Artificial Intelligence in Classroom Assessment: Opportunities, Equity Challenges, and Best Practices for Formative and Summative Integration

Abstract

Artificial Intelligence (AI) is rapidly transforming classroom assessment by enabling adaptive, data-driven, and personalized approaches that extend be-yond the limitations of traditional methods. This paper presents a systematic literature review of recent peer-reviewed studies focusing on AI integration in both formative and summative assessments. The review synthesizes evidence on four thematic areas: personalization, efficiency, and scalability; equity and inclusion challenges; ethical and transparency concerns; and methodological gaps in current research. Findings indicate that AI-powered tools—such as adaptive learning platforms, automated scoring systems, and natural language processing applications enhance feedback timeliness, assessment accuracy, and instructional responsiveness, particularly in high-resource settings with strong infrastructure, teacher training, and policy support. However, persistent challenges related to algorithmic bias, data privacy, transparency, and unequal access in low-resource contexts limit equitable adoption. Methodological im-balances in the literature, including overreliance on pilot studies in affluent contexts, further constrain generalizability. The paper concludes that realizing AI’s transformative potential in classroom assessment requires equi-ty-centered implementation, culturally responsive design, robust governance, and sustained professional development to ensure AI serves as a tool for inclu-sion rather than a mechanism for reinforcing educational disparities.