The integration of Artificial Intelligence (AI) into undergraduate education

Waleed Salameh *

College of Graduate Studies - An-Najah National University, - Nablus, - Palestine.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 2455–2463.
Article DOI: 10.30574/ijsra.2024.13.1.1916
Publication history: 
Received on 05 September 2024; revised on 12 October 2024; accepted on 14 October 2024
 
Abstract: 
The incorporation of AI as a tool in educating undergraduates is a promising concept held with a great potential of positively influencing the learning process, learners’ experiences and institutional management systems. At the same time, it lifts essential moral issues that ought to be adequately addressed into new stages. The aim of this paper is to review literature on the subject of ethics in the application of AI in the context of an undergraduate degree program. Algorithmic choices for instructional design are another relevant ethical concern along with privacy and data protection, practitioners’ responsibilities, and the requisite levels of transparency, as well as the depersonalized and mechanized nature of learning management systems. The analysis reveals how the use of AI technology might deepen the social and economic inequities the students have described and how difficult it is to keep the students’ trust in cases when AI technology is not transparent. Also, the paper raises concerns related to several ethical issues and their consequences to students, teachers, and institutions on how strong ethical principles and policies to apply AI in the educational sectors. It is established that the use of AI in the delivery of undergraduate education has the potential of enhancing the effectiveness of learning(). Nonetheless, it is recommended that the integration of AI in the classroom must be done with reference to several ethical concerns so as to prevent further negative ramifications of the technology.
 
Keywords: 
Artificial Intelligence (AI); Ethical Concerns; Undergraduate Education Bias and Fairness; Privacy and Data Security
 
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