The Functions of Artificial intelligence technologies in higher education learning system: Aligning with the global evolution of learning

Namdarian, L.; Armanfar, M.R.; Khedmatgozar, H.R.

doi:10.22061/tej.2025.11335.3135

Articles in Press

Document Type : Original Research Paper

Authors

¹ Information Policy Research Group, Information and Society Research Department, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran

² Department of Information Technology Management, Faculty of Management & Economics, Tarbiat Modares University, Tehran, Iran

³ Information Technology Management Research Group, Information Technology Research Department, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran

https://doi.org/10.22061/tej.2025.11335.3135

Abstract

Background and Objectives: One of the most significant recent developments is the emergence of educational technologies and the integration of artificial intelligence (AI) into learning systems, particularly in higher education. AI has the potential to fundamentally transform the processes of teaching, learning, and knowledge acquisition, providing innovative solutions for improving the performance of learning systems. This research explores the key outcomes and new opportunities arising from the integration of AI technologies into higher education.
Methods: This study utilizes a systematic literature review of relevant academic papers to first identify the AI technologies used in learning, and then analyze their impacts and functions in higher education learning systems. The research was conducted in two main steps. The first step analyzes four major learning theories, including constructivism, connectivism, personalized learning, and transformative learning. Based on these theories, a set of AI technologies aligned with the principles of each theory were identified and introduced. In the second step, this research used thematic analysis to categorize the various functions of these technologies in higher education.
Findings: According to the results, AI technologies in learning systems include: Adaptive Learning Systems (ALS), which analyze learning data to adaptively adjust educational content for each student; Intelligent Tutoring Systems (ITS), which use intelligent algorithms to act as personal tutors for students; Learning Analytics (LA), which analyze students' learning data to enhance educational processes; AI-based Content Production and Management Systems (CPMS), which automatically produce and manage educational content; Plagiarism Detection and Citation Analysis Systems (PD&CA), which enable the evaluation of the authenticity and accuracy of research; Exploratory Learning Environments (ELEs), which allow students to engage in practical, exploratory learning experiences; and Intelligent Assistive Systems (IAS) for disabled students, which assist students with special needs in gaining better access to education. Some of the key functions of these technologies include personalized learning, which improves the learning experience by tailoring educational content to each student's needs; real-time and personalized feedback, which allows students to quickly view their performance and adjust their learning paths; automating assessments and improving teaching strategies and curricula, where AI helps optimize class assessments and course design; automatic and intelligent content creation and management; support for research and innovation, where AI tools help researchers analyze research data and achieve new findings; the creation of interactive virtual environments and immersive simulations that enable practical learning experiences in virtual settings; and making education accessible for all and supporting students with disabilities, where AI assistive technologies make education more accessible for all students, including those with disabilities.
Conclusion: The results of this study show that integrating AI into higher education learning systems can lead to improved educational processes, personalized learning, enhanced access to education for all students, and increased efficiency in the management and production of educational content. However, the successful implementation of these technologies requires attention to challenges and considerations, including maintaining human interaction in education, data privacy and ethics, and teaching new skills to both faculty and students. Therefore, policymakers and higher education administrators must plan in such a way that, in addition to utilizing the opportunities presented by AI, they also ensure the preservation of the human aspects of education and focus on sustainable learning and the personal development of students. Furthermore, it is necessary to strengthen technological infrastructure and foster a culture of embracing digital innovations in universities to fully utilize AI's potential. Finally, more research is needed to evaluate the long-term impacts of these technologies on the quality of education and the development of students' skills.

Keywords

Main Subjects

Technology-based learning environments

COPYRIGHTS
© 2025 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

References

[1] Popenici SA, Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanced Learn. 2017;12(1):1-13.

doi:10.1186/s41039-017-0044-2

[2] Bates AW. Teaching in a digital age: Guidelines for designing teaching and learning. Tony Bates Associates Ltd; 2015. doi:10.1007/s10834-020-09789-5

[3] Harasim L. Shift happens: Online education as a new paradigm in learning. The Internet and higher education. 2000;3(1-2):41-61. doi:10.1016/S1096-7516(00)00024-8

[4] Graham CR. Blended learning systems. In: The handbook of blended learning: Global perspectives, local designs. 2006;1:3-21. doi:10.1108/9781786354325-004

[5] Papanastasiou EC, Angeli C. Evaluating the use of ICT in education: Psychometric properties of the survey of factors affecting teachers teaching with technology (SFA-T3). Educ Technol Soc. 2008;11(1):69-86. doi:10.1007/s10639-007-9052-1

[6] Essa A, Ayad H. Improving student success using predictive models and data visualizations. Res Learn Technol. 2012;20. doi:10.3402/rlt.v20i0.17285

[7] Terras MM, Ramsay J. A psychological perspective on mobile learning. In: Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global; 2018. p. 6398-411. doi:10.4018/978-1-5225-2255-3.ch139

[8] Pappano L. The year of the MOOC. New York Times. 2012;2(12):2012. doi:10.1001/jama.2013.3931

[9] Schwab K. The fourth industrial revolution. World Economic Forum; 2016. doi:10.1142/9789813274257_0002

[10] Ciolacu M, Tehrani AF, Binder L, Svasta PM. Education 4.0-Artificial intelligence assisted higher education: Early recognition system with machine learning to support students' success. In: 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME). 2018 Oct;23-30. doi:10.1109/SIITME.2018.8619731

[11] Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int J Educ Technol High Educ. 2019;16(1):1-27.

doi:10.1186/s41239-019-0171-0

[12] Luckin R, Holmes W. Intelligence unleashed: An argument for AI in education. London: Pearson; 2016.

doi:10.1007/978-3-319-33376-4

[13] Roll I, Wylie R. Evolution and revolution in artificial intelligence in education. Int J Artif Intell Educ. 2016;26:582-99. doi:10.1007/s40593-016-0013-0

[14] Chen L, Chen P, Lin Z. Artificial intelligence in education: A review. IEEE Access. 2020;8:75264-78.

doi:10.1109/ACCESS.2020.2985164

[15] Xie H, Chu HC, Hwang GJ, Wang CC. Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Comput Educ. 2019;140:103599.

doi:10.1016/j.compedu.2019.103599

[16] Srivastava SK. Artificial Intelligence: Way forward for India. JISTEM-Journal of Information Systems and Technology Management. 2018;15:e201815004.

doi:10.4301/S1679-43912018150001

[17] Luan H, Tsai CC. A review of using machine learning approaches for precision education. Educ Technol Soc. 2021;24(1):250-66. doi:10.1007/s10639-021-10404-8

[18] Ertmer PA, Newby TJ. Learning theory and technology: A reciprocal relationship. The Wiley handbook of learning technology. 2016;58-76. doi:10.1002/9781119217872.ch3

[19] Nkambou R, Mizoguchi R, Bourdeau J, editors. Advances in intelligent tutoring systems. Vol. 308. Springer Science & Business Media; 2010. doi:10.1007/978-1-4419-0416-0

[20] Ouyang F, Jiao P. Artificial intelligence in education: The three paradigms. Comput Educ Artif Intell. 2021;2:100020. doi:10.1016/j.caeai.2021.100020

[21] Edelson DC, Gordin DN, Pea RD. Addressing the challenges of inquiry-based learning through technology and curriculum design. J Learn Sci. 1999;8(3-4):391-450.

doi:10.1207/s15327809jls0803&4_4.

[22] Siemens G. Connectivism: A learning theory for the digital age. Int J Instructional Technol Dist Learn [Internet]. 2005 [cited 2024 Oct 5].

[23] Downes S. Places to go: Connectivism & connective knowledge. Innovate: J Online Educ. 2008;5(1):6. doi:10.4172/2151-6200.1000291

[24] Zhang Y, Xu X, Zhang M, Cai N, Lei VNL. Personal Learning Environments and Personalized Learning in the Education Field: Challenges and Future Trends. In: Applied Degree Education and the Shape of Things to Come. Singapore: Springer Nature Singapore; 2023. p. 231-47.

doi:10.1007/978-981-19-6001-2_15

[25] Bray B, McClaskey K. Make learning personal: The what, who, wow, where, and why. Corwin Press; 2014. doi:10.1016/j.learninstruc.2017.01.005

[26] Truong HM. Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Comput Human Behav. 2016; 55: 1185-93. doi:10.1016/j.chb.2015.10.018

[27] Mezirow J. Transformative learning. New Dir Adult Contin Educ. 1997;74:5-12. doi:10.1002/ace.7401

[28] Nye BD. Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context. Int J Artif Intell Educ. 2015;25:177-203. doi:10.1007/s40593-015-0031-0

[29] Winkler R, Söllner M. Unleashing the potential of chatbots in education: A state-of-the-art analysis. In: Academy of Management Proceedings; 2018 Jul; Vol. 2018, No. 1, p. 15903. Briarcliff Manor, NY: Academy of Management. doi:10.5465/ambpp.2018.15903abstract

[30] Taylor EW, Cranton P. The handbook of transformative learning: Theory, research, and practice. John Wiley & Sons; 2012. doi:10.1002/9781118545016

[31] Alzahrani SM, Salim N, Abraham A. Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Trans Syst Man Cybern Part C Appl Rev. 2011;42(2):133-49. doi:10.1109/TSMCC.2011.2101445

[32] Drigas A, Rodi EI. Special education and ICTs. Int J Emerg Technol Learn. 2013;8(2):41. doi:10.3991/ijet.v8i2.2411

[33] Manouselis N, Drachsler H, Vuorikari R, Hummel H, Koper R. Recommender systems in technology enhanced learning. In: Recommender Systems Handbook. Springer; 2011. p. 387-415. doi:10.1007/978-0-387-85820-3_11

[34] Jingning L. Speech recognition based on mobile sensor networks application in English education intelligent assisted learning system. Measurement: Sensors. 2024; 32: 101084. doi:10.1016/j.measensors.2024.101084

[35] Pardo A, Siemens G. Ethical and privacy principles for learning analytics. Br J Educ Technol. 2014;45(3):438-50. doi:10.1111/bjet.12052

[36] Huang J, Saleh S, Liu Y. A review on artificial intelligence in education. Acad J Interdiscip Stud. 2021; 10(3). doi:10.36941/ajis-2021-0053

[37] Renz A, Hilbrich R. Prerequisites for artificial intelligence in further education: Identification of drivers, barriers, demands, and expectations. Int J Educ Technol High Educ. 2020;17(1):1-21. doi:10.1186/s41239-020-00196-5

[38] Litman D, Forbes-Riley K. Correlations between dialogue acts and learning in spoken tutoring dialogues. Nat Lang Eng. 2006;12(2):161-76. doi:10.1017/S1351324906003966

[39] Corbett AT, Anderson JR. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Model User-Adapt Interact. 1994;4:253-78. doi:10.1007/BF01099816

[40] Romero C, Ventura S, Pechenizkiy M, Baker RS, editors. Handbook of educational data mining. CRC Press; 2010. doi:10.1201/b10239

[41] VanLehn K. The behavior of tutoring systems. Int J Artif Intell Educ. 2006;16(3):227-65. doi:10.1007/BF02932098

[42] Kerly A, Hall P, Bull S. Bringing chatbots into education: Towards natural language negotiation of open learner models. Knowl-Based Syst. 2007;20(2):177-85.

doi:10.1016/j.knosys.2006.09.005

[43] Kurni M, Mohammed MS, Srinivasa KG. Natural language processing for education. In: A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning. Cham: Springer International Publishing; 2023. p. 45-54.

doi:10.1007/978-3-031-28026-4_5

[44] Goel AK, Polepeddi L. Jill Watson: A virtual teaching assistant for online education. In: Learning engineering for online education. 2018;120-43.

doi:10.1007/978-3-319-90582-2_7

[45] Malik G, Tayal DK, Vij S. An analysis of the role of artificial intelligence in education and teaching. In: Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017; 2019; Volume 1. Singapore: Springer; 2019. p. 407-17. doi:10.1007/978-981-13-1278-3_39

[46] Fitria TN. Artificial intelligence (AI) in education: Using AI tools for teaching and learning process. In: Prosiding Seminar Nasional & Call for Paper STIE AAS. 2021 Dec;134-47. doi:10.1109/ISD2021.9631492

[47] Polson MC, Richardson JJ. Foundations of intelligent tutoring systems. Psychology Press; 2013.

doi:10.4324/9781315809274

[48] Graesser AC, Chipman P, Haynes BC, Olney A. AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Trans Educ. 2005;48(4):612-8. doi:10.1109/TE.2005.859134

[49] Mitrovic A, Mayo M, Suraweera P, Martin B. Constraint-based tutors: A success story. In: Engineering of Intelligent Systems: 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems; 2001 Jun 4-7; Budapest, Hungary. Springer; 2001. p. 931-40. doi:10.1007/978-3-642-13525-8_80

[50] Anderson JR, Corbett AT, Koedinger KR, Pelletier R. Cognitive tutors: Lessons learned. J Learn Sci. 1995;4(2):167-207. doi:10.1207/s15327809jls0402_3

[51] Sun Y, Li Z. A multi-agent intelligent tutoring system. In: 2009 4th International Conference on Computer Science & Education; 2009 Jul; pp. 1724-8. IEEE.

doi:10.1109/ICCSE.2009.5224013

[52] Aguiar E, Chawla NV, Brockman J, Ambrose GA, Goodrich V. Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge. 2014 Mar;103-12. doi:10.1145/2567574.2567575

[53] Papamitsiou Z, Economides AA. Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. J Educ Technol Soc. 2014;17(4):49-64. doi:10.1007/978-3-319-74044-4_9

[54] Rienties B, Toetenel L, Bryan A. "Scaling up" learning design: Impact of learning design activities on LMS behavior and performance. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge; 2015 Mar; pp. 315-9. doi:10.1145/2723576.2723607

[55] Burrows S, Gurevych I, Stein B. The eras and trends of automatic short answer grading. Int J Artif Intell Educ. 2015;25:60-117. doi:10.1007/s40593-015-0021-4.

[56] Ramesh D, Sanampudi SK. An automated essay scoring system: A systematic literature review. Artif Intell Rev. 2022;55(3):2495-527. doi:10.1007/s10462-021-10093-4

[57] Rad HS, Alipour R, Jafarpour A. Using artificial intelligence to foster students’ writing feedback literacy, engagement, and outcome: A case of Wordtune application. Interact Learn Environ. 2023;1-21. doi:10.1080/10494820.2023.2181710

[58] Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, bin Saleh K, et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res Soc Adm Pharm. doi:10.1016/j.sapharm.2023.08.013

[59] Pallathadka H, Wenda A, Ramirez-Asís E, Asís-López M, Flores-Albornoz J, Phasinam K. Classification and prediction of student performance data using various machine learning algorithms. Mater Today Proc. 2023;80:3782-5. doi:10.1016/j.matpr.2023.01.214

[60] Agudo-Peregrina ÁF, Iglesias-Pradas S, Conde-González MÁ, Hernández-García Á. Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Comput Hum Behav. 2014; 31: 542-50. doi:10.1016/j.chb.2013.05.002

[61] Bienkowski M, Feng M, Means B. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Office of Educational Technology, US Department of Education; 2012. doi:10.1037/e587482013-001

[62] Roscoe RD, Allen LK, Weston JL, Crossley SA, McNamara DS. The Writing Pal intelligent tutoring system: Usability testing and development. Comput Compos. 2014; 34: 39-59. doi:10.1016/j.compcom.2014.03.005

[63] Wen M, Yang D, Rose C. Sentiment analysis in MOOC discussion forums: What does it tell us? In: Educational Data Mining; 2014 Jul. doi:10.1145/2614321.2614334

[64] Raca M, Dillenbourg P. System for assessing classroom attention. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge; 2013 Apr; pp. 265-9. doi:10.1145/2460296.2460372

[65] Baral S, Santhanam A, Botelho A, Gurung A, Heffernan N. Automated scoring of image-based responses to open-ended mathematics questions. In: The Proceedings of the 16th International Conference on Educational Data Mining. 2023 Jul. doi:10.5281/zenodo.7211607

[66] Tsintsifas A. A framework for the computer-based assessment of diagram-based coursework [PhD thesis]. University of Nottingham; 2002.

doi:10.13140/RG.2.2.26608.35849