Outcomes of Large Language Models and Artificial Intelligence in Education


  • Andrada-Iulia State S.C. THECON S.R.L.
  • Georgiana-Alexandra Morosanu Dunărea de Jos University of Galati
  • Laura-Andreea Rata S.C. THECON S.R.L.
  • Marius Geru S.C. THECON S.R.L.


review; education; AI; learning; technologies


The recent development in Artificial Intelligence, specifically with the advent of large language models like GPT-4, is transforming educational paradigms. This literature review investigates the implications of AI and large language models in education, outlining a vision for personalized, on-demand and interactive learning. This research examines how these technologies can function as virtual tutors, encourage real-time examination, support lifelong education, and function as teachers' resources by automating administrative tasks. The paper also addresses the ethical concerns of utilizing AI in educational frameworks and advocating for a complement rather than a replacement of teachers. Additionally, it investigates their ability to promote real-time assessment, enabling students to receive immediate feedback and adapt their learning strategies accordingly. Moreover, this review highlights the potential of AI and large language models to support improving knowledge, competencies and new skills development. By providing learners access to vast knowledge repositories, these technologies empower individuals to pursue continuous learning beyond traditional classroom settings. By enabling a general understanding of the arising paradigm, the aim is to design a framework for future education systems where AI plays a constructive role.

Author Biographies

Andrada-Iulia State, S.C. THECON S.R.L.

Project Assistant

Georgiana-Alexandra Morosanu, Dunărea de Jos University of Galati

PhD Eng.

Laura-Andreea Rata, S.C. THECON S.R.L.

Marketing researcher

Marius Geru, S.C. THECON S.R.L.

Project Manager


Mariani, M., Machado, I., Magrelli, V., Dwivedi, Y. (2023). Artificial intelligence in innovation research: a systematic review, conceptual framework, and future research directions. Technovation, Vol. 122, pp. 1-25.

Androidro. (2023). How Chat GPT-4, artificial intelligence, can help us and what it can do. Web page. Retrieved from https://www.androidro.ro/cum-ne-poate-ajuta-si-ce-stie-sa-faca-chat-gpt-4-inteligenta-artificiala/, date: 20.08.2023.

OpenAI. (2015). About. Web page. Retrieved from https://openai.com/about, date: 20.08.2023.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, Vol. 1(8):9, 1-24.

Xipeng, Q., Tianxiang, S., Yige, X., Yunfan, S., Ning, D., Xuanjing, H. (2020). Pre-trained models for natural language processing: a survey. Science China Technological Sciences, Vol. 63, pp. 1872-1897.

Machine Intelligence Update. (2023). GPT-1 to GPT-4: The evolution of AI language models. Web page. Retrieved from https://www.youtube.com/watch?v=dNFC57Bz10c, date: 20.08.2023.

MUO. (2023), GPT-1 to GPT-4: Each of OpenAI's GPT Models Explained and Compared. Web page. Retrieved from https://www.makeuseof.com/gpt-models-explained-and-compared/, date: 20.08.2023.

IP Bytes. (2023). What is ChatGPT and how does it work? Web page. Retrieved from https://blogs.luc.edu/ipbytes/2023/04/22/chat-gpt-should-we-have-a-chat-about-ips-role/, date: 20.08.2023.

Geeki. (2023). What is GPT Chat and how is it used? Web page. Retrieved from

https://geeki.ro/ce-este-gpt-chat-si-cum-se-foloseste/#ce-este-gpt-chat, date: 20.08.2023.

Tooabstractive. (2023). GPT-4. Web page. Retrieved from

https://tooabstractive.com/how-to-tech/difference-between-gpt-1-gpt-2-gpt-3-gpt-4/?utm_content=cmp-true, date: 20.08.2023.

MetaversePost. (2023). GPT-4 vs. GPT-3: What does the new model have to offer? Web page. Retrieved from https://mpost.io/ro/gpt-4-vs-gpt-3/, date: 20.08.2023.

OpenAI (2023). Privacy policy. Web page. Retrieved from https://openai.com/policies/privacy-policy, date 19.08.2023.

Twitter (2023). Post form Dan Miller. Web Page. Retrieved from

https://twitter.com/danmiller999/status/1636780580431093760, date: 19.08.2023.

Fieldfisher (2023). Unveiling the Crucial 5 GDPR Obstacles of ChatGPT That Can’t Be Ignored. Web Page. Retrieved from https://www.fieldfisher.com/en/insights/unveiling-the-crucial-5-gdpr-obstacles-of-chatgpt, date: 19.08.2023.

Reuters (2023). China reports first arrest over fake news generated by ChatGPT. Web Page. Retrieved from https://www.reuters.com/technology/china-reports-first-arrest-over-fake-news-generated-by-chatgpt-2023-05-10/, date: 19.08.2023

Eur-lex Europa (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Web page. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679 , date: 19.08.2023.

Cameron Coles (2023). 11% of data employees paste into ChatGPT is confidential. Web page. Retrieved from https://www.cyberhaven.com/blog/4-2-of-workers-have-pasted-company-data-into-chatgpt/ , date: 19.08.2023.

Garante per la protezione dei dati personali (2023). ChatGPT: Italian SA to lift temporary limitation if OpenAI implements measures 30 April set as deadline for compliance. Web Page. Retrieved from https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/9874751#english. Date: 19.08.2023

Open AI (2023). GPT-4 Technical Report. Web page. Retrieved from https://cdn.openai.com/papers/gpt-4.pdf, date: 19.08.2023

Yasmina Yakimova, Janne Ojamo (2023). MEPs ready to negotiate first-ever rules for safe and transparent AI. Web page. Retrieved from

https://www.europarl.europa.eu/news/en/press-room/20230609IPR96212/meps-ready-to-negotiate-first-ever-rules-for-safe-and-transparent-ai, date: 19.08.2023.

Ng, D.T.K., Lee, M., Tan, R.J. Y., Hu, X., Downie, J.S., & Chu, S.K.W. (2022). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, pp. 1-57.

Baidoo-Anu, D., Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN, pp. 1-20.

Fitria, T.N. (2021). Artificial Intelligence (AI) in education: using AI tools for teaching and learning process. In Prosiding Seminar Nasional & Call for Paper STIE AAS, Vol. 4, No. 1, pp. 134-147.

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., Gašević, D. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, pp. 1-23.

Zhang, J., Ji, X., Zhao, Z., Hei, X., Choo, K.K.R. (2023). Ethical considerations and policy implications for large language models: guiding responsible development and deployment. Arxiv, pp. 1-5.

Zhou, J., Müller, H., Holzinger, A., Chen, F. (2023). Ethical ChatGPT: Concerns, challenges, and commandments. Arxiv, pp. 1-8.

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, Vol. 103, pp. 1-9.




How to Cite

State, A.-I., Morosanu, G.-A., Rata, L.-A., & Geru, M. (2023). Outcomes of Large Language Models and Artificial Intelligence in Education. Didactica Danubiensis, 3(1), 30–52. Retrieved from https://dj.univ-danubius.ro/index.php/DD/article/view/2473



Educational Management