Operationalizing Bloom’s Mastery Learning Through DARTS: Classroom Response Systems and AI Conversational Micro Tutoring

Bloom’s mastery-learning framework proposed that achievement should depend on mastery rather than fixed instructional time (Bloom, 1968). Later, Bloom’s 2 Sigma Problem demonstrated that individualized tutoring can significantly improve learning outcomes compared with conventional classroom instruction, while also revealing the difficulty of scaling one-to-one tutoring in higher education. DARTS attempts to address this challenge by combining classroom-based formative assessment with individualized AI-supported conversational tutoring beyond scheduled instructional time. As the third contribution in the DARTS research series, this work examines the platform implementation and classroom workflow of the Dynamic Academic Response and Tutoring System (DARTS) within a staging-based operational testing environment. Extending earlier conceptual and architectural studies, the paper documents how DARTS integrates classroom interaction, formative assessment, gap analysis, and conversational micro-tutoring within an integrated instructional framework. DARTS functions as a mobile-first Student Response System (SRS) that captures attendance, quizzes, brainstorming responses, and topic-level learning gaps in real time while assigning individualized micro-tutorials for mastery reinforcement beyond classroom sessions. Rather than presenting a completed causal evaluation, this paper focuses on implementation and deployment methodology and demonstrates the feasibility of DARTS as an AI-supported mastery-learning framework for higher education through system workflows and screenshots.