization,” Advances in Neural Information Processing Systems, vol. 34, pp. 23 309–23 320,

A.-W. de Leeuw, S. van der Zwaard, R. van Baar, and A. Knobbe, “Personalized machine learning approach to injury monitoring in elite volleyball players,” European journal of sport science, vol. 22, no. 4,

pp. 511–520, 2022.

J. DeMink-Carthew and S. Netcoh, “Mixed feelings about choice: Exploring variation in middle school

student experiences with making choices in a personalized learning project,” in Dialogues in Middle Level

Education Research Volume 1. Routledge, 2022, pp. 73–105.

B. Whalley, D. France, J. Park, A. Mauchline, and K. Welsh, “Towards flexible personalized learning

and the future educational system in the fourth industrial revolution in the wake of covid-19,” Higher

Education Pedagogies, vol. 6, no. 1, pp. 79–99, 2021.

R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or, “An image is worth one word: Personalizing text-to-image generation using textual inversion,” arXiv preprint

arXiv:2208.01618, 2022.

Z. Fu, X. He, E. Wang, J. Huo, J. Huang, and D. Wu, “Personalized human activity recognition based on

integrated wearable sensor and transfer learning,” Sensors, vol. 21, no. 3, p. 885, 2021.

P. Bedi, S. Goyal, A. S. Rajawat, R. N. Shaw, and A. Ghosh, “A framework for personalizing atypical

web search sessions with concept-based user profiles using selective machine learning techniques,” in

Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021. Springer, 2022, pp.








How to Cite

Optimizing Student Engagement and Performance usingAI-Enabled Educational Tools. (2024). CORISINTA, 1(1), 53-60. https://journal.corisinta.org/index.php/corisinta/article/view/22