Artificial Intelligence Adoption in Learning Systems: Systematic Literature Review

Hendi Sama, Aisyah Nurkayla, Stefanus Eko Prasetyo

Abstract


The integration of Artificial Intelligence (AI) in education has accelerated rapidly, reshaping teaching and learning processes through intelligent, adaptive, and data-driven systems. Despite its widespread adoption, a consolidated understanding of implementation trends, benefits, and challenges across educational contexts remains limited. This study aims to examine the current state of AI adoption in education by identifying key applications, success factors, and implementation barriers. A systematic literature review (SLR) was conducted by analyzing 32 peer-reviewed journal articles published within the last five years and indexed in reputable academic databases. The reviewed studies focus on adaptive learning systems, intelligent tutoring systems, and learning analytics. The findings demonstrate that AI contributes significantly to personalized learning, real-time performance assessment, and improved learner engagement. However, effective implementation is strongly influenced by institutional readiness, educator digital literacy, data quality, and ethical governance. Major challenges identified include data privacy concerns, lack of standardization, and unequal access to technological infrastructure. This study concludes that AI has substantial potential to support more adaptive and inclusive educational systems, but sustainable integration requires coordinated efforts among educators, policymakers, and technology developers.


Keywords


Artificial Intelligence; Education Technology; Adaptive Learning; Intelligent Tutoring System; Learning Analytics

Full Text:

PDF

References


H. E. Sari, B. Tumanggor, and D. Efron, “Improving Educational Outcomes Through Adaptive Learning Systems using AI,” Int. Trans. Artif. Intell., vol. 3, no. 1, pp. 21–31, Nov. 2024, doi: 10.33050/italic.v3i1.647. Sari, H. E., Tumanggor, B., & Efron, D. (2024). Improving Educational Outcomes Through Adaptive Learning Systems using AI. International Transactions on Artificial Intelligence (ITALIC), 3(1), 21–31. https://doi.org/10.33050/italic.v3i1.647

M. Nasser, “Personalized Learning through AI: Enhancing Student Engagement and Teacher Effectiveness,” Int. J. Teaching, Learn. Educ., vol. 3, no. 6, pp. 23–26, 2024, doi: 10.22161/ijtle.3.6.4.

Y. Christian, Y. H. Choo, and N. F. A. B. D. Yusof, “Systematic Literature Review on the Use of Machine Learning in Online Learning in the Context of Skill Achievement,” J. Theor. Appl. Inf. Technol., vol. 102, no. 6, pp. 2466–2479, 2024, [Online]. Available: https://www.jatit.org/volumes/Vol102No6/19Vol102No6.pdf

K. Woodruff, J. Hutson, and K. Arnone, “Perceptions and Barriers to Adopting Artificial Intelligence in K-12 Education: A Survey of Educators in Fifty States,” in Reimagining Education - The Role of E-Learning, Creativity, and Technology in the Post-Pandemic Era, vol. 506, IntechOpen, 2023. doi: 10.5772/intechopen.1002741.

Kardina Engelina Siregar, “Increasing Digital Literacy In Education: Analysis Of Challenges And Opportunities Through Literature Study,” Int. J. Multiling. Educ. Appl. Linguist., vol. 1, no. 2, pp. 10–25, 2024, doi: 10.61132/ijmeal.v1i2.18.

H. Sama, T. Wibowo, and Tukino, “Analysis of the Use of Distance Learning Technology in Universities in the Riau Islands Province with the Technology Acceptance Model (TAM),” J. Penelit. Pendidik. IPA, vol. 9, no. 12, pp. 11070–11077, 2023, doi: 10.29303/jppipa.v9i12.5836.

S. Panjwani-Charania and X. Zhai, “AI for Students with Learning Disabilities,” in Uses of Artificial Intelligence in STEM Education, Oxford University PressOxford, 2024, pp. 469–493. doi: 10.1093/oso/9780198882077.003.0021.

C.-C. Lin, A. Y. Q. Huang, and O. H. T. Lu, “Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review,” Smart Learn. Environ., vol. 10, no. 1, p. 41, Aug. 2023, doi: 10.1186/s40561-023-00260-y.

S. Akter et al., “Algorithmic bias in data-driven innovation in the age of AI,” Int. J. Inf. Manage., vol. 60, p. 102387, Oct. 2021, doi: 10.1016/j.ijinfomgt.2021.102387.

M. Akintolu and A. A. Oyekunle, “Data-Driven Decision-Making: Utilising Ai-Powered Learning Analytics To Make Informed Primary Educators’ Decisions,” J. Educ. Online, vol. 22, no. 3, 2025, doi: 10.9743/JEO.2025.22.3.1.

M. Shahvaroughi Farahani and G. Ghasemi, “Artificial Intelligence and Inequality: Challenges and Opportunities,” Feb. 21, 2024. doi: 10.32388/7HWUZ2.

W. Mengist, T. Soromessa, and G. Legese, “Method for conducting systematic literature review and meta-analysis for environmental science research,” MethodsX, vol. 7, p. 100777, 2020, doi: 10.1016/j.mex.2019.100777.

J. Sun et al., “A catalyst for education? A study on the impact of artificial intelligence assisted learning in painting courses on college students’ continuous learning intention,” Acta Psychol. (Amst)., vol. 258, p. 105130, 2025, doi: https://doi.org/10.1016/j.actpsy.2025.105130.

Z. Hu, “A method for generating personalized learning content based on AIGC,” Sustain. Futur., vol. 10, p. 101331, 2025, doi: https://doi.org/10.1016/j.sftr.2025.101331.

M. A. Kia and A. Koliousis, “ACKT : Self-Attentive Convolutional Model for Knowledge Tracing,” Procedia Comput. Sci., vol. 246, pp. 2723–2732, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.399.

A. C. M. Yang, B. Flanagan, and H. Ogata, “Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning,” Comput. Educ. Artif. Intell., vol. 3, p. 100104, 2022, doi: https://doi.org/10.1016/j.caeai.2022.100104.

C. Ferguson, E. L. van den Broek, and H. van Oostendorp, “AI-Induced guidance: Preserving the optimal Zone of Proximal Development,” Comput. Educ. Artif. Intell., vol. 3, p. 100089, 2022, doi: https://doi.org/10.1016/j.caeai.2022.100089.

J. Liu, D. Sun, J. Sun, J. Wang, and P. L. H. Yu, “Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction,” Comput. Educ. Artif. Intell., vol. 9, p. 100438, 2025, doi: https://doi.org/10.1016/j.caeai.2025.100438.

R. M. Al-Bogami and N. A. Alahmadi, “Effects of an AI-based reading progress tool on third-grade EFL learners’ oral reading fluency,” Comput. Educ. Open, vol. 9, p. 100283, 2025, doi: https://doi.org/10.1016/j.caeo.2025.100283.

J. C.-Y. Sun, H.-E. Tsai, and W. K. R. Cheng, “Effects of integrating an open learner model with AI-enabled visualization on students’ self-regulation strategies usage and behavioral patterns in an online research ethics course,” Comput. Educ. Artif. Intell., vol. 4, p. 100120, 2023, doi: https://doi.org/10.1016/j.caeai.2022.100120.

O. Alsaiari, N. Baghaei, H. Lahza, J. M. Lodge, M. Boden, and H. Khosravi, “Emotionally enriched AI-generated feedback: Supporting student well-being without compromising learning,” Comput. Educ., vol. 239, p. 105363, 2025, doi: https://doi.org/10.1016/j.compedu.2025.105363.

S. A. Alshaya, “Enhancing Educational Materials: Integrating Emojis and AI Models into Learning Management Systems,” Comput. Mater. Contin., vol. 83, no. 2, pp. 3075–3095, 2025, doi: https://doi.org/10.32604/cmc.2025.062360.

J. Kay, K. Bartimote, K. Kitto, B. Kummerfeld, D. Liu, and P. Reimann, “Enhancing learning by Open Learner Model (OLM) driven data design,” Comput. Educ. Artif. Intell., vol. 3, p. 100069, 2022, doi: https://doi.org/10.1016/j.caeai.2022.100069.

A. C. M. Yang, J.-Y. Lin, C.-Y. Lin, and H. Ogata, “Enhancing python learning with PyTutor: Efficacy of a ChatGPT-Based intelligent tutoring system in programming education,” Comput. Educ. Artif. Intell., vol. 7, p. 100309, 2024, doi: https://doi.org/10.1016/j.caeai.2024.100309.

E. S. Adabor, E. Addy, N. Assyne, and E. Antwi-Boasiako, “Enhancing sustainable academic course delivery using AI in technical universities: An empirical analysis using adaptive learning theory,” Sustain. Futur., vol. 10, p. 100828, 2025, doi: https://doi.org/10.1016/j.sftr.2025.100828.

G. Yu, Z. Xie, G. Zhou, Z. Zhao, and J. X. Huang, “Exploring long- and short-term knowledge state graph representations with adaptive fusion for knowledge tracing,” Inf. Process. Manag., vol. 62, no. 3, p. 104074, 2025, doi: https://doi.org/10.1016/j.ipm.2025.104074.

J. Fan and Q. Zhang, “From literacy to learning: The sequential mediation of attitudes and enjoyment in AI-assisted EFL education,” Heliyon, vol. 10, no. 17, p. e37158, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e37158.

E. Pogorskiy and J. F. Beckmann, “From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning,” Comput. Educ. Artif. Intell., vol. 4, p. 100111, 2023, doi: https://doi.org/10.1016/j.caeai.2022.100111.

J. Wu, Y. Wang, F. Chen, X. Yin, and Y. He, “Impact of AI-Powered Adaptive Learning Platforms on English Reading Proficiency: Evidence From Structural Equation Modeling,” IEEE Access, vol. 13, pp. 88230–88242, 2025, doi: 10.1109/ACCESS.2025.3571055.

J. Katona and K. I. K. Gyonyoru, “Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes,” Comput. Educ. Artif. Intell., vol. 8, p. 100392, 2025, doi: https://doi.org/10.1016/j.caeai.2025.100392.

M. Alier, J. Pereira, F. J. García-Peñalvo, M. J. Casañ, and J. Cabré, “LAMB: An open-source software framework to create artificial intelligence assistants deployed and integrated into learning management systems,” Comput. Stand. Interfaces, vol. 92, p. 103940, 2025, doi: https://doi.org/10.1016/j.csi.2024.103940.

R. Boehme, S. Coors, P. Oster, M. Munser-Kiefer, and S. Hilbert, “Machine learning for spelling acquisition: How accurate is the prediction of specific spelling errors in German primary school students?,” Comput. Educ. Artif. Intell., vol. 6, p. 100233, 2024, doi: https://doi.org/10.1016/j.caeai.2024.100233.

R. Gutierrez, W. E. Villegas-Ch, A. M. Navarro, and S. Luján-Mora, “Optimizing Problem-Solving in Technical Education: An Adaptive Learning System Based on Artificial Intelligence,” IEEE Access, vol. 13, pp. 61350–61367, 2025, doi: 10.1109/ACCESS.2025.3557281.

A. Al-Badi, A. Khan, and Eid-Alotaibi, “Perceptions of Learners and Instructors towards Artificial Intelligence in Personalized Learning,” Procedia Comput. Sci., vol. 201, pp. 445–451, 2022, doi: https://doi.org/10.1016/j.procs.2022.03.058.

I. Pesovski, P. Jolakoski, V. Trajkovik, Z. Kubincova, and M. A. Herzog, “Predicting student achievement through peer network analysis for timely personalization via generative AI,” Comput. Educ. Artif. Intell., vol. 8, p. 100430, 2025, doi: https://doi.org/10.1016/j.caeai.2025.100430.

R. Lamb, K. Neumann, and K. A. Linder, “Real-time prediction of science student learning outcomes using machine learning classification of hemodynamics during virtual reality and online learning sessions,” Comput. Educ. Artif. Intell., vol. 3, p. 100078, 2022, doi: https://doi.org/10.1016/j.caeai.2022.100078.

A. Ghimire and Y. Qiu, “Redefining pedagogy with artificial intelligence: How nursing students are shaping the future of learning,” Nurse Educ. Pract., vol. 84, p. 104330, 2025, doi: https://doi.org/10.1016/j.nepr.2025.104330.

[

C.-F. Buciuman and S. Potra, “Revolutionizing Education in Industry 4.0: Eye-Tracking and AI for Personalized Learning,” Procedia Comput. Sci., vol. 253, pp. 1658–1667, 2025, doi: https://doi.org/10.1016/j.procs.2025.01.228.

C. Li, W. Xing, Y. Song, and B. Lyu, “RICE AlgebraBot: Lessons learned from designing and developing responsible conversational AI using induction, concretization, and exemplification to support algebra learning,” Comput. Educ. Artif. Intell., vol. 8, p. 100338, 2025, doi: https://doi.org/10.1016/j.caeai.2024.100338.

Y. Hayashi, S. Shimojo, and T. Kawamura, “Scripted interventions versus reciprocal teaching in collaborative learning: A comparison of pedagogical and teachable agents using a cognitive architecture,” Learn. Instr., vol. 96, p. 102057, 2025, doi: https://doi.org/10.1016/j.learninstruc.2024.102057

R. Yilmaz et al., “Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal,” Comput. Educ. Artif. Intell., vol. 3, p. 100092, 2022, doi: https://doi.org/10.1016/j.caeai.2022.100092.

M. N. Hasnine, H. T. T. Bui, T. T. Thu Tran, H. T. Nguyen, G. Akçapınar, and H. Ueda, “Students’ emotion extraction and visualization for engagement detection in online learning,” Procedia Comput. Sci., vol. 192, pp. 3423–3431, 2021, doi: https://doi.org/10.1016/j.procs.2021.09.115.

N. Lo, A. Wong, and S. Chan, “The impact of generative AI on essay revisions and student engagement,” Comput. Educ. Open, vol. 9, p. 100249, 2025, doi: https://doi.org/10.1016/j.caeo.2025.100249.

D. Castellanos-Reyes, L. Olesova, and A. Sadaf, “Transforming online learning research: Leveraging GPT large language models for automated content analysis of cognitive presence,” Internet High. Educ., vol. 65, p. 101001, 2025, doi: https://doi.org/10.1016/j.iheduc.2025.101001.




DOI: https://doi.org/10.31326/jisa.v9i1.2705

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Hendi Sama, Aisyah Nurkayla, Stefanus Eko Prasetyo

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: English
APC: The Journal Charges Fees for Publishing 
Indexing:  EBSCO , DOAJ, Google Scholar, Arsip Relawan Jurnal Indonesia, Directory of Research Journals Indexing, Index Copernicus International, PKP Index, Science and Technology Index (SINTA, S4) , Garuda Index
OAI address: http://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contact: jisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal Indonesia, Jurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 

JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.