Authors : Mekam Kontche Steve

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/48ztyud2

Scribd : https://tinyurl.com/37nkbsvx

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG1297

Abstract : Large Language Models (LLMs) have revolutionized natural language processing, offering significant advancements in educational software through applications like personalized learning and virtual tutoring. This position paper investigates the ethical considerations for companies integrating LLMs into educational tools. Key issues include data privacy, with a focus on safeguarding sensitive student information against breaches while ensuring transparency and consent. The paper highlights the risk of misinformation, as LLMs might generate incorrect or misleading content that could affect students’ learning. It also addresses concerns about algorithmic bias, which can lead to unfair treatment of students from diverse backgrounds, and the potential over-reliance on AI, which may undermine critical thinking and human oversight. Additionally, the paper explores the challenge of equitable access to LLM- based technologies, particularly in underserved communities. The analysis concludes with practical recommendations for companies, including robust data protection measures, balanced AI integration with human oversight, and strategies to enhance access for all students. By emphasizing these ethical challenges, the paper aims to guide responsible AI implementation in education, ensuring that technological advancements benefit all learners fairly and effectively.

Keywords : Large Language Models — Artificial Intelligence — Education — Educational Technologies — Responsible AI.

References :

1. Language Models are Few-Shot Learners by Ilya Sutskever et al.
2. A Comprehensive Overview of Large Language Models. Humza Naveeda, Asad Ullah Khana,∗, Shi Qiub,∗ et al
3. Commercial Software Programs Approved for Teaching Reading and Writing in Primary Grades: Another Sobering Reality. Meridith Lovell and Linda Phillips
4. Comprehension Quiz Generation using Generative Pre-trained Transformers. Ramon Dijkstra, Zu ̈lku ̈f Genc ̧, Subhradeep Kayal, and Jaap Kamps. Reading .
5. Exploring the impact of artificial intelligence on teaching and learning in higher education. Stefan A. D. Popenici and Sharon Kerr
6. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv, 2022. Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov, Emma C. Dyer, Siddhi Ramesh, Yuan Luo, and Alexander T. Pearson.
7. Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT. EdArXiv, 2023. Debby R.E. Cotton, Peter A. Cotton, and J.Reuben Ship- way.
8. UNESCO. Education 2030 Agenda. https:// www.unesco.org/en/digital-education/ artificial-intelligence, 2023. Accessed: 22.01.2023.