References

Bender, Emily M., et al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 610-623. doi:10.1145/3442188.3445922.

Chan, Cecilia Ka Yuk, and Katherine K. W. Lee. “The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?” Smart Learning Environments, vol. 10, no. 1, 2023, p. 60. doi:10.1186/s40561-023-00269-3.

Cotton, Debby R. E., et al. “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT.” Innovations in Education and Teaching International, vol. 61, no. 2, 2023, pp. 228-239. doi:10.1080/14703297.2023.2190148.

Floridi, Luciano. “Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical.” Philosophy & Technology, vol. 32, no. 2, 2019, pp. 185-193. doi:10.1007/s13347-019-00354-x.

Floridi, Luciano, et al. “AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.” Minds and Machines, vol. 28, no. 4, 2018, pp. 689-707. doi:10.1007/s11023-018-9482-5.

Hadi, Muhammad Usman, et al. “A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage.” TechRxiv, 2023. doi:10.36227/techrxiv.23589741.v1.

Jegham, Nidhal, et al. “How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference.” arXiv preprint arXiv:2505.09598, 2025.

Ji, Ziwei, et al. “Survey of Hallucination in Natural Language Generation.” ACM Computing Surveys, vol. 55, no. 12, 2023, pp. 248:1-248:38. doi:10.1145/3571730.

Kasneci, Enkelejda, et al. “ChatGPT for good? On opportunities and challenges of large language models for education.” Learning and Individual Differences, vol. 103, 2023, p. 102274.

Mahapatra, Santosh. “Impact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study.” Smart Learning Environments, vol. 11, no. 1, 2024, p. 9. doi:10.1186/s40561-024-00295-9.

Peláez-Sánchez, Iris Cristina, et al. “The impact of large language models on higher education: exploring the connection between AI and Education 4.0.” Frontiers in Education, vol. 9, 2024, p. 1392091. doi:10.3389/feduc.2024.1392091.

Qian, Yufeng. “Prompt Engineering in Education: A Systematic Review of Approaches and Educational Applications.” Journal of Educational Computing Research, vol. 0, no. 0, 2025, pp. 1-37. doi:10.1177/07356331251365189.

Radford, Alec, et al. “Improving Language Understanding by Generative Pre-Training.” OpenAI, 2018. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.

Shahzad, Tariq, et al. “A comprehensive review of large language models: issues and solutions in learning environments.” Discover Sustainability, vol. 6, no. 1, 2025, p. 27. doi:10.1007/s43621-025-00815-8.

Su, Jiahong, and Weipeng Yang. “Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education.” ECNU Review of Education, vol. 6, no. 3, 2023, pp. 355-366. doi:10.1177/20965311231168423.

Vaswani, Ashish, et al. “Attention is all you need.” Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 5998-6008.

Wei, Jason, et al. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” Advances in Neural Information Processing Systems, vol. 35, 2022.

Winfield, Alan F. T., and Marina Jirotka. “Ethical governance is essential to building trust in robotics and artificial intelligence systems.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 376, no. 2133, 2018, p. 20180085. doi:10.1098/rsta.2018.0085.

Zheng, Lianmin, et al. “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.” Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, 2023.