Acta Informatica Pragensia 2024, 13(3), 460-489 | DOI: 10.18267/j.aip.23511633

Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning

Manel Guettala ORCID...1, Samir Bourekkache ORCID...1, Okba Kazar ORCID...2, Saad Harous ORCID...3
1 Laboratoire de l'INFormatique Intelligente (LINFI), Department of Computer Science, University of Mohamed Khider Biskra, Algeria
2 Department of Computer Science, College of Arts, Sciences, IT & Communication, University of Kalba, Sharjah, United Arab Emirates
3 Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates

The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape. This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education. The study begins with a comprehensive review of the evolution of generative AI models and frameworks. A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education. These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks. Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development. Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists. The findings underscore the transformative potential of generative AI in revolutionizing education. By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments. Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.

Keywords: Ubiquitous learning; AI-driven education; Ethical considerations; ChatGPT; GPT-4o; Content generation; Educational transformation; Technological integration; Education 4.0; Education 5.0.

Received: March 21, 2024; Revised: July 26, 2024; Accepted: July 29, 2024; Published: August 22, 2024  Show citation

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Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024). Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning. Acta Informatica Pragensia, 13(3), 460-489. doi: 10.18267/j.aip.235
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    References

    1. Abdi, H., Valentin, D., & Edelman, B. (1999). Neural networks. Sage. Go to original source...
    2. Abdin, M., Jacobs, S. A., Awan, A. A., Aneja, J., Awadallah, A., Awadalla, H., … Zhou, X. (2024). Phi-3 technical report: A highly capable language model locally on your phone. Microsoft. https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/
    3. Abe, H. (2023). Improving learning experience on the adaptive learning platforms through learning analytics and student feedback. PhD Thesis, School of Biomedical Sciences, The University of Queensland. https://doi.org/10.14264/971f444 Go to original source...
    4. Aidan G. I. Z., & Frosst, N. (2019). Cohere. https://dashboard.cohere.com
    5. Alam, A. (2021). Should robots replace teachers? Mobilisation of AI and learning analytics in education. In International Conference on Advances in Computing, Communication, and Control (ICAC3'2021). IEEE. https://doi.org/10.1109/ICAC353642.2021.9697300 Go to original source...
    6. Alam, M. S., Mohamed, F. B., Selamat, A., & Hossain, A. B. (2023). A review of recurrent neural network based camera localization for indoor environments. IEEE Access, 11, 43985-44009. https://doi.org/10.1109/access.2023.3272479 Go to original source...
    7. Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in Education and Research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971. https://doi.org/10.1021/acs.jchemed.3c00323 Go to original source...
    8. Anthropic. (2024). Claude. https://claude.ai/chats
    9. Aravind Srinivas, D. Y., Konwinski, A., & Ho, J. (2022). Perplexity. https://www.perplexity.ai/
    10. Asmus, J. (2023). Explainpaper. https://www.explainpaper.com/
    11. Assogba, Y., Pearce, A., & Elliott, M. (2023). Large scale qualitative evaluation of generative image model outputs. arXiv preprint. arXiv:2301.04518. https://doi.org/10.48550/arXiv.2301.04518 Go to original source...
    12. Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability, 15(17), 12983. https://doi.org/10.3390/su151712983 Go to original source...
    13. Bai̇doo-Anu, D., & Ansah, L. O. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500 Go to original source...
    14. Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023). The power of Generative AI: a review of requirements, models, Input-Output formats, evaluation metrics, and challenges. Future Internet, 15(8), 260. https://doi.org/10.3390/fi15080260 Go to original source...
    15. Bankins, S. (2021). The ethical use of artificial intelligence in human resource management: a decision-making framework. Ethics and Information Technology, 23(4), 841-854. https://doi.org/10.1007/s10676-021-09619-6 Go to original source...
    16. Berrar, D. (2019). Cross-Validation. In Encyclopedia of Bioinformatics and Computational Biology (pp. 542-545). Elsevier. https://doi.org/10.1016/b978-0-12-809633-8.20349-x Go to original source...
    17. Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), e2218523120. https://doi.org/10.1073/pnas.2218523120 Go to original source...
    18. Black, A. V. G. (2023). Diffit. https://beta.diffit.me
    19. Bozkurt, A., Junhong, X., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., … Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130.
    20. Brandl, L. C., & Schrader, A. (2024). Serious Games in Higher Education in the Transforming Process to Education 4.0-Systematized Review. Education Sciences, 14(3), 281. https://doi.org/10.3390/educsci14030281 Go to original source...
    21. Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at work. National Bureau of Economic Research. https://doi.org/10.3386/w31161 Go to original source...
    22. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. arXiv preprint. arXiv:2303.04226. https://doi.org/10.48550/arXiv.2303.04226 Go to original source...
    23. Chang, T.-Y., & Jia, R. (2022). Data curation alone can stabilize in-context learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, (pp. 8123-8144. Association for Computational Linguistics. https://aclanthology.org/2023.acl-long.452.pdf Go to original source...
    24. Chheang, V., Sharmin, S., Márquez-Hernández, R., Patel, M., Rajasekaran, D., Caulfield, G., Kiafar, B., Li, J., Kullu, P., & Barmaki, R. L. (2024). Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments. In 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). IEEE. https://doi.org/10.1109/aixvr59861.2024.00011 Go to original source...
    25. Costa, V. G., & Pedreira, C. E. (2023). Recent advances in decision trees: an updated survey. Artificial Intelligence Review, 56(5), 4765-4800. https://doi.org/10.1007/s10462-022-10275-5 Go to original source...
    26. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine, 35(1), 53-65. https://doi.org/10.1109/msp.2017.2765202 Go to original source...
    27. Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia: case studies on organization and retrieval (pp. 21-49). Springer. Go to original source...
    28. Dhoni, P. (2023). Exploring the Synergy between Generative AI, Data and Analytics in the Modern Age. TechRxiv. https://doi.org/10.36227/techrxiv.24045792.v1 Go to original source...
    29. Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C., Chen, W., Yi, J., Zhao, W., Wang, X., Liu, Z., Zheng, H., Chen, J., Liu, Y., Tang, J., Li, J., & Sun, M. (2023). Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3), 220-235. https://doi.org/10.1038/s42256-023-00626-4 Go to original source...
    30. Doersch, C. (2016). Tutorial on variational autoencoders. arXiv preprint. arXiv:1606.05908. https://doi.org/10.48550/arXiv.1606.05908 Go to original source...
    31. Dogan, M. E., Dogan, T. G., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A Systematic review of Empirical studies. Applied Sciences, 13(5), 3056. https://doi.org/10.3390/app13053056 Go to original source...
    32. Doo, F. X., Cook, T. S., Siegel, E. L., Joshi, A., Parekh, V., Elahi, A., & Yi, P. H. (2023). Exploring the clinical translation of generative models like ChatGPT: promise and pitfalls in radiology, from patients to population health. Journal of the American College of Radiology, 20(9), 877-885. https://doi.org/10.1016/j.jacr.2023.07.007 Go to original source...
    33. equipo Xmind. (2023). Chatmind. https://chatmind.tech/fr
    34. Fahes, M., Vu, T.-H., Bursuc, A., Pérez, P., & De Charette, R. (2023). Poda: Prompt-driven zero-shot domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 18623-18633). IEEE. Go to original source...
    35. Foster, M. E. (2023). Evaluating the Impact of Supplemental Computer-Assisted Math instruction in Elementary School: A Conceptual Replication. Journal of Research on Educational Effectiveness, 17(1), 94-118. https://doi.org/10.1080/19345747.2023.2174919 Go to original source...
    36. Gamoura, S. C., Koruca, H. İ., & Urganci, K. B. (2024). Exploring the Transition from "Contextual AI" to "Generative AI" in Management: Cases of ChatGPT and DALL-E 2. In Advances in Intelligent Manufacturing and Service System Informatics (pp. 368-381). Springer. https://doi.org/10.1007/978-981-99-6062-0_34 Go to original source...
    37. Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., … Vandirk, S. (2023). Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers. University of Hohenheim. https://wiso.uni-hohenheim.de/fileadmin/einrichtungen/wiso/Forschungsdekan/Papers_BESS/dp_2023-02_online.pdf
    38. Girin, L., Leglaive, S., Bie, X., Diard, J., Hueber, T., & Alameda-Pineda, X. (2021). Dynamical Variational Autoencoders: A Comprehensive review. Foundations and Trends in Machine Learning, 15(1-2), 1-175. https://doi.org/10.1561/2200000089 Go to original source...
    39. Goldstein, J. A., Sastry, G., Musser, M., DiResta, R., Gentzel, M., & Sedova, K. (2023). Generative language models and automated influence operations: Emerging threats and potential mitigations. arXiv preprint. arXiv:2301.04246. https://doi.org/10.48550/arXiv.2301.04246 Go to original source...
    40. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622 Go to original source...
    41. Goodwin, S., Attali, Y., LaFlair, G. T., Runge, A., Park, Y., Davier, A. v., & Yancey, K. P. (2022). Duolingo English Test: Writing construct: Duolingo Research Report DR-22-03. Duolingo. https://duolingo-papers.s3.amazonaws.com/other/writing-whitepaper.pdf Go to original source...
    42. Google. (2024). Gemini. https://gemini.google.com/app
    43. Greek philosopher. (2023). Socratic. https://socratic.org/
    44. Grinding Gear Games. (February 2023). POE. https://poe.com
    45. Gruber, J. B., & Weber, M. (2024). rollama: An R package for using generative large language models through Ollama. arXiv preprint. arXiv:2404.07654. https://doi.org/10.48550/arXiv.2404.07654 Go to original source...
    46. Guettala, M., Bourekkache, S., & Kazar, O. (2021). Ubiquitous learning a new challenge of ubiquitous computing: state of the art. In International Conference on Information Systems and Advanced Technologies (ICISAT'2021). IEEE. https://doi.org/10.1109/ICISAT54145.2021.9678434 Go to original source...
    47. Guettala, M., Bourekkache, S., Kazar, O., Harous, S., & Zouai, M. (2023). The design and implementation of intelligent ubiquitous learning multi-agent context-aware system. World Journal on Educational Technology Current Issues, 15(4), 429-450. https://doi.org/10.18844/wjet.v15i4.9091 Go to original source...
    48. Guettala, M., Harous, S., Bourekkache, S., Athamena, B., Kazar, O., & Houhamdi, Z. (2022). Cloud ubiquitous learning approach based on multi-agents system. In International Arab Conference on Information Technology (ACIT). IEEE. https://doi.org/10.1109/ACIT57182.2022.9994205 Go to original source...
    49. Han, Z., Wang, J., Fan, H., Wang, L., & Zhang, P. (2018). Unsupervised generative modeling using matrix product states. Physical Review X, 8(3). https://doi.org/10.1103/physrevx.8.031012 Go to original source...
    50. Han, Z., Gao, C., Liu, J., Zhang, J., & Zhang, S. Q. (2024). Parameter-efficient fine-tuning for large models: A comprehensive survey. arXiv preprint. arXiv:2403.14608. https://doi.org/10.48550/arXiv.2403.14608 Go to original source...
    51. Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer. Go to original source...
    52. Heston, T., & Khun, C. (2023). Prompt engineering in medical education. International Medical Education, 2(3), 198-205. https://doi.org/10.3390/ime2030019 Go to original source...
    53. Hicks, C. (2023). SchoolAI. https://schoolai.com/
    54. Hind, M., Houde, S., Martino, J., Mojsilovic, A., Piorkowski, D., Richards, J., & Varshney, K. R. (2020). Experiences with improving the transparency of AI models and services. In CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3334480.3383051 Go to original source...
    55. Hitawala, S. (2018). Comparative study on generative adversarial networks. arXiv preprint. arXiv:1801.04271. https://doi.org/10.48550/arXiv.1801.04271 Go to original source...
    56. Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., … Clark, A. (2022). Training compute optimal large language models. In NIPS'22: Proceedings of the 36th International Conference on Neural Information Processing Systems (pp. 30016-30030). ACM.
    57. Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), (pp. 328-339). Association for Computational Linguistics. Go to original source...
    58. Huang, W., Ma, X., Qin, H., Zheng, X., Lv, C., Chen, H., … Magno, M. (2024). How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study. arXiv preprint. arXiv:2404.14047. https://doi.org/10.48550/arXiv.2404.14047 Go to original source...
    59. Italki. (2024). Italki. https://www.italki.com/
    60. Ivanovic, B., Leung, K., Schmerling, E., & Pavone, M. (2021). Multimodal Deep Generative Models for Trajectory Prediction: a conditional variational autoencoder approach. IEEE Robotics and Automation Letters, 6(2), 295-302. https://doi.org/10.1109/lra.2020.3043163 Go to original source...
    61. Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493 Go to original source...
    62. Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., … Sayed, W.E. (2023). Mistral 7B. arXiv preprint. arXiv:2310.06825. https://doi.org/10.48550/arXiv.2310.06825 Go to original source...
    63. Johri, A., Lindsay, E., & Qadir, J. (2023). Ethical concerns and responsible use of generative artificial intelligence in engineering education. Practice papers. Technological University Dublin. https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1113&context=sefi2023_prapap
    64. Karabacak, M., Ozkara, B. B., Margetis, K., Wintermark, M., & Bisdas, S. (2023). The advent of generative language models in medical education. JMIR Medical Education, 9, e48163. https://doi.org/10.2196/48163 Go to original source...
    65. Kem, D. (2022). Personalised and adaptive learning: Emerging learning platforms in the era of digital and smart learning. International Journal of Social Science and Human Research, 5(2), 385-391. https://doi.org/10.47191/ijsshr/v5-i2-02 Go to original source...
    66. Khan, A. (2023). MagicSchool. https://www.magicschool.ai/
    67. Khanzode, K. C. A., & Sarode, R. D. (2020). Advantages and disadvantages of artificial intelligence and machine learning: A literature review. International Journal of Library & Information Science, 9(1), 30-36.
    68. Khusid. A. (2023). Miro. https://miro.com
    69. Kleinbaum, D. G., & Klein, M. (2002). Logistic regression. Springer.
    70. Krichen, M. (2023). Convolutional Neural Networks: a survey. Computers, 12(8), 151. https://doi.org/10.3390/computers12080151 Go to original source...
    71. Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2019). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121-204. https://doi.org/10.1007/s40593-019-00186-y Go to original source...
    72. Lam, A. L. (2023). Studdy. https://studdy.ai/
    73. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 Go to original source...
    74. Leiker, D., Gyllen, A. R., Eldesouky, I., & Cukurova, M. (2023). Generative AI for Learning: Investigating the Potential of Learning Videos with Synthetic Virtual Instructors. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (pp. 523-529). Springer. https://doi.org/10.1007/978-3-031-36336-8_81 Go to original source...
    75. Lichtenberger, M. (2023). ChatPDF. https://www.chatpdf.com/?via=lting
    76. Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: ethical challenges for medical publishing. The Lancet Digital Health, 5(3), e105-e106. https://doi.org/10.1016/s2589-7500(23)00019-5 Go to original source...
    77. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790 Go to original source...
    78. Magee, J. F. (1964). Decision trees for decision making. Harvard Business Review. https://hbr.org/1964/07/decision-trees-for-decision-making
    79. Mao, R., Chen, G., Zhang, X., Guerin, F., & Cambria, E. (2023). GPTEval: A survey on assessments of ChatGPT and GPT-4. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (pp. 7844-7866). ACL. https://aclanthology.org/2024.lrec-main.693
    80. Mayer, R. E., Makransky, G., & Parong, J. (2023). The promise and pitfalls of learning in immersive virtual reality. International Journal of Human-Computer Interaction, 39(11), 2229-2238. https://doi.org/10.1080/10447318.2022.2108563 Go to original source...
    81. Koedinger, K. R., McLaughlin, E. A., & Heffernan, N. T. (2010) A Quasi-Experimental Evaluation of an On-line Formative Assessment and Tutoring System. Journal of Educational Computing Research, 43(1), 489-510. Go to original source...
    82. Melzer, P. (2019). A Conceptual Framework for Personalised Learning: Influence Factors, Design, and Support Potentials. Springer. Go to original source...
    83. Microsoft. (2023). Copilot. https://copilot.microsoft.com/
    84. Mills, A., Bali, M., & Eaton, L. (2023). How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process. Journal of Applied Learning & Teaching, 6(1), 16-30. https://doi.org/10.37074/jalt.2023.6.1.34 Go to original source...
    85. Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259-265. https://doi.org/10.1038/s41586-023-05881-4 Go to original source...
    86. Mote Technologies. (2023). Conker. https://www.conker.ai/
    87. Nah, F. F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304. https://doi.org/10.1080/15228053.2023.2233814 Go to original source...
    88. Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., … Mian, A. (2023). A comprehensive overview of large language models. arXiv preprint. arXiv:2307.06435. https://doi.org/10.48550/arXiv.2307.06435 Go to original source...
    89. Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565-1567. https://doi.org/10.1038/nbt1206-1565 Go to original source...
    90. Nysom, L. (2023). AI Generated Feedback for Students' Assignment Submissions: A case study in generating feedback for students' submissions using ChatGPT. Master project. University College of Northern Denmark. https://projekter.aau.dk/projekter/files/547261577/Lars_Nysom_Master_Project.pdf
    91. Openai. (2024). GPT-4o. https://openai.com/index/hello-gpt-4o/
    92. Opderbeck, D. W. (2019). Artificial Intelligence in Pharmaceuticals, Biologics, and Medical Devices: Present and Future Regulatory Models. Fordham Law Review, 88(2), Article 7.
    93. Ought. (2023). Elicit. https://elicit.com/
    94. Patton, D. U., Landau, A. Y., & Mathiyazhagan, S. (2023). ChatGPT for Social Work Science: Ethical Challenges and Opportunities. Journal of the Society for Social Work and Research, 14(3), 553-562. https://doi.org/10.1086/726042 Go to original source...
    95. Patwardhan, N., Marrone, S., & Sansone, C. (2023). Transformers in the Real World: A survey on NLP applications. Information, 14(4), 242. https://doi.org/10.3390/info14040242 Go to original source...
    96. Peñalvo, F. J. G., Llorens-Largo, F., & Vidal, J. (2023). La nueva realidad de la educación ante los avances de la inteligencia artificial generativa. RIED Revista Iberoamericana De Educación a Distancia, 27(1), 9-39. https://doi.org/10.5944/ried.27.1.37716 Go to original source...
    97. Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 219(1), 103-119. https://doi.org/10.1243/095440605x8298 Go to original source...
    98. Peeperkorn, M., Kouwenhoven, T., Brown, D., & Jordanous, A. (2024). Is temperature the creativity parameter of large language models? arXiv preprint. arXiv:2405.00492. https://doi.org/10.48550/arXiv.2405.00492 Go to original source...
    99. Peis, I., Olmos, P. M., & Artés-Rodríguez, A. (2023). Unsupervised learning of global factors in deep generative models. Pattern Recognition, 134, 109130. https://doi.org/10.1016/j.patcog.2022.109130 Go to original source...
    100. Pesenti, J. (2023). Sizzle. https://web.szl.ai/
    101. Poggio, T., Torre, V., & Koch, C. (1987). Computational vision and regularization theory. In Readings in Computer Vision (pp. 638-643). https://doi.org/10.1016/b978-0-08-051581-6.50061-1 Go to original source...
    102. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. PrePrint. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
    103. Rane, N. L. (2024). ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges, and opportunities for Industry 4.0, Industry 5.0, and Society 5.0. Innovations in Business and Strategic Management, 2(1), 10-17. https://doi.org/10.61577/ibsm.2024.100002 Go to original source...
    104. Rasul, T., Nair, S., Kalendra, D., Robin, M., De Oliveira Santini, F., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning & Teaching, 6(1), 41-56. https://doi.org/10.37074/jalt.2023.6.1.29 Go to original source...
    105. Regenwetter, L., Nobari, A. H., & Ahmed, F. (2022). Deep Generative Models in Engineering Design: a review. Journal of Mechanical Design, 144(7), 071704. https://doi.org/10.1115/1.4053859 Go to original source...
    106. Ross, J. (2016). groq. https://groq.com/
    107. Roy, A., & Chakraborty, S. (2023). Support vector machine in structural reliability analysis: A review. Reliability Engineering & System Safety, 233, 109126. https://doi.org/10.1016/j.ress.2023.109126 Go to original source...
    108. Schick, T., & Schütze, H. (2022). True few-shot learning with Prompts-A real-world perspective. Transactions of the Association for Computational Linguistics, volume 10, (pp. 716-731). MIT Press. https://doi.org/10.1162/tacl_a_00485 Go to original source...
    109. Shanto, S. S., Ahmed, Z., & Jony, A. I. (2024). Enriching Learning Process with Generative AI: A Proposed Framework to Cultivate Critical Thinking in Higher Education using Chat GPT. Journal of Propulsion Technology, 45(1), 3019-3029.
    110. Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019). Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint. arXiv:1909.08053. https://doi.org/10.48550/arXiv.1909.08053 Go to original source...
    111. Showbie. (2024). Socrative. https://www.socrative.com/
    112. Shrestha, R., Kafle, K., & Kanan, C. (2022). An investigation of critical issues in bias mitigation techniques. In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE. https://doi.org/10.1109/WACV51458.2022.00257 Go to original source...
    113. Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT - Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74, 102700. https://doi.org/10.1016/j.ijinfomgt.2023.102700 Go to original source...
    114. Temsah, M., Jamal, A., Aljamaan, F., Al-Tawfiq, J. A., & Al-Eyadhy, A. (2023). CHATGPT-4 and the Global Burden of Disease Study: Advancing Personalized Healthcare through Artificial Intelligence in Clinical and Translational Medicine. Cureus, 15(5), e39384. https://doi.org/10.7759/cureus.39384 Go to original source...
    115. Tirumala, K., Markosyan, A., Zettlemoyer, L., & Aghajanyan, A. (2022). Memorization without overfitting: Analyzing the training dynamics of large language models. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022). NeurIPS.
    116. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., … Lample, G. (2023). Llama: Open and efficient foundation language models. arXiv preprint. arXiv:2302.13971. https://doi.org/10.48550/arXiv.2302.13971 Go to original source...
    117. Varią, D., & Bojar, O. (2021). Sequence length is a domain: Length-based overfitting in transformer models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 8246-8257). ACL. https://doi.org/10.18653/v1/2021.emnlp-main.650 Go to original source...
    118. Wang, C., Li, M., & Smola, A. J. (2019). Language models with transformers. arXiv preprint. arXiv:1904.09408. https://doi.org/10.48550/arXiv.1904.09408 Go to original source...
    119. Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793-803. https://doi.org/10.1080/10494820.2020.1808794 Go to original source...
    120. Wang, W., Lin, X., Feng, F., He, X., & Chua, T.-S. (2023). Generative recommendation: Towards next-generation recommender paradigm. arXiv preprint. arXiv:2304.03516. https://doi.org/10.48550/arXiv.2304.03516 Go to original source...
    121. Watters, A. (2023). Teaching machines: The history of personalized learning. MIT Press.
    122. Weisberg, S. (2005). Applied linear regression. John Wiley & Sons. Go to original source...
    123. Wetzel, S. J. (2017). Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders. Physical Review E, 96(2), 022140. https://doi.org/10.1103/physreve.96.022140 Go to original source...
    124. Whitham, R., Stockton, G., Richards, D., Lindley, J., Jacobs, N., & Coulton, P. (2018). Implications of Generative AI on Learning and Assessment in Higher Education and Design Research Practice. TU Delft. https://studiolab.ide.tudelft.nl/studiolab/genai-dis2023/files/2023/07/Implications-of-Generative-AI-on-Higher-Education-Assessment-0.pdf
    125. Wu, J., & Mao, Z. (2023). Research on the Digital Competency Improvement Path of Higher Vocational UAV Application Technology Professional Teachers Based on AI Technology. In 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023). Atlantis Press. https://doi.org/10.2991/978-94-6463-264-4_66 Go to original source...
    126. Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., & Chen, X. (2023). Large language models as optimizers. arXiv preprint. arXiv:2309.03409. https://doi.org/10.48550/arXiv.2309.03409 Go to original source...
    127. Yang, L., Yang, G., Bing, Z., Tian, Y., Niu, Y., Huang, L., & Yang, L. (2021). Transformer-Based generative model accelerating the development of novel BRAF inhibitors. ACS Omega, 6(49), 33864-33873. https://doi.org/10.1021/acsomega.1c05145 Go to original source...
    128. Yin, J., Huang, Y., & Ma, Z. (2023). Explore the feeling of presence and purchase intention in livestream shopping: a Flow-Based model. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 237-256. https://doi.org/10.3390/jtaer18010013 Go to original source...
    129. Zamfir, F. S., & Pricop, E. (2022). On the design of an interactive automatic Python programming skills assessment system. In 14th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE. https://doi.org/10.1109/ECAI54874.2022.9847414 Go to original source...
    130. Zeadally, S., Adi, E., Baig, Z., & Khan, I. A. (2020). Harnessing artificial intelligence capabilities to improve cybersecurity. IEEE Access, 8, 23817-23837. https://doi.org/10.1109/access.2020.2968045 Go to original source...
    131. Zhang, C., Zhang, C., Zhang, M., & Kweon, I. S. (2023). Text-to-image Diffusion Models in Generative AI: A Survey. arXiv preprint. arXiv:2303.07909. https://doi.org/10.48550/arXiv.2303.07909 Go to original source...
    132. Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, 100339. https://doi.org/10.1016/j.edurev.2020.100339 Go to original source...
    133. Zheng, C., Wu, G., Bao, F., Cao, Y., Li, C., & Zhu, J. (2023). Revisiting discriminative vs. generative classifiers: Theory and implications. In Proceedings of the 40th International Conference on Machine Learning. PLMR. https://proceedings.mlr.press/v202/zheng23f/zheng23f.pdf
    134. Zhou, L., Varadharajan, V., & Hitchens, M. (2013). Achieving secure Role-Based access control on encrypted data in cloud storage. IEEE Transactions on Information Forensics and Security, 8(12), 1947-1960. https://doi.org/10.1109/tifs.2013.2286456 Go to original source...
    135. Zhou, Z.-H. (2021). Machine learning. Springer Nature. Go to original source...
    136. Zhu, B., & Rao, Y. (2023). Exploring Robust Overfitting for Pre-trained Language Models. In Findings of the Association for Computational Linguistics: ACL 2023 (pp. 5506-5522). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.340 Go to original source...
    137. Zohny, H., McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of Medical Ethics, 49(2), 79-80. https://doi.org/10.1136/jme-2023-108909 Go to original source...

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