Ethical implications of artificial intelligence-based academic integrity systems in Palestinian higher education: a systematic review

A systematic review of academic literature from 2012-2024 highlights significant ethical concerns surrounding the deployment of AI-based academic integrity systems in higher education. While these systems offer scalable solutions for detecting academic dishonesty through natural language processing, stylometry, and machine learning, they introduce risks of algorithmic bias, limited transparency, and potential infringements on privacy and academic freedom due to increased surveillance.

Why it matters

The increasing integration of AI into critical functions like academic integrity requires careful consideration of its ethical implications. Unaddressed issues such as bias, transparency, and privacy can erode trust in educational institutions and compromise the fairness and equity of academic processes, affecting student outcomes and institutional reputation.

Key insights

• AI-powered systems, leveraging NLP, stylometry, and machine learning, are proposed as scalable solutions for academic integrity concerns.
• Potential for algorithmic bias, particularly against non-native English speakers, exists within these systems.
• Challenges in algorithmic transparency limit understanding and accountability of decision-making processes.
• Increased surveillance capabilities inherent in AI systems raise concerns about privacy and academic freedom violations.
• The review was conducted on English-language peer-reviewed literature across six major databases over a 12-year period (2012-2024).

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1905889