Reconstructing Education in the Age of Generative AI: A Formal Framework for Measuring Reasoning Integrity, Conceptual Understanding, and AI Dependence

This paper proposes a formal educational assessment framework for the age of generative artificial intelligence. As large language models increasingly enable students to generate fluent essays, reports, summaries, and analyses with minimal effort, traditional output-based assessment systems face a growing validity crisis. The paper argues that education must transition from evaluating polished outputs alone toward evaluating reasoning integrity: the alignment between what students produce and what they demonstrably understand. The framework introduces three complementary assessment mechanisms: Differential Output Comparison (DOC) Debate and Oral Argument Testing (DOAT) Conceptual Modeling and Mathematical Abstraction (CMMA) Together, these systems aim to distinguish productive AI augmentation from conceptual substitution by evaluating students’ ability to explain, defend, reconstruct, transfer, and model the ideas represented in their work. The paper further formalizes a set of proposed assessment indices, including: AI Dependence Index (ADI) Understanding Integrity Score (UIS) Fluency–Understanding Differential (FUD) Model Construction Score (MCS) Reasoning Integrity Index (RII) The framework emphasizes conceptual compression, abstraction, and model-building as core indicators of genuine understanding in AI-augmented learning environments. It also incorporates modality-agnostic assessment pathways to support neurodivergent, disabled, multilingual, and nonverbal learners. This work is presented as a theoretical and conceptual framework intended for future empirical validation across educational settings. The paper is designed for researchers, educators, curriculum designers, educational policymakers, and institutions seeking to redesign assessment systems for the era of generative AI.