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Artificial intelligence, education, contemporary, e-learning, online teaching, deep learning
Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., Chouchane, L., EINatour, R., Abd-Alrazaq, A., Pandas, S., & Sheikh, J. (2022). Wearable artificial intelligence for assessing physical activity in high school children. Sustainability, 15(1), 638. https://doi.org/10.3390/su15010638
Alhumaid, K., Naqbi, S.A., Elsori, D., & Mansoori, M.A. (2023). The adoption of artificial intelligence applications in education. International Journal of Data and Network Science, 7(1), 457-466. https://doi.org/10.5267/j.ijdns.2022.8.013
Allaoua-Chelloug, S., Ashfaq, H., Alsuhibany, S., Shorfuzzaman, M., Alsufyani, A., Jalal, A., & Park, J. (2023). Real objects understanding using 3D haptic virtual reality for e-learning education. Computers, Materials & Continua, 74(1), 1607-1624. https://doi.org/10.32604/cmc.2023.032245
Aloisi, C. (2023). The future of standardised assessment: Validity and trust in algorithms for assessment and scoring. European Journal of Education, 58(1), 98-110. https://doi.org/10.1111/ejed.12542
Arbelaez-Ossa, L., Rost, M., Lorenzini, G., Shaw, D.M., & Elger, B.S. (2023). A smarter perspective: Learning with and from AI-cases. Artificial Intelligence in Medicine, 135, 102458. https://doi.org/10.1016/j.artmed.2022.102458
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Bañeres, D., Rodríguez-González, M.E., Guerrero-Roldán, A.E., & Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1), 1-25. https://doi.org/10.1186/s41239-022-00371-5
Cerqueira, J.M., Cleto, B., Moura, J.M., Sylla, C., & Ferreira, L. (2023). Potentiating learning through augmented reality and serious games. In A.Y.C. Nee & S.K. Ong (eds), Springer Handbook of Augmented Reality (pp. 369-390). Springer. https://doi.org/10.1007/978-3-030-67822-7_15
Chai, C.S., Chiu, T.K.F., Wang, X., Jiang, F., & Lin, X.F. (2023). Modeling Chinese Secondary School students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1), 605. https://doi.org/10.3390/su15010605
Dabbous, A., & Boustani, N.M. (2023). Digital explosion and entrepreneurship education: Impact on promoting entrepreneurial intention for business students. Journal of Risk and Financial Management, 16(1), 27-48. https://doi.org/10.3390/jrfm16010027
Dong, Y. (2022). Application of artificial intelligence software based on semantic web technology in english learning and teaching. Journal of Internet Technology, 23(1), 143-152. https://doi.org/10.53106/160792642022012301015
Ednie, G., Kapoor, T., Koppel, O., Piczak, M.L., Reid, J.L., Murdoch, A.D., Cook, C.N., Sutherland, W.J., & Cooke, S.J. (2022). Foresight science in conservation: Tools, barriers, and mainstreaming opportunities. Ambio, 52(2), 411-424. https://doi.org/10.1007/s13280-022-01786-0
Flores-Vivar, J., & García-Peñalvo, F. (2023). Reflexiones sobre la ética, potencialidades y desafíos de la inteligencia artificial en el marco de una educación de calidad (ODS4). [Reflexiones sobre la ética, potencialidades y desafíos de la IA en el marco de la Educación de Calidad (ODS4)]. Comunicar, 74, 37-47. https://doi.org/10.3916/C74-2023-03
García-Orosa, B., Canavilhas, J., & Vázquez-Herrero, J. (2023). Algorithms and communication: A systematized literature review. [Algoritmos y comunicación: Revisión sistematizada de la literatura]. Comunicar, 74, 9-21. https://doi.org/10.3916/C74-2023-01
Hinojo-Lucena, F., Aznar-Díaz, I., Cáceres-Reche, M., & Romero-Rodríguez, J. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51-60. https://doi.org/10.3390/educsci9010051
Ho, M., Le, N., Mantello, P., Ho, M., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72, 102-166. https://doi.org/10.1016/j.techsoc.2022.102166
Hort, M., Moussa, R., & Sarro, F. (2023). Multi-objective search for gender-fair and semantically correct word embeddings. Applied Soft Computing, 133, 109916. https://doi.org/10.1016/j.asoc.2022.109916
Hu, Y., Fu, J.S., & Yeh, H. (2023). Developing an early-warning system through robotic process automation: Are intelligent tutoring robots as effective as human teachers? Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2022.2160467
Hua-Hu, K. (2023). An exploration of the key determinants for the application of AI-enabled higher education based on a hybrid soft-computing technique and a DEMATEL approach. Expert Systems with Applications, 212, 118-762. https://doi.org/10.1016/j.eswa.2022.118762
Huang, A.Y.Q., Lu, O.H.T., & Yang, S.J.H. (2023). Effects of artificial Intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers and Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
Hussain, A. (2023). Use of artificial intelligence in the library services: prospects and challenges. Library Hi Tech News, 40(2), 15-17. https://doi.org/10.1108/LHTN-11-2022-0125
Kaur, D., Uslu, S., Rittichier, K.J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1-38. https://doi.org/10.1145/3491209
King, M.R., & chatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1-2. https://doi.org/10.1007/s12195-022-00754-8
Lahza, H., Khosravi, H., & Demartini, G. (2023). Analytics of learning tactics and strategies in an online learnersourcing environment. Journal of Computer Assisted Learning, 39(1), 94-112. https://doi.org/10.1111/jcal.12729
Li, C., Zheng, P., Yin, Y., Wang, B., & Wang, L. (2023). Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology, 40, 75-101. https://doi.org/10.1016/j.cirpj.2022.11.003
Matthew, J., Pagea, J.E., McKenziea, P.M., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuiness, L.A., … Moher, D. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4
Picciano, A.G. (2019). Artificial intelligence and the academy’s loss of purpose. Online Learning Journal, 23(3), 270-284. https://doi.org/10.24059/olj.v23i3.2023
Sayed, B.T., Madanan, M., & Biju, N. (2023). An efficient artificial intelligence-based educational data mining approach for higher education and early recognition system. SN Computer Science, 4(2), 130. https://doi.org/10.1007/s42979-022-01562-7
Shen, C., & Tan, Y. (2023). Effect evaluation model of computer aided physical education teaching and training based on artificial intelligence. Computer-Aided Design and Applications, 20(S5), 106-115. https://doi.org/10.14733/cadaps.2023.S5.106-115
Sun, F., & Ye, R. (2023). Moral considerations of artificial intelligence. Science and Education, 32(1), 1-17. https://doi.org/10.1007/s11191-021-00282-3
Tongkachok, K., Ali, B.M., Ganguly, M., Kumar, S., Malathi, M., & Subramanian, M. (2023). A detailed exploration of artificial intelligence and digital education and its sustainable impact on the youth of society. In S. Yadav., A. Haleem, P.K. Arora., & H. Kumar, H. (eds), Proceedings of Second International Conference in Mechanical and Energy Technology (pp. 139-146). Springer. https://doi.org/10.1007/978-981-19-0108-9_15
Ursani, Z., & Ursani, A.A. (2023). The theory of probabilistic hierarchical learning for classification. Annals of Emerging Technologies in Computing, 7(1), 61-74. https://doi.org/10.33166/AETiC.2023.01.005
Vila, E.M.S., & Penín, M.L. (2007). Introduction to special issue AI techniches applied in education. Inteligencia Artificial, 11(33), 7-12. https://doi.org/10.4114/ia.v11i33.914
Wang, X., Liu, Q., Pang, H., Tan, S.C., Lei, J., Wallace, M.P., & Li, L. (2023). What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis. Computers and Education, 194, 104703. https://doi.org/10.1016/j.compedu.2022.104703
Zhen, R., Song, W., He, Q., Cao, J., Shi, L., & Luo, J. (2023). Human-computer interaction system: A survey of talking-head generation. Electronics, 12(1), 218-239. https://doi.org/10.3390/electronics12010218
Zhou, W. (2023). The development system of local music teaching materials based on deep learning. Optik, 273, 170421. https://doi.org/10.1016/j.ijleo.2022.170421