Using artificial intelligence in digital video production: A systematic review study

Abstract

Advancements in artificial intelligence (AI) have tailored computer systems to meet user needs, thereby enhancing user experience. The application of AI technology in the production of digital videos, particularly in education, is becoming increasingly prevalent. This study aims to explore trends in the use of AI technology for digital video production. To achieve this, a systematic literature review was conducted across the Web of Science, ERIC, Taylor & Francis, Education Full Text EBSCO, and ScienceDirect databases. Studies were selected following the PRISMA flowchart, adhering to inclusion criteria aligned with the study's objectives. Consequently, 21 international studies were analyzed. The findings indicate that AI supports the creation of diverse digital content, which can serve various purposes such as general guidance, knowledge reinforcement, design and experimentation, and personalized experiences. However, it appears that AI's full potential has not yet been efficiently harnessed. Therefore, it is recommended that future research focus on developing digital content that caters to individual differences, enhances social interaction, includes enriched features, and is adaptable to various environments.

Keywords

• digital video
• artificial intelligence
• human-computer interaction

References

1. Admiraal, W., Kester, L., Janssen, C., Jonge, M., Louws, M., Post, L., & Lockhorst, D. (2018). Personalizing Learning with Mobile Technology in Secondary Education. International Association for Development of the Information Society, 62-69.
2. Akay, E., Uzuner, Y., & Girgin, Ü. (2014). Problems and Solution Efforts in the Support Education Room Application with Hearing Impaired Students in Inclusion. Journal of Qualitative Research in Education, 2(2), 42-67.
3. Akhila, C. V. (2018, June). A Survey on Collaborative Learning Approach for Speech and Speaker Recognition. In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics (p. 199). https://doi.org/10.21467/PROCEEDINGS.1.34
4. Aktay, S. (2022). The usability of images generated by artificial intelligence (AI) in education. International technology and education journal, 6(2), 51-62.
5. Amirhosseini, M. H., & Kazemian, H. (2019). Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive processing, 20(2), 175-193. https://doi.org/10.1007/s10339-019-00912-3
6. An, L. (2023). Video recording an automatic editing method based on artificial intelligence algorithm, 12599, 1259924 - 1259924-6. https://doi.org/10.1117/12.2673367.
7. Bala, A., Padmaja, T., & Gopisettry, D. (2018). Auto-Dialog Systems: Implementing Automatic Conversational Man-Machine Agents by Using Artificial Intelligence & Neural Networks. International Journal of Scientific Research and Review, 7(1), 1-5.
8. Balti, M., Somrani, G., Jemai, A., & Bouhachem, M. (2023). AI Based Video and Image Analytics. 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA), 1-6. https://doi.org/10.1109/INISTA59065.2023.10310403.

9. Bayne, S. (2015). Teacherbot: Interventions in automated teaching. Teaching in Higher Education, 20(4), 455–467. https://doi.org/10.1080/13562517.2015.1020783
10. Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3. https://doi.org/10.1126/scirobotics.aat5954.
11. Bourguet, M., Jin, Y., Shi, Y., Chen, Y., Ardila, L., & Venture, G. (2020). Social Robots that can Sense and Improve Student Engagement. 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 127-134. https://doi.org/10.1109/TALE48869.2020.9368438.
12. Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI Studies in half a century. Sustainability, 13(2), 800.
13. Callaway, C., Not, E., Novello, A., Rocchi, C., Stock, O., & Zancanaro, M. (2005). Automatic cinematography and multilingual NLG for generating video documentaries. Artificial Intelligence, 165(1), 57-89. https://doi.org/10.1016/j.artint.2005.02.001
14. Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (2022). How deepfakes and artificial ıntelligence could reshape the advertising ındustry. Journal of Advertising Research, 62, 241 - 251. https://doi.org/10.2501/jar-2022-017.
15. Campbell, L., & Cox, T. (2018). Digital Video as a personalized learning assignment: a qualitative study of student authored video using the ıcsdr model. Journal of the Scholarship of Teaching and Learning, 18, 11-24. https://doi.org/10.14434/JOSOTL.V18I1.21027.
16. Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510.
17. Cohen, P. (2016). Harold Cohen and AARON. AI Magazine, 37(4).
18. Crossan, M. M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational innovation: A systematic review of the literature. Journal of management studies, 47(6), 1154-1191. doi:10.1111/j.1467-6486.2009.00880.x
19. Daugavet, M., Shabelnikov, S., Adonin, L., Olga, I., , P., Dvorkina, T., Antipov, D., Korobeynikov, A., , S., & , N. (2019). Third international conference “Bioinformatics: from Algorithms to Applications” (BiATA 2019). BMC Bioinformatics, 20. https://doi.org/10.1186/s12859-019-3122-9.
20. Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends in signal processing, 7(3–4), 197- 387.
21. Denny, P., Khosravi, H., Hellas, A., Leinonen, J., & Sarsa, S. (2023). Can we trust AI-generated educational content? comparative analysis of human and AI-generated learning resources. arXiv preprint arXiv:2306.10509.
22. Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A., & Russakovsky, O. (2023). Art and the science of generative AI. Science, 380, 1110 - 1111. https://doi.org/10.1126/science.adh4451.
23. Er, G., Sk, V., & Gk, B. (2023). Development of an Automated Tool to download Youtube Audio/Video using Artificial Intelligence Techniques. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 763-768). https://doi.org/10.1109/ICCES57224.2023.10192860.
24. Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023). AI-based adaptive learning: A systematic mapping of the literature. Journal of Universal Computer Science, 29(10), 1161. https://doi.org/10.3897/jucs.90528
25. Ezzat, T., Geiger, G., & Poggio, T. (2002). Trainable videorealistic speech animation. ACM Transactions on Graphics (TOG), 21(3), 388-398. https://doi.org/10.1145/566654.566594
26. Fill, J., & Ward, M. (2020). Special Issue on Analysis of Algorithms. Algorithmica, 82, 385 - 385. https://doi.org/10.1007/s00453-019-00668-4.
27. Fırat, M. (2020, December). Natural Language Processing in Student Support Services: The Case of GPT-3. In International Conference of Strategic Research in Social Science and Education (pp. 532-536).
28. Forkan, A. R. M., Kang, Y. B., Jayaraman, P. P., Du, H., Thomson, S., Kollias, E., & Wieland, N. (2023). VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment. Int. J. Adv. Corp. Learn., 16(1), 19-27.
29. Gamage, K. A., & Perera, E. (2021). Undergraduate students’ device preferences in the transition to online learning. Social Sciences, 10(8), 288. https://doi.org/10.3390/socsci10080288.
30. Gareev, D., Glassl, O., & Nouzri, S. (2022). Using GANs to generate lyric videos. IFAC-PapersOnLine, 55(10), 3292-3297. https://doi.org/10.1016/j.ifacol.2022.10.126
31. Genç, Z., & Çelik, B. (2022). A Systematic Review on the Use of Social Robots in Special Education. 15th Internatıonal Computer And Instructıonal Technologıes Symposıum, 52.
32. Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A Twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001
33. Guill, K., Lüdtke, O., & Köller, O. (2019). Assessing the instructional quality of private tutoring and its effects on student outcomes: Analyses from the German National Educational Panel Study. The British Journal of Educational Psychology, 90, 282 - 300. https://doi.org/10.1111/bjep.12281.
34. Hirschberg, J., & Manning, C. (2015). Advances in natural language processing. Science, 349, 261 - 266. https://doi.org/10.1126/science.aaa8685.
35. Holmes, K. (2009). Planning to Teach with Digital Tools: Introducing the Interactive Whiteboard to Pre-Service Secondary Mathematics Teachers. Australasian Journal of Educational Technology, 25, 351-365. https://doi.org/10.14742/AJET.1139.
36. Huang, Y., Lv, S., Tseng, K. K., Tseng, P. J., Xie, X., & Lin, R. F. Y. (2023). Recent advances in artificial intelligence for video production system. Enterprise Information Systems, 17(11), 2246188. https://doi.org/10.1080/17517575.2023.2246188
37. İlyas, A., Park, S., Engstrom, L., Leclerc, G., & Madry, A. (2022). Datamodels: Predicting Predictions from Training Data. ArXiv, abs/2202.00622.
38. Ingavélez-Guerra, P., Robles-Bykbaev, V., Perez-Muñoz, A., Hilera, J., & Tortosa, S. (2022). Automatic adaptation of open educational resources: an approach from a multilevel methodology based on students’ preferences, educational special needs, artificial intelligence, and accessibility metadata. IEEE Access, PP, 1-1. https://doi.org/10.1109/access.2021.3139537.
39. Ismail, L. I., Hanapiah, F. A., Belpaeme, T., Dambre, J., & Wyffels, F. (2021). Analysis of attention in child–robot interaction among children diagnosed with cognitive impairment. International Journal of Social Robotics, 13(2), 141-152.
40. Jayanthiladevi, A., Raj, A., Narmadha, R., Chandran, S., Shaju, S., & Prasad, K. (2020). AI in Video Analysis, Production and Streaming Delivery. Journal of Physics: Conference Series, 1712. https://doi.org/10.1088/1742-6596/1712/1/012014.
41. Jian, M. (2023). Personalized learning through AI. Advances in Engineering Innovation. https://doi.org/10.54254/2977-3903/5/2023039.
42. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2017). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82, 3713 - 3744. https://doi.org/10.1007/s11042-022-13428-4.
43. Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University,33(2004), 1-26.
44. Kostolányová, K. (2017). Adaptation of personalized education in e-learning environment. In Emerging Technologies for Education: First International Symposium, SETE 2016, Held in Conjunction with ICWL 2016, Rome, Italy, October 26-29, 2016, Revised Selected Papers 1 (pp. 433-442). Springer International Publishing.
45. Kumar, S., Ghai, V., Jha, A., & Sharma, S. (2022, March). Role of artificial intelligence in generating video. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 697-701). IEEE. https://doi.org/10.1109/ICACCS54159.2022.9785336
46. Lauriola, I., Lavelli, A., & Aiolli, F. (2021). An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing, 470, 443-456. https://doi.org/10.1016/j.neucom.2021.05.103.
47. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539.
48. Lee, C. A., Tzeng, J. W., Huang, N. F., & Su, Y. S. (2021). Prediction of student performance in massive open online courses using deep learning system based on learning behaviors. Educational Technology & Society, 24(3), 130-146.
49. Leiker, D., Gyllen, A., Eldesouky, I., & Cukurova, M. (2023). Generative AI for learning: Investigating the potential of synthetic learning videos. ArXiv, abs/2304.03784. https://doi.org/10.48550/arXiv.2304.03784.
50. Li, S. (2023). Application of artificial intelligence-based style transfer algorithm in animation special effects design. Open Computer Science, 13. https://doi.org/10.1515/comp-2022-0255.
51. Li, X. (2022). Research on reform and breakthrough of news, film, and television media based on artificial intelligence. Journal of Intelligent Systems, 31(1), 992-1001. https://doi.org/10.1515/jisys-2022-0112
52. Li, Y. (2021). Film and TV Animation Production Based on Artificial Intelligence AlphaGd. Mob. Inf. Syst., 2021, 1104248:1-1104248:8. https://doi.org/10.1155/2021/1104248.
53. Liu, C., & Yu, H. (2023). Ai-empowered persuasive video generation: A survey. ACM Computing Surveys, 55(13s), 1-31. https://doi.org/10.1145/3588764
54. Liu, Q., & Peng, H. (2021). Influence of Artificial Intelligence Technology on Animation Creation. Journal of Physics: Conference Series, 1881. https://doi.org/10.1088/1742-6596/1881/3/032076.
55. Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Zhao, X., Kim, T.-K. (2014). Multiple Object Tracking: A Literature Review. Computer Vision and Pattern Recognition, doi: 10.1016/j.artint.2020.103448.
56. Lv, Z., Poiesi, F., Dong, Q., Lloret, J., & Song, H. (2022). Deep Learning for Intelligent Human–Computer Interaction. Applied Sciences. https://doi.org/10.3390/app122211457.
57. Major, L., Francis, G., & Tsapali, M. (2021). The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis. Br. J. Educ. Technol., 52, 1935-1964. https://doi.org/10.1111/BJET.13116.
58. Malakul, S., & Park, I. (2023). The effects of using an auto-subtitle system in educational videos to facilitate learning for secondary school students: learning comprehension, cognitive load, and satisfaction. Smart Learning Environments, 10(1), 4. https://doi.org/10.1186/s40561-023-00224-2
59. Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022). DeepFake detection for human face images and videos: A survey. Ieee Access, 10, 18757-18775. https://doi.org/10.1109/ACCESS.2022.3151186
60. Mazaheri, A., & Shah, M. (2022, August). Video generation from text employing latent path construction for temporal modeling. In 2022 26th International Conference on Pattern Recognition (ICPR) (pp. 5010-5016). IEEE. https://doi.org/10.1109/ICPR56361.2022.9956706
61. Mengist, W., Soromessa, T., & Legese, G. (2020). Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX, 7, 100777. https://doi.org/10.1016/j.mex.2019.100777
62. Mikos, L. (2016). Digital Media Platforms and the Use of TV Content: Binge Watching and Video-on-Demand in Germany. Media and Communication, 4, 154-161. https://doi.org/10.17645/MAC.V4I3.542.
63. Miyaji, I. (2019). Comparison of Technical Terms and Consciousness of Blended Classes in ‘AI Technology’and ‘Artificial Intelligence'. European Journal of Educational Research, 8(1), 107-121. https://doi.org/10.12973/eu-jer.8.1.107
64. Mousavinasab, E., Zarifsanaiey, N., Kalhori, S., Rakhshan, M., Keikha, L., & Saeedi, M. (2018). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29, 142 - 163. https://doi.org/10.1080/10494820.2018.1558257.
65. Naik, V. S., & Shinde, R. (2022). A Systematic Review and Research Agenda on Corporate Expectations from Management Graduates. International Journal of Management, Technology and Social Sciences (IJMTS), 7(1), 141-162. https://doi.org/10.48175/ijarsct-7041
66. Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2021). Video improves learning in higher education: A systematic review. Review of educational research, 91(2), 204-236.
67. Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. bmj, 372. http://dx.doi.org/10.1136/bmj.n160
68. Park, J., Tiefenbach, J., & Demetriades, A. (2022). The role of artificial intelligence in surgical simulation. Frontiers in Medical Technology, 4. https://doi.org/10.3389/fmedt.2022.1076755.
69. Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013-1022. https://doi.org/10.1038/s42256-021-00417-9
70. Pérez-Navarro, A., García, V., & Conesa, J. (2020). Students perception of videos in introductory physics courses of engineering in face-to-face and online environments. Multimedia Tools and Applications, 80, 1009-1028. https://doi.org/10.1007/s11042-020-09665-0.
71. Pi, Z., Deng, L., Wang, X., Guo, P., Xu, T., & Zhou, Y. (2022). The influences of a virtual instructor's voice and appearance on learning from video lectures. Journal of Computer Assisted Learning, 38(6), 1703-1713. https://doi.org/10.1111/jcal.12704
72. Pittas, E., & Adeyemi, A. (2019). Technology integration in education: Effectiveness, pedagogical use and competence. Lumat: International Journal of Math, Science and Technology Education. https://doi.org/10.31129/lumat.7.1.396.
73. Platt, M., & Platt, D. (2023, October). Effectiveness of Generative Artificial Intelligence for Scientific Content Analysis. In 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-4). IEEE. https://doi.org/10.1109/AICT59525.2023.10313167.
74. Pluzhnikova, N. N. (2020, October). Technologies of artificial intelligence in educational management. In 2020 International Conference on Engineering Management of Communication and Technology (EMCTECH) (pp. 1-6). IEEE. https://doi.org/10.1109/EMCTECH49634.2020.9261561
75. Poquet, O., Lim, L., Mirriahi, N., & Dawson, S. (2018, March). Video And Learning: A Systematic Review (2007--2017). In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 151-160).
76. Pratami, D., & Mirfani, A. (2020). Management of Information and Communication Technologies-Based Curriculum in Private Vocational Education Unit. Proceedings of the 3rd International Conference on Research of Educational Administration and Management (ICREAM 2019). https://doi.org/10.2991/assehr.k.200130.136.
77. Russell, S. J., & Norvig, P. (2010). Artificial intelligence a modern approach. London.
78. Santagata, R., König, J., Scheiner, T., Nguyen, H., Adleff, A. K., Yang, X., & Kaiser, G. (2021). Mathematics teacher learning to notice: A systematic review of studies of video-based programs. ZDM–Mathematics Education, 53(1), 119-134.
79. Santos, I., Castro, L., Rodriguez-Fernandez, N., Torrente-Patiño, Á., & Carballal, A. (2021). Artificial Neural Networks and Deep Learning in the Visual Arts: a review. Neural Computing and Applications, 33, 121 - 157. https://doi.org/10.1007/s00521-020-05565-4.
80. Schmidt, A., Mayer, S., & Buschek, D. (2021). Introduction to Intelligent User Interfaces. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411763.3445021.
81. Şen, N. (2021). Humanoid robots in special education. European Journal of Science and Technology, (32), 832- 842.
82. Sharma, V., Gupta, M., Kumar, A., & Mishra, D. (2021). Video Processing using Deep learning Techniques: A Systematic Literature Review. IEEE Access, PP, 1-1. https://doi.org/10.1109/ACCESS.2021.3118541.
83. Singh, A., & Dhandayuthapani, S. (2022). Overview of image processing technology in healthcare systems. In Data Science for Effective Healthcare Systems (pp. 25-36). Chapman and Hall/CRC.
84. Sivakumar, N., C, K., Easwaran, B., & Tabassum, H. (2023). Design And Analysis of Human Computer Interaction Using AI Intelligence. 2023 International Conference on Disruptive Technologies (ICDT), 195-198. https://doi.org/10.1109/ICDT57929.2023.10150705.
85. Sokolov, I. A. (2019). Theory and practice of application of artificial intelligence methods. Herald of the Russian Academy of Sciences, 89, 115-119. https://doi.org/10.1134/S1019331619020205
86. Stadlinger, B., Jepsen, S., Chapple, I., Sanz, M., & Terheyden, H. (2021). Technology-enhanced learning: a role for video animation. British Dental Journal, 230, 93 - 96. https://doi.org/10.1038/s41415-020-2588-1.
87. Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning. Journal of Educational Psychology, 106, 331-347. https://doi.org/10.1037/A0034752.
88. Susilo, M., Sulisworo, D., & Beungacha, S. (2023). Technology and Its Impact on Education. Buletin Edukasi Indonesia. https://doi.org/10.56741/bei.v2i02.285.
89. Taluğ, D. Y., & Eken, B. (2023). Intersection of human creativity and artificial ıntelligence in visual design. Journal of Art and Iconography, 1(4). Doi:10.5152/articon.2023.1256114
90. Tapalova, O., Zhiyenbayeva, N., & Gura, D. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of e-Learning. https://doi.org/10.34190/ejel.20.5.2597.
91. Taufik, R., & Nurjanah, D. (2019). An Intelligent Tutoring System with Adaptive Exercises Based on a Student’s Knowledge and Misconception. 2019 IEEE International Conference on Engineering, Technology and Education (TALE), 1-5. https://doi.org/10.1109/TALE48000.2019.9226001.
92. Turan, Z., Küçük, S., & Karabey, S. (2022). Investigating Pre-service teachers’ behavioral intentions to use web 2.0 Gamification Tools. Participatory Educational Research, 9(4), 172-189. https://doi.org/10.17275/per.22.85.9.4
93. Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (2017). Learning from synthetic humans. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 109-117).
94. Wang, F. (2019). Computer art design based on artificial intelligence. Cluster Computing, 22(Suppl 6), 13881-13887. https://doi.org/10.1007/s10586-018-2121-3.
95. Wang, Y., & Li, P. (2022). Development and Strategy Analysis of Short Video News Dissemination under the Background of Artificial Intelligence. Mobile Information Systems, 2022(1), 2750925. https://doi.org/10.1155/2022/2750925
96. Whittaker, L., Kietzmann, T. C., Kietzmann, J., & Dabirian, A. (2020). “All around me are synthetic faces”: the mad world of AI-generated media. IT Professional, 22(5), 90-99. https://doi.org/10.1109/MITP.2020.2985492
97. Woo, H., LeTendre, G., Pham-Shouse, T., & Xiong, Y. (2021). The use of social robots in classrooms: A review of field-based studies. Educational Research Review, 33, 100388. https://doi.org/10.1016/J.EDUREV.2021.100388.
98. Wu, Y., Yi, A., Ma, C., & Chen, L. (2023). Artificial intelligence for video game visualization, advancements, benefits and challenges.. Mathematical biosciences and engineering : MBE, 20 8, 15345-15373. https://doi.org/10.3934/mbe.2023686.
99. Xian, D., & Sahagun, J. (2023). An Automated Generation from Video to 3D Character Animation using Artificial Intelligence and Pose Estimate. Artificial Intelligence Advances. https://doi.org/10.5121/csit.2023.130703.
100. Yavuzkilic, S., Sengur, A., Akhtar, Z., & Siddique, K. (2021). Spotting deepfakes and face manipulations by fusing features from multi-stream cnns models. Symmetry, 13(8), 1352. https://doi.org/10.3390/sym13081352
101. Yu, Y., Tu, Z., Lu, L., Chen, X., Zhan, H., & Sun, Z. (2021). Text2Video: Automatic Video Generation Based on Text Scripts. Proceedings of the 29th ACM International Conference on Multimedia. https://doi.org/10.1145/3474085.3478548.
102. Yung, K. (2015). Learning English in the Shadows: Understanding Chinese Learners' Experiences of Private Tutoring. TESOL Quarterly, 49, 707-732. https://doi.org/10.1002/TESQ.193.
103. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 1-18.
104. Zhang, Y., & Wilker, K. (2022). Artificial intelligence and big data driven digital media design. Journal of Intelligent & Fuzzy Systems, 43(4), 4465-4475. https://doi.org/10.3233/JIFS-219433.
105. 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. https://doi.org/10.3390/electronics12010218