Refers to the tendency of AI systems to make unfair or prejudiced decisions, often due to skewed data or flawed algorithmic design. AI bias can result in discrimination and misrepresentation of certain groups.
Published in Chapter:
Automated Assessment and Feedback in Higher Education Using Generative AI
Fawad Naseer (Beaconhouse International College, Pakistan), Muhammad Usama Khalid (Beaconhouse International College, Pakistan),
Nafees Ayub (Government College University, Faisalabad, Pakistan), Akhtar Rasool (Università Degli Studi di Milano Bicocca, Italy), Tehseen Abbas (University of Education, Pakistan), and
Muhammad Waleed Afzal (Beaconhouse International College, Pakistan)
Copyright: © 2024
|Pages: 29
DOI: 10.4018/979-8-3693-1351-0.ch021
Abstract
This chapter explores the integration of generative AI in higher education assessment, addressing the inadequacies of traditional methods in meeting the diverse needs of contemporary learners. It highlights the potential of AI technologies, such as natural language processing and computer vision, to offer personalized, scalable, and insightful evaluations. The chapter critically examines both the enhanced capabilities introduced by AI in educational settings and the ethical challenges it poses. Emphasizing the need for a balanced approach, it suggests synergizing AI's analytical strengths with human expertise to ensure equitable and effective assessments. This work aims to guide educators, administrators, and policymakers through the complexities of AI adoption in academic evaluation, focusing on maintaining academic integrity and inclusivity while leveraging the transformative potential of AI in education.