AI and student assessment

This article examines the transformative role of artificial intelligence (AI) in student assessment within contemporary educational settings. Drawing on current implementations across international education systems, we analyze how AI technologies are reshaping assessment design, administration, scoring, and feedback mechanisms. The paper explores key benefits including automated question generation, real-time monitoring, personalized feedback systems, and enhanced accessibility features. We address critical challenges surrounding academic integrity, algorithmic bias, and the balance between technological efficiency and human judgment. Through examination of case studies, including International Baccalaureate programs, we identify effective strategies for AI integration that maintain pedagogical integrity whilst leveraging technological advantages. Our findings suggest that successful AI implementation in assessment requires careful collaboration between educators and technologists, robust AI literacy development, and ethical frameworks that prioritize equity and fairness. The article concludes with recommendations for future directions in AI-driven assessment that support both individual learning needs and broader educational objectives.

Authors

Publication Details

Journal
International Journal of Science and Research Archive
Published
2025-08-10
DOI
https://doi.org/10.30574/ijsra.2025.16.2.2225
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

AI and student assessment

Paul Main
International Journal of Science and Research Archive
Intelligent Tutoring Systems and Adaptive Learning
article

AI and student assessment

Paul Main
article en

Abstract

This article examines the transformative role of artificial intelligence (AI) in student assessment within contemporary educational settings. Drawing on current implementations across international education systems, we analyze how AI technologies are reshaping assessment design, administration, scoring, and feedback mechanisms. The paper explores key benefits including automated question generation, real-time monitoring, personalized feedback systems, and enhanced accessibility features. We address critical challenges surrounding academic integrity, algorithmic bias, and the balance between technological efficiency and human judgment. Through examination of case studies, including International Baccalaureate programs, we identify effective strategies for AI integration that maintain pedagogical integrity whilst leveraging technological advantages. Our findings suggest that successful AI implementation in assessment requires careful collaboration between educators and technologists, robust AI literacy development, and ethical frameworks that prioritize equity and fairness. The article concludes with recommendations for future directions in AI-driven assessment that support both individual learning needs and broader educational objectives.

International Journal of Science and Research ArchiveVol. 16(2)
Openalex Percentile: Top 89%
Intelligent Tutoring Systems and Adaptive Learning
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