METHODOLOGIES OF ARTIFICIAL INTELLIGENCE IN ASSESSMENT FOR LEARNING IN HIGHER EDUCATION: A SYNTHESIS OF RECENT REVIEWS
Keywords:
Artificial Intelligence, Assessment for Learning, Higher Education, Automated Feedback, Learning Analytics, Responsible AI.Abstract
This article examines methodological approaches to the application of Artificial Intelligence (AI) in assessment within higher education. The study is based exclusively on the analysis of two recent reviews: C. Zhao (2024), which synthesizes 81 empirical studies on AI-assisted assessment in universities and B. Memarian and T. Doleck (2024), which reviews 35 studies on Assessment for Learning (AFL) supported by AI. The article identifies key AI methodologies, including intelligent tutoring and personalized learning, automated assessment and feedback, learning analytics and prediction, virtual classroom support, knowledge management systems and educational chat assistants. It further analyzes the pedagogical foundations of AI-supported AFL, emphasizing formative design, continuous feedback and student learning growth. The findings indicate that AI methodologies enhance cognitive and metacognitive skills, provide real-time personalized feedback and foster positive academic emotions. However, significant challenges remain, including privacy concerns, algorithmic bias, unreliable feedback, academic integrity risks and insufficient guidance for responsible use. The study highlights the importance of user acceptance, teacher TPACK competence and discipline-specific calibration. The article concludes that AI methodologies in assessment require structured guidelines and responsible implementation to ensure pedagogical coherence and ethical use in higher education.
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