Citation

Bukar, Umar Ali and Sayeed, Md. Shohel and Abdul Razak, Siti Fatimah and Yogarayan, Sumendra and Sneesl, Radhwan (2024) Decision-Making Framework for the Utilization of Generative Artificial Intelligence in Education: A Case Study of ChatGPT. IEEE Access, 12. pp. 95368-95389. ISSN 2169-3536

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Abstract

The increasing integration of ChatGPT, a Generative Artificial Intelligence (Gen-AI) model, into educational environments has sparked substantial ethical concerns. This paper addresses the crucial question of whether to impose restrictions or legislate the usage of Gen-AI, with ChatGPT as a pivotal case study. Through systematic literature review and frequency of occurrence analysis, 10 ethical concerns were selected for further analysis using the Analytic Hierarchy Process (AHP). The analysis responses of 10 expert panels show that the top concerns, as revealed by their weights, after meeting the consistency requirement, include copyright, legal, and compliance issues (0.1731), privacy and confidentiality (0.1286), academic integrity (0.1206), incorrect reference and citation practices (0.1111), and safety and security concerns (0.1050). Evaluating the impact of these concerns on the policy alternatives (restriction and legislation), the findings revealed that ‘‘Restriction’’ received a higher weight (0.513712) compared to ‘‘Legislation’’ (0.485887). Notably, copyright, legal, and compliance issues, privacy and confidentiality, and academic integrity emerged as crucial factors influencing the decision between restriction and legislation. This study offers valuable insights for educational institutions and policymakers, suggesting the need for inclusive discussions, pilot programs to assess impacts on critical thinking, development of clear guidelines, flexible regulatory frameworks, awareness campaigns, and potential strategies for ethical and responsible use.

Item Type:	Article
Uncontrolled Keywords:	ChatGPT, decision making, AHP, ethical concerns, restriction, legislation
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2024 02:02
Last Modified:	01 Aug 2024 02:02
URII:	http://shdl.mmu.edu.my/id/eprint/12689

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