Personalised learning and artificial intelligence in science education: current state and future perspectives

Özkan Yılmaz

doi:10.55056/etq.744

Authors

Özkan Yılmaz Erzincan University https://orcid.org/0000-0001-8963-3354

DOI:

https://doi.org/10.55056/etq.744

Keywords:

artificial intelligence, science education, personalised learning, intelligent tutoring systems, educational technology, systematic literature review, learner engagement

Abstract

This paper presents a comprehensive examination of the integration of artificial intelligence (AI) in science education and its impact on personalised learning. The research explores current applications, challenges, and future perspectives of AI technologies in educational settings. Through a systematic literature review, we identify the advantages of AI, such as enhanced individualised instruction, data-informed insights, and increased student engagement. The study combines quantitative and qualitative analyses, case studies, expert interviews, and technology assessments to offer a multidimensional understanding of AI's role in personalising science education. Despite the potential benefits, the research highlights barriers, including financial costs, infrastructure requirements, data privacy, and the need for teacher training. The future of AI in education suggests a trajectory towards advanced personalisation capabilities through adaptable learning systems, virtual tutors, and immersive learning environments. We underscore the importance of addressing the identified challenges to fully realise the transformative power of AI in science education. The findings illustrate that, with thoughtful implementation, AI holds promise for tailoring science learning experiences, making them more effective, inclusive, and engaging for students of varied needs and abilities.

Downloads

Download data is not yet available.

Abstract views: 360 / PDF views: 142

References

Alharbi, W., 2023. AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools. Education Research International, 2023(1), p.4253331. Available from: https://doi.org/10.1155/2023/4253331. DOI: https://doi.org/10.1155/2023/4253331

Ally, M., 2019. Competency Profile of the Digital and Online Teacher in Future Education. The International Review of Research in Open and Distributed Learning, 20(2). Available from: https://doi.org/10.19173/irrodl.v20i2.4206. DOI: https://doi.org/10.19173/irrodl.v20i2.4206

Antonenko, P. and Abramowitz, B., 2023. In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), pp.64–78. Available from: https://doi.org/10.1080/15391523.2022.2119450. DOI: https://doi.org/10.1080/15391523.2022.2119450

Baek, C. and Doleck, T., 2020. A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1), p.pp. 67–84. Available from: https://doi.org/10.3991/ijai.v2i1.14481. DOI: https://doi.org/10.3991/ijai.v2i1.14481

Barsoum, S.S., Elnagar, M.S. and Awad, B.M., 2022. The Effectiveness of Using a Cognitive Style-based Chatbot in Developing Science Concepts and Critical Thinking Skills among Preparatory School Pupils. European Scientific Journal, ESJ, 18(22), p.52. Available from: https://doi.org/10.19044/esj.2022.v18n22p52. DOI: https://doi.org/10.19044/esj.2022.v18n22p52

Bini, F., Pica, A., Azzimonti, L., Giusti, A., Ruinelli, L., Marinozzi, F. and Trimboli, P., 2021. Artificial Intelligence in Thyroid Field—A Comprehensive Review. Cancers, 13(19), p.4740. Available from: https://doi.org/10.3390/cancers13194740. DOI: https://doi.org/10.3390/cancers13194740

Buabbas, A.J., Miskin, B., Alnaqi, A.A., Ayed, A.K., Shehab, A.A., Syed-Abdul, S. and Uddin, M., 2023. Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education. Healthcare, 11(9), p.1298. Available from: https://doi.org/10.3390/healthcare11091298. DOI: https://doi.org/10.3390/healthcare11091298

Cao, W., Wang, Q., Sbeih, A. and Shibly, F.H.A., 2020. Artificial intelligence based efficient smart learning framework for education platform. Inteligencia Artificial, 23(66), p.112–123. Available from: https://doi.org/10.4114/intartif.vol23iss66pp112-123. DOI: https://doi.org/10.4114/intartif.vol23iss66pp112-123

Castellano, M.S., Contreras-McKay, I., Neyem, A., Farfán, E., Inzunza, O., Ottone, N.E., Sol, M. del, Alario-Hoyos, C., Alvarado, M.S. and Tubbs, R.S., 2024. Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation. Clinical Anatomy, 37(1), pp.12–24. Available from: https://doi.org/10.1002/ca.24074. DOI: https://doi.org/10.1002/ca.24074

Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I.G. and Cosentini, A.C., 2022. A clarification of the nuances in the fairness metrics landscape. Scientific Reports, 12(1), p.4209. Available from: https://doi.org/10.1038/s41598-022-07939-1. DOI: https://doi.org/10.1038/s41598-022-07939-1

Celik, I., 2023. Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, p.107468. Available from: https://doi.org/10.1016/j.chb.2022.107468. DOI: https://doi.org/10.1016/j.chb.2022.107468

Chan, K.S. and Zary, N., 2019. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Medical Education, 5(1), p.e13930. Available from: https://doi.org/10.2196/13930. DOI: https://doi.org/10.2196/13930

Chen, L., Chen, P. and Lin, Z., 2020. Artificial Intelligence in Education: A Review. IEEE Access, 8, pp.75264–75278. Available from: https://doi.org/10.1109/ACCESS.2020.2988510. DOI: https://doi.org/10.1109/ACCESS.2020.2988510

Chen, X., Xie, H. and Hwang, G.J., 2020. A multi-perspective study on Artificial Intelligence in Education: grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, p.100005. Available from: https://doi.org/10.1016/j.caeai.2020.100005. DOI: https://doi.org/10.1016/j.caeai.2020.100005

Chiu, M.C., Hwang, G.J., Hsia, L.H. and Shyu, F.M., 2024. Artificial intelligence-supported art education: a deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, 32(3), pp.824–842. Available from: https://doi.org/10.1080/10494820.2022.2100426. DOI: https://doi.org/10.1080/10494820.2022.2100426

Coşkun, F. and Gülleroğlu, H.D., 2021. Development of Artificial Intelligence in History and Its Usage in Education [Yapay Zekanın Tarih İçindeki Gelişimi ve Eğitimde Kullanılması]. Ankara University Journal of Faculty of Educational Sciences (JFES), 54(3), p.947–966. Available from: https://doi.org/10.30964/auebfd.916220. DOI: https://doi.org/10.30964/auebfd.916220

Dābolin, š, J. and Grundspen, k, is, J., 2013. The Role of Feedback in Intelligent Tutoring System. Applied Computer Systems, 14(1), pp.88–93. Available from: https://doi.org/10.2478/acss-2013-0011. DOI: https://doi.org/10.2478/acss-2013-0011

Damioli, G., Van Roy, V. and Vertesy, D., 2021. The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), pp.1–25. Available from: https://doi.org/10.1007/s40821-020-00172-8. DOI: https://doi.org/10.1007/s40821-020-00172-8

Donnermann, M., Schaper, P. and Lugrin, B., 2022. Social Robots in Applied Settings: A Long-Term Study on Adaptive Robotic Tutors in Higher Education. Frontiers in Robotics and AI, 9. Available from: https://doi.org/10.3389/frobt.2022.831633. DOI: https://doi.org/10.3389/frobt.2022.831633

Edwards, B. and Orland, K., 2024. Major ChatGPT-4o update allows audio-video talks with an “emotional” AI chatbot. Available from: https://arstechnica.com/information-technology/2024/05/chatgpt-4o-lets-you-have-real-time-audio-video-conversations-with-emotional-chatbot/.

Fairén, M., Moyés, J. and Insa, E., 2020. VR4Health: Personalized teaching and learning anatomy using VR. Journal of Medical Systems, 44(5), p.94. Available from: https://doi.org/10.1007/s10916-020-01550-5. DOI: https://doi.org/10.1007/s10916-020-01550-5

Fu, R., Tian, M. and Tang, Q., 2022. The Design of Personalized Education Resource Recommendation System under Big Data. Computational Intelligence and Neuroscience, 2022(1), p.1359730. Available from: https://doi.org/10.1155/2022/1359730. DOI: https://doi.org/10.1155/2022/1359730

Garcia-Iruela, M., Hijón-Neira, R. and Connolly, C., 2021. Analysis of Three Methodological Approaches in the Use of Gamification in Vocational Training. Information, 12(8), p.300. Available from: https://doi.org/10.3390/info12080300. DOI: https://doi.org/10.3390/info12080300

Goksel, N. and Bozkurt, A., 2019. Artificial Intelligence in Education: Current Insights and Future Perspectives. In: S. Sisman-Ugur and G. Kurubacak, eds. Handbook of Research on Learning in the Age of Transhumanism. Hershey, PA: IGI Global, pp.224–236. https://www.researchgate.net/publication/332704741, Available from: https://doi.org/10.4018/978-1-5225-8431-5.ch014. DOI: https://doi.org/10.4018/978-1-5225-8431-5.ch014

Gunawan, K.D.H., Liliasari, L., Kaniawati, I. and Setiawan, W., 2021. Implementation of Competency Enhancement Program for Science Teachers Assisted by Artificial Intelligence in Designing HOTS-based Integrated Science Learning. Jurnal Penelitian dan Pembelajaran IPA, 7(1), pp.55–65. Available from: https://doi.org/10.30870/jppi.v7i1.8655. DOI: https://doi.org/10.30870/jppi.v7i1.8655

Holicza, B. and Kiss, A., 2023. Predicting and Comparing Students’ Online and Offline Academic Performance Using Machine Learning Algorithms. Behavioral Sciences, 13(4), p.289. Available from: https://doi.org/10.3390/bs13040289. DOI: https://doi.org/10.3390/bs13040289

Hong, X. and Liu, Q., 2022. Assessing young children’s national identity through human-computer interaction: A game-based assessment task. Frontiers in Psychology, 13. Available from: https://doi.org/10.3389/fpsyg.2022.956570. DOI: https://doi.org/10.3389/fpsyg.2022.956570

How, M.L. and Hung, W.L.D., 2019. Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations. Education Sciences, 9(2), p.110. Available from: https://doi.org/10.3390/educsci9020110. DOI: https://doi.org/10.3390/educsci9020110

Jia, X., 2021. Research on the Role of Big Data Technology in the Reform of English Teaching in Universities. Wireless Communications and Mobile Computing, 2021(1), p.9510216. Available from: https://doi.org/10.1155/2021/9510216. DOI: https://doi.org/10.1155/2021/9510216

Jiao, P., Ouyang, F., Zhang, Q. and Alavi, A.H., 2022. Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), pp.6321–6344. Available from: https://doi.org/10.1007/s10462-022-10155-y. DOI: https://doi.org/10.1007/s10462-022-10155-y

Jing, X., Zhu, R., Lin, J., Yu, B. and Lu, M., 2022. Education Sustainability for Intelligent Manufacturing in the Context of the New Generation of Artificial Intelligence. Sustainability, 14(21), p.14148. Available from: https://doi.org/10.3390/su142114148. DOI: https://doi.org/10.3390/su142114148

Karaca, A. and Kılcan, B., 2023. The Adventure of Artificial Intelligence Technology in Education: Comprehensive Scientific Mapping Analysis. Participatory Educational Research, 10(4), p.144–165. Available from: https://doi.org/10.17275/per.23.64.10.4. DOI: https://doi.org/10.17275/per.23.64.10.4

Katsaris, I. and Vidakis, N., 2021. Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), pp.124–145. Available from: https://doi.org/10.25082/AMLER.2021.02.007. DOI: https://doi.org/10.25082/AMLER.2021.02.007

Kızılaslan, A., 2019. The development of science process skills in visually impaired students: analysis of the activities. International Journal of Evaluation and Research in Education (IJERE), 8(1), pp.90–96. Available from: https://doi.org/10.11591/ijere.v8i1.17427. DOI: https://doi.org/10.11591/ijere.v8i1.17427

Klašnja-Milićević, A. and Ivanović, M., 2021. E-learning Personalization Systems and Sustainable Education. Sustainability, 13(12), p.6713. Available from: https://doi.org/10.3390/su13126713. DOI: https://doi.org/10.3390/su13126713

Klašnja-Milićević, A., Ivanović, M. and Stantić, B., 2020. Designing personalized learning environments — the role of learning analytics. Vietnam journal of computer science, 07(03), pp.231–250. Available from: https://doi.org/10.1142/S219688882050013X. DOI: https://doi.org/10.1142/S219688882050013X

Kramer, N., 2024. OpenAI’s GPT-4.0: Everything you need to know in one place. Available from: https://daily.dev/blog/openais-gpt-4o-everything-you-need-to-know-in-one-place.

Li, J., Li, J., Yang, Y. and Ren, Z., 2021. Design of Higher Education System Based on Artificial Intelligence Technology. Discrete Dynamics in Nature and Society, 2021(1), p.3303160. Available from: https://doi.org/https://doi.org/10.1155/2021/3303160. DOI: https://doi.org/10.1155/2021/3303160

Li, K.C. and Wong, B.T.M., 2023. Artificial intelligence in personalised learning: a bibliometric analysis. Interactive Technology and Smart Education, 20(3), p.422–445. Available from: https://doi.org/10.1108/itse-01-2023-0007. DOI: https://doi.org/10.1108/ITSE-01-2023-0007

Li, L., Chen, C.P., Wang, L., Liang, K. and Bao, W., 2023. Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices. Sustainability, 15(10), p.7940. Available from: https://doi.org/10.3390/su15107940. DOI: https://doi.org/10.3390/su15107940

Lin, X.F., Chen, L., Chan, K.K., Peng, S., Chen, X., Xie, S., Liu, J. and Hu, Q., 2022. Teachers’ Perceptions of Teaching Sustainable Artificial Intelligence: A Design Frame Perspective. Sustainability, 14(13), p.7811. Available from: https://doi.org/10.3390/su14137811. DOI: https://doi.org/10.3390/su14137811

Liu, H., 2022. Design of Neural Network Model for Cross-Media Audio and Video Score Recognition Based on Convolutional Neural Network Model. Computational Intelligence and Neuroscience, 2022(1), p.4626867. Available from: https://doi.org/10.1155/2022/4626867. DOI: https://doi.org/10.1155/2022/4626867

Lu, Y., 2019. Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), pp.1–29. Available from: https://doi.org/10.1080/23270012.2019.1570365. DOI: https://doi.org/10.1080/23270012.2019.1570365

Madani, M., Behzadi, M.M. and Nabavi, S., 2022. The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review. Cancers, 14(21), p.5334. Available from: https://doi.org/10.3390/cancers14215334. DOI: https://doi.org/10.3390/cancers14215334

Murtaza, M., Ahmed, Y., Shamsi, J.A., Sherwani, F. and Usman, M., 2022. AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access, 10, pp.81323–81342. Available from: https://doi.org/10.1109/ACCESS.2022.3193938. DOI: https://doi.org/10.1109/ACCESS.2022.3193938

Nazaretsky, T., Cukurova, M. and Alexandron, G., 2022. An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. LAK22: 12th International Learning Analytics and Knowledge Conference. New York, NY, USA: Association for Computing Machinery, LAK22, p.56–66. Available from: https://doi.org/10.1145/3506860.3506866. DOI: https://doi.org/10.1145/3506860.3506866

Nja, C.O., Idiege, K.J., Uwe, U.E., Meremikwu, A.N., Ekon, E.E., Erim, C.M., Ukah, J.U., Eyo, E.O., Anari, M.I. and Cornelius-Ukpepi, B.U., 2023. Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1), p.42. Available from: https://doi.org/10.1186/s40561-023-00261-x. DOI: https://doi.org/10.1186/s40561-023-00261-x

O’Donnell, J., 2024. OpenAI’s new GPT-4o lets people interact using voice or video in the same model. Available from: https://www.technologyreview.com/2024/05/13/1092358/openais-new-gpt-4o-model-lets-people-interact-using-voice-or-video-in-the-same-model.

Ososky, S., Brawner, K., Goldberg, B. and Sottilare, R., 2016. GIFT Cloud: Improving Usability of Adaptive Tutor Authoring Tools within a Web-based Application. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), pp.1389–1393. Available from: https://doi.org/10.1177/1541931213601320. DOI: https://doi.org/10.1177/1541931213601320

Patiño, A., Ramírez-Montoya, M.S. and Buenestado-Fernández, M., 2023. Active learning and education 4.0 for complex thinking training: analysis of two case studies in open education. Smart Learning Environments, 10(1), p.8. Available from: https://doi.org/10.1186/s40561-023-00229-x. DOI: https://doi.org/10.1186/s40561-023-00229-x

Popenici, S.A.D. and Kerr, S., 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), p.22. Available from: https://doi.org/10.1186/s41039-017-0062-8. DOI: https://doi.org/10.1186/s41039-017-0062-8

Pu, S., Ahmad, N.A., Khambari, M.N.M. and Yap, N.K., 2021. Identification and analysis of core topics in educational artificial intelligence research: A Bibliometric analysis. Cypriot Journal of Educational Sciences, 16(3), p.995–1009. https://www.researchgate.net/publication/352972830, Available from: https://doi.org/10.18844/cjes.v16i3.5782. DOI: https://doi.org/10.18844/cjes.v16i3.5782

Qu, J., Zhao, Y. and Xie, Y., 2022. Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), pp.581–588. Available from: https://doi.org/10.1002/sres.2864. DOI: https://doi.org/10.1002/sres.2864

Rad, P., Roopaei, M., Beebe, N., Shadaram, M. and Au, Y., 2018. AI Thinking for Cloud Education Platform with Personalized Learning. In: T. Bui, ed. 51st Hawaii International Conference on System Sciences, HICSS 2018, Hilton Waikoloa Village, Hawaii, USA, January 3-6, 2018. ScholarSpace / AIS Electronic Library (AISeL), pp.1–10. Available from: https://hdl.handle.net/10125/49890. DOI: https://doi.org/10.24251/HICSS.2018.003

Renz, A., Krishnaraja, S. and Gronau, E., 2020. Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology? International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1), p.pp. 14–30. Available from: https://doi.org/10.3991/ijai.v2i1.12675. DOI: https://doi.org/10.3991/ijai.v2i1.12675

Robins, A., Hunt, T., Robertson, D. and Carter, R., 2022. Amazon Alexa Skills as a Novel Modality for In-service Professional Micro-Development (WIP). Proceedings of the Ninth ACM Conference on Learning @ Scale. New York, NY, USA: Association for Computing Machinery, L@S ’22, p.284–288. Available from: https://doi.org/10.1145/3491140.3528314. DOI: https://doi.org/10.1145/3491140.3528314

Roll, I. and Wylie, R., 2016. Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26(2), pp.582–599. Available from: https://doi.org/10.1007/s40593-016-0110-3. DOI: https://doi.org/10.1007/s40593-016-0110-3

Rui, Z. and Badarch, T., 2022. Research on Applications of Artificial Intelligence in Education. American Journal of Computer Science and Technology, 5(2), pp.72–79. Available from: https://doi.org/10.11648/j.ajcst.20220502.17. DOI: https://doi.org/10.11648/j.ajcst.20220502.17

Salas-Pilco, S.Z. and Yang, Y., 2022. Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19(1), p.21. Available from: https://doi.org/10.1186/s41239-022-00326-w. DOI: https://doi.org/10.1186/s41239-022-00326-w

Sanusi, I.T., Olaleye, S.A., Oyelere, S.S. and Dixon, R.A., 2022. Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Computers and Education Open, 3, p.100083. Available from: https://doi.org/10.1016/j.caeo.2022.100083. DOI: https://doi.org/10.1016/j.caeo.2022.100083

Shao, T. and Zhou, J., 2021. Brief Overview of Intelligent Education. Journal of Contemporary Educational Research, 5(8), p.187–192. Available from: https://doi.org/10.26689/jcer.v5i8.2460. DOI: https://doi.org/10.26689/jcer.v5i8.2460

Skrypnyk, A., Talavyria, M. and Sayapin, S., 2019. Information economy as a factor of rural development. Bioeconomics and agrarian business, 2(2019), p.111–123. Available from: https://doi.org/10.31548/bioeconomy2019.01.111. DOI: https://doi.org/10.31548/bioeconomy2019.01.111

Sofyan, D., Puspitasari, N., Maryati, I., Basuki, B. and Madio, S.S., 2021. The Effects of GeoGebra on Problems Solving Skill in the Integral Calculus. Proceedings of the 1st International Conference on Islam, Science and Technology, ICONISTECH 2019, 11-12 July 2019, Bandung, Indonesia. EAI. Available from: https://doi.org/10.4108/eai.11-7-2019.2297424. DOI: https://doi.org/10.4108/eai.11-7-2019.2297424

Song, P. and Wang, X., 2020. A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), pp.473–486. Available from: https://doi.org/10.1007/s12564-020-09640-2. DOI: https://doi.org/10.1007/s12564-020-09640-2

Tao, L., 2021. Application of Data Mining in the Analysis of Martial Arts Athlete Competition Skills and Tactics. Journal of healthcare engineering, 2021(1), p.5574152. Available from: https://doi.org/10.1155/2021/5574152. DOI: https://doi.org/10.1155/2021/5574152

Tapalova, O. and Zhiyenbayeva, N., 2022. Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of e-Learning, 20(5), p.639–653. Available from: https://doi.org/10.34190/ejel.20.5.2597. DOI: https://doi.org/10.34190/ejel.20.5.2597

Vesin, B., Mangaroska, K. and Giannakos, M., 2018. Learning in smart environments: user-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5(1), p.24. Available from: https://doi.org/10.1186/s40561-018-0071-0. DOI: https://doi.org/10.1186/s40561-018-0071-0

Watters, J.D., Hill, A., Weinrich, M., Supalo, C. and Jiang, F., 2021. An Artificial Intelligence Tool for Accessible Science Education. Journal of Science Education for Students with Disabilities, 24(1), p.1–14. Available from: https://doi.org/10.14448/jsesd.13.0010. DOI: https://doi.org/10.14448/jsesd.13.0010

Wrubel, J., White, D.W. and Allen, J.H., 2009. High-Fidelity e-Learning: SEI’s Virtual Training Environment (VTE). Carnegie Mellon University, Software Engineering Institute. Available from: https://apps.dtic.mil/sti/citations/ADA501744. DOI: https://doi.org/10.21236/ADA501744

Wu, S.Y. and Yang, K.K., 2022. The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities. Frontiers in Psychology, 13. Available from: https://doi.org/10.3389/fpsyg.2022.868623. DOI: https://doi.org/10.3389/fpsyg.2022.868623

Xin, J., Yan, G. and Song, Q., 2022. Application of Patent Right and Trademark Right in Packaging Design Based on Computer Nonlinear Prediction Systems for Virtual Reality Technology. Scientific Programming, 2022(1), p.7507497. Available from: https://doi.org/10.1155/2022/7507497. DOI: https://doi.org/10.1155/2022/7507497

Xu, W. and Ouyang, F., 2022. The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), p.59. Available from: https://doi.org/10.1186/s40594-022-00377-5. DOI: https://doi.org/10.1186/s40594-022-00377-5

Xue, X. and Jia, Z., 2022. The Piano-Assisted Teaching System Based on an Artificial Intelligent Wireless Network. Wireless Communications and Mobile Computing, 2022(1), p.5287172. Available from: https://doi.org/10.1155/2022/5287172. DOI: https://doi.org/10.1155/2022/5287172