Artificial intelligence in education - State of the art

Authors

  • Aymane Ezzaim Laboratory of Information Technologies National School of Applied Sciences, Chouaib Doukkali University
  • Fouad Kharroubi Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
  • Aziz Dahbi Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
  • Abdelhak Aqqal Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
  • Abdelfatteh Haidine Laboratory of Information Technologies, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco

Keywords:

Artificial intelligence, Education, AIED, AI in Education

Abstract

Information and communication technologies (ICT), e-learning, mobile learning hypermedia have considerably improved education, but today artificial intelligence offers us a variety of possibilities that we were previously unaware of and leading us to a new revolution known as Education 4.0. This article presents a literature review of journal and research articles in artificial intelligence in the field of education (AIEd) published between 2019 and 2021 on the scientific database ScienceDirect. Through a bibliometric selection based on selective criteria, we were able to highlight the most requested AIEd technologies and their applications. We also talked about real-world examples of how AIEd tools can be used in many educational contexts and disciplines. This research can serve as a starting point for future research to be aware of trends in AIEd applications and future directions.

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References

Whitby, Artificial Intelligence: A Beginner’s Guide, Oxford, U.K.:Oneworld, 2008.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, Article 100002.

Zhang, Ke, and Ayse Begum Aslan. “AI Technologies for Education: Recent Research & Future Directions.” Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, p. 100025, doi:10.1016/j.caeai.2021.100025.

Bieri, Peter. “Thinking Machines: Some Reflections on the Turing Test.” Poetics Today, vol. 9, no. 1, 1988, pp. 163–86, doi:10.2307/1772893.

Copeland, B. Jack. “The Turing Test*.” Minds and Machines, vol. 10, no. 4, Nov. 2000, pp. 519–39, doi:10.1023/A:1011285919106.

Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann Publishers, Inc.

Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website: https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

B. Coppin, Artificial Intelligence Illuminated. Boston, MA, USA: Jones and Bartlett, 2004.

M. Chassignol, A. Khoroshavin, A. Klimova, and A. Bilyatdinova, ‘‘Artificial intelligence trends in education: A narrative overview,’’ Procedia Comput. Sci., vol. 136, pp. 16–24, Jan. 2018.

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. (RPTEL), 12(22), 1e13.

S. Pokrivcakova, ‘‘Preparing teachers for the application of AI-powered technologies in foreign language education,’’ J. Lang. Cultural Edu., vol. 7, no. 3, pp. 135–153, Dec. 2019.

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., et al. (2016). Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel. Stanford University, Stanford, CA. Retrieved from: http://ai100.stanford.edu/2016-report.

Artificial Intelligence in Education | Center for Curriculum Redesign. 30 Oct. 2018, https://curriculumredesign.org/our-work/artificial-intelligence-in-education/.

Samuel, A.L. (1959) Some Studies in Machine Learning Using the Game of Checkers, IBM Journal of Research and Development, vol. 3, no. 3, pp. 210-229, doi: 10.1147/rd.33.0210

Kohavi, R., & Provost, F. (1998). Glossary of terms journal of machine learning. In Obtido.

Mitchell, T. (1997). Machine learning. WBC/McGraw-Hill, Boston: MA

Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229.

Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197–387.

Bengio, Y., Lee, D.-H., Bornschein, J., Mesnard, T., & Lin, Z. (2015). Towards biologically plausible deep learning. arXiv preprint arXiv:1502.04156.

Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329.

A comprehensive list of the algorithms available on one of the leading “AI as a service” platforms, Microsoft Azure, is available at http://download.microsoft.com/download/A/6/1/A613E11E-8F9C-424AB99D-65344785C288/microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf

Walczak, Steven. “Artificial Neural Networks.” Encyclopedia of Information Science and Technology, Fourth Edition, 2018, doi:10.4018/978-1-5225-2255-3.ch011.

Chan, C., Chow, C., Wong, J., Dimakis, N., Nayler, D., Bermudes, J., Raman, J., Lam, R., & Baker, M., (2017). Artificial intelligence applications in financial services.

Tadapaneni, Narendra Rao. “Artificial Intelligence in Finance and Investments.” International Journal of Innovative Research in Science, Engineering and Technology, vol. 9, no. 5, 2019.

Qiao, Chang, et al. Analysis on The Development of AI Clothing Marketing. Atlantis Press, 2019, pp. 34–37, doi:10.2991/icssed-19.2019.7.

Lin, D. Y., Blumenkranz, M. S., Brothers, R. J. & Grosvenor, D. M. Te sensitivity and specifcity of single-feld nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am. J. Ophthalmol. 134, 204–213 (2002)

Van Ginneken, B., Setio, A. A., Jacobs, C. & Ciompi, F. Of-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In IEEE 12th International Symposium Biomedical Imaging (ISBI) 286–289 (IEEE, 2015).

Esteva, A. et al. Dermatologist-level classifcation of skin cancer with deep neural networks. Nature 542, 115–118 (2017)

Yu, Kun-Hsing, et al. “Artificial Intelligence in Healthcare.” Nature Biomedical Engineering, vol. 2, no. 10, Oct. 2018, pp. 719–31, doi:10.1038/s41551-018-0305-z.

Chen, L., et al. “Artificial Intelligence in Education: A Review.” IEEE Access, vol. 8, 2020, pp. 75264–78, doi:10.1109/ACCESS.2020.2988510.

H. Sutton, ‘‘Minimize online cheating through proctoring, consequences,’’ Recruiting Retaining Adult Learners, vol. 21, no. 5, pp. 1–5, Jan. 2019.

D. Crowe, M. LaPierre, and M. Kebritchi, ‘‘Knowledge based artificial augmentation intelligence technology: Next step in academic instructional tools for distance learning,’’ TechTrends, vol. 61, no. 5, pp. 494–506, Jul. 2017.

R. F. Murphy, ‘‘Artificial intelligence applications to support K–1 2 teachers and teaching,’’ RAND Corp., Santa Monica, CA, USA, Tech. Rep. PE135, 2019, doi: 10.7249/PE315.

T. Yi-Shan and D. Gasevic, ‘‘Learning analytics in higher education—Challenges and policies: A review of eight learning analytics policies,’’ in Proc. 7th Int. Learn. Anal. Knowl. Conf. Mar. 2017, pp. 233–242.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education.

Ross, P. (1987). Intelligent tutoring systems. J. Comput. Assist. Learn., 3, 194e203.

Hwang, G. (2003). A conceptual map model for developing Intelligent Tutoring Systems. Comput. Educ., 40(3), 217e235. https://doi:10.1016/S0360-1315(02)00121-5

Cooper, G., Park, H., Nasr, Z., Thong, L. P., & Johnson, R. (2019). Using virtual reality in the classroom: preservice teachers’ perceptions of its use as a teaching and learning tool. Educ. Media Int., 56(1), 1e13. https://doi.org/10.1080/09523987.2019.1583461

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. (RPTEL), 12(22), 1e13.

Guan, Chong, et al. “Artificial Intelligence Innovation in Education: A Twenty-Year Data-Driven Historical Analysis.” International Journal of Innovation Studies, vol. 4, no. 4, Dec. 2020, pp. 134–47, doi:10.1016/j.ijis.2020.09.001.

Hwang, G. J. (2014). Definition, framework, and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.

Hwang, Gwo-Jen, et al. “Vision, Challenges, Roles and Research Issues of Artificial Intelligence in Education.” Computers and Education: Artificial Intelligence, vol. 1, Jan. 2020, p. 100001, doi:10.1016/j.caeai.2020.100001.

Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers & Education: Artificial Intelligence, 1, Article 100005

Ouyang, Fan, and Pengcheng Jiao. “Artificial Intelligence in Education: The Three Paradigms.” Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, p. 100020, doi:10.1016/j.caeai.2021.100020.

H. Rostaing, La bibliométrie et ses techniques, Outils et méthode, 1996.

ScienceDirect.Com | Science, Health and Medical Journals, Full Text Articles and Books. https://www.sciencedirect.com/. Accessed 25 Nov. 2021.

About ScienceDirect | Premier Platform for Discovering Peer-Reviewed Scientific, Technical and Medical Information | Elsevier. https://www.elsevier.com/solutions/sciencedirect. Accessed 3 June 2022.

English Education Game Using Non-Player Character Based on Natural Language Processing - ScienceDirect. https://www.sciencedirect.com/science/article/pii/S1877050919318691. Accessed 4 Dec. 2021.

Rekh, Shobha, and Abraham Chandy. “Implementation of Academia 4.0 for Engineering College Education.” Procedia Computer Science, vol. 172, Jan. 2020, pp. 673–78, doi:10.1016/j.procs.2020.05.088.

Kanuru, Srii Laasya, and Priyaadharshini M. “Lifelong Learning in Higher Education Using Learning Analytics.” Procedia Computer Science, vol. 172, Jan. 2020, pp. 848–52, doi:10.1016/j.procs.2020.05.122.

Yang, Stephen J. H., et al. “Human-Centered Artificial Intelligence in Education: Seeing the Invisible through the Visible.” Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, p. 100008, doi:10.1016/j.caeai.2021.100008.

Chatbots Applications in Education: A Systematic Review - ScienceDirect. https://www.sciencedirect.com/science/article/pii/S2666920X21000278. Accessed 4 Dec. 2021.

Ara Shaikh, Asmat, et al. “The Role of Machine Learning and Artificial Intelligence for Making a Digital Classroom and Its Sustainable Impact on Education during Covid-19.” Materials Today: Proceedings, Sept. 2021, doi:10.1016/j.matpr.2021.09.368.

Nazari, Nabi, et al. “Application of Artificial Intelligence Powered Digital Writing Assistant in Higher Education: Randomized Controlled Trial.” Heliyon, vol. 7, no. 5, May 2021, p. e07014, doi:10.1016/j.heliyon.2021.e07014.

Kose, ¨ U., & Arslan, A. (2016). Intelligent e-learning system for improving students’ academic achievements in computer programming courses. International Journal of Engineering Education, 32, 185–198

Cheung, B., Hui, L., Zhang, J., & Yiu, S. (2003). SmartTutor: An intelligent tutoring system in web-based adult education. Journal of Systems and Software, 68(1), 11–25. https://doi.org/10.1016/s0164-1212(02)00133-4.

Hobert, Sebastian, and Raphael Meyer von Wolff. “Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents.” Wirtschaftsinformatik 2019 Proceedings, Feb. 2019, https://aisel.aisnet.org/wi2019/track04/papers/2.

Saravana Kumar, N. M. “IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN IMPARTING EDUCATION AND EVALUATING STUDENT PERFORMANCE.” Journal of Artificial Intelligence and Capsule Networks, vol. 01, Sept. 2019, pp. 1–9, doi:10.36548/jaicn.2019.1.001.

Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75, 461–468. https://doi.org/ 10.1016/j.chb.2017.05.045

Rivas, Alberto, et al. “Artificial Neural Network Analysis of the Academic Performance of Students in Virtual Learning Environments.” Neurocomputing, vol. 423, Jan. 2021, pp. 713–20, doi:10.1016/j.neucom.2020.02.125.

Griol, D., Molina, J. M., & Callejas, Z. (2014). An approach to develop intelligent learning environments by means of immersive virtual worlds. Journal of Ambient Intelligence and Smart Environments, 6(2), 237–255.

Keshav, N. U., Salisbury, J. P., Vahabzadeh, A., & Sahin, N. T. (2017). Social communication coaching smartglasses: Well tolerated in a diverse sample of children and adults with autism. JMIR MHealth and UHealth, 5(9). https://doi.org/10.2196/mhealth.8534.

Phungsuk, Rojana, et al. “Development of a Problem-Based Learning Model via a Virtual Learning Environment.” Kasetsart Journal of Social Sciences, vol. 38, no. 3, Sept. 2017, pp. 297–306, doi:10.1016/j.kjss.2017.01.001.

Chi, M., & VanLehn, K. (2010). Meta-cognitive strategy instruction in intelligent tutoring systems: how, when, and why. Educ. Technol. Soc., 13(1), 25e39. https://www.jstor.org/stable/10.2307/jeductechsoci.13.1.25.

Deschênes, Michelle. “Recommender Systems to Support Learners’ Agency in a Learning Context: A Systematic Review.” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, Oct. 2020, p. 50, doi:10.1186/s41239-020-00219-w.

Perez-Ortiz, Maria, et al. “X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI.” 26th International Conference on Intelligent User Interfaces - Companion, Association for Computing Machinery, 2021, pp. 70–74, doi:10.1145/3397482.3450721.

Supriyanto, G., et al. “Application of Expert System for Education.” IOP Conference Series: Materials Science and Engineering, vol. 434, Dec. 2018, p. 012304, doi:10.1088/1757-899X/434/1/012304.

Kučak, Danijel, et al. Machine Learning in Education - a Survey of Current Research Trends. 2018, pp. 0406–10, doi:10.2507/29th.daaam.proceedings.059.

Supriyanto, G., et al. “Application of Expert System for Education.” IOP Conference Series: Materials Science and Engineering, vol. 434, Dec. 2018, p. 012304, doi:10.1088/1757-899X/434/1/012304.

Kabudi, Tumaini, et al. “AI-Enabled Adaptive Learning Systems: A Systematic Mapping of the Literature.” Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, p. 100017, doi:10.1016/j.caeai.2021.100017.

Różewski, Przemysław, et al. “Concept of Expert System for Creation of Personalized, Digital Skills Learning Pathway.” Procedia Computer Science, vol. 159, Jan. 2019, pp. 2304–12, https://doi.org/10.1016/j.procs.2019.09.405.

Muangprathub, Jirapond, et al. “Learning Recommendation with Formal Concept Analysis for Intelligent Tutoring System.” Heliyon, vol. 6, no. 10, Oct. 2020, p. e05227, https://doi.org/10.1016/j.heliyon.2020.e05227.

Treceño-Fernández, Daniel, et al. “Integration of an Intelligent Tutoring System in a Magnetic Resonance Simulator for Education: Technical Feasibility and User Experience.” Computer Methods and Programs in Biomedicine, vol. 195, Oct. 2020, p. 105634, https://doi.org/10.1016/j.cmpb.2020.105634.

Castro-Schez, J. J., et al. “An Intelligent Tutoring System for Supporting Active Learning: A Case Study on Predictive Parsing Learning.” Information Sciences, vol. 544, Jan. 2021, pp. 446–68, https://doi.org/10.1016/j.ins.2020.08.079.

Taub, Michelle, et al. “How Are Students’ Emotions Related to the Accuracy of Cognitive and Metacognitive Processes during Learning with an Intelligent Tutoring System?” Learning and Instruction, vol. 72, Apr. 2021, p. 101200, https://doi.org/10.1016/j.learninstruc.2019.04.001.

Demetriadis, Stavros, et al. “Conversational Agents in MOOCs: Reflections on First Outcomes of the ColMOOC Project.” Intelligent Systems and Learning Data Analytics in Online Education, edited by Santi Caballé et al., Academic Press, 2021, pp. xxxvii–lxxiv, https://doi.org/10.1016/B978-0-12-823410-5.00001-2.

Campos, Rodrigo, et al. “Providing Recommendations for Communities of Learners in MOOCs Ecosystems.” Expert Systems with Applications, vol. 205, Nov. 2022, p. 117510, https://doi.org/10.1016/j.eswa.2022.117510.

Pringle, Jamie K., et al. “Extended Reality (XR) Virtual Practical and Educational EGaming to Provide Effective Immersive Environments for Learning and Teaching in Forensic Science.” Science & Justice, Apr. 2022, https://doi.org/10.1016/j.scijus.2022.04.004.

Bian, Yulong. “Motivation Effect of Animated Pedagogical Agent’s Personality and Feedback Strategy Types on Learning in Virtual Training Environment.” Virtual Reality & Intelligent Hardware, vol. 4, no. 2, Apr. 2022, pp. 153–72, https://doi.org/10.1016/j.vrih.2021.11.001.

Sikström, Pieta, et al. “How Pedagogical Agents Communicate with Students: A Two-Phase Systematic Review.” Computers & Education, May 2022, p. 104564, https://doi.org/10.1016/j.compedu.2022.104564.

Tavakoli, Mohammadreza, et al. “An AI-Based Open Recommender System for Personalized Labor Market Driven Education.” Advanced Engineering Informatics, vol. 52, Apr. 2022, p. 101508, https://doi.org/10.1016/j.aei.2021.101508.

Nabizadeh, Amir Hossein, et al. “Adaptive Learning Path Recommender Approach Using Auxiliary Learning Objects.” Computers & Education, vol. 147, Apr. 2020, p. 103777, https://doi.org/10.1016/j.compedu.2019.103777.

Shao, Zhou, et al. “Adaptive Online Learning for IoT Botnet Detection.” Information Sciences, vol. 574, Oct. 2021, pp. 84–95, https://doi.org/10.1016/j.ins.2021.05.076.

Gomede, Everton, et al. “Deep Auto Encoders to Adaptive E-Learning Recommender System.” Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, p. 100009, https://doi.org/10.1016/j.caeai.2021.100009.

Afini Normadhi, Nur Baiti, et al. “Identification of Personal Traits in Adaptive Learning Environment: Systematic Literature Review.” Computers & Education, vol. 130, Mar. 2019, pp. 168–90, https://doi.org/10.1016/j.compedu.2018.11.005.

Pliakos, Konstantinos, et al. “Integrating Machine Learning into Item Response Theory for Addressing the Cold Start Problem in Adaptive Learning Systems.” Computers & Education, vol. 137, août 2019, pp. 91–103, https://doi.org/10.1016/j.compedu.2019.04.009.

Han, Jeongyun, et al. “Learning Analytics Dashboards for Adaptive Support in Face-to-Face Collaborative Argumentation.” Computers & Education, vol. 163, Apr. 2021, p. 104041, https://doi.org/10.1016/j.compedu.2020.104041.

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Published

2022-06-09

How to Cite

Ezzaim, A., Kharroubi, F. ., Dahbi, A. ., Aqqal, A. ., & Haidine, A. . (2022). Artificial intelligence in education - State of the art. International Journal of Computer Engineering and Data Science (IJCEDS), 2(2). Retrieved from http://www.ijceds.com/ijceds/article/view/37