Ethical Implications of AI in Student Assessments: A Critical Examination

Abstract

This research delves into the ethical dimensions of employing Artificial Intelligence (AI) in student assessments within an Asian University. A comparative analysis between AI grading and human grading highlights AI's commendable 90% accuracy but raises concerns about its consistency compared to human grading's 95%. This trade-off underscores the need for transparent evaluation criteria and bias mitigation mechanisms, as AI systems' consistent undervaluation or overvaluation of student work may lead to unfair outcomes. The study examines AI grading bias and its impact on demographic groups, revealing negative bias scores in gender (-0.2), race/ethnicity (0.1), and socioeconomic status (-0.3). Addressing biases becomes imperative to ensure fair assessments, especially when AI grading exacerbates existing disparities. Privacy concerns related to AI student monitoring are discussed, detailing the types of collected data, their purposes, and retention periods. Ethical considerations revolve around responsibly handling sensitive student data and ensuring robust data security. The research explores the impact of AI-generated feedback on student confidence, noting a 70% positive impact alongside potential drawbacks, such as the absence of nuanced understanding found in human-generated feedback. Balancing timely assessments with empathetic feedback is crucial ethically. Furthermore, accessibility features in AI-enabled assessment tools are highlighted, aiming to provide equitable access for students with diverse needs. Ethical considerations stress the importance of fair opportunities for all students, regardless of their backgrounds. This research offers a comprehensive examination of the ethical implications of AI in student assessments, providing valuable insights for educators, policymakers, and technologists.