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 beyond 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 imbalances 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 equity-centered implementation, culturally responsive design, robust governance, and sustained professional development to ensure AI serves as a tool for inclusion rather than a mechanism for reinforcing educational disparities.