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This research aims to quantify the efficiency of AI-assisted learning and its effect on cognitive understanding.

April 28, 20260 citationsOpen Access

MindTrace AI: A Quantitative Framework for Evaluating Cognitive Depth and Efficiency in AI-Assisted Learning

Key Points

This research aims to quantify the efficiency of AI-assisted learning and its effect on cognitive understanding.
Introduced a novel mathematical framework, MindTrace AI, for evaluating AI tutoring systems.
Utilized a longitudinal dataset of 2,342 user interactions from the Neurova platform.
Derived metrics: Efficiency of Retention (E_r) and Rate of Cognitive Change (ΔC).
AI-assisted learning environments using 'Adaptive Friction' resulted in a 14.2% higher ΔC compared to direct-answer models.
Data supports the effectiveness of intentional delays and Socratic questioning in enhancing understanding.

Abstract

MindTrace AI: A Quantitative Framework for Evaluating Cognitive Depth and Efficiency in AI-Assisted Learning Abstract: This research introduces MindTrace AI, a novel mathematical and technical framework designed to quantify the "Efficiency–Depth Trade-off" in generative AI tutoring systems. While Large Language Models (LLMs) provide instantaneous solutions, they often bypass the cognitive friction necessary for long-term conceptual retention. Utilizing a longitudinal dataset of 2, 342 user interactions from the Neurova platform, this paper derives two primary metrics for assessing pedagogical quality: Efficiency of Retention (Eᵣ): A measure of the time-decay of conceptual understanding relative to AI-assisted prompt cycles. Rate of Cognitive Change (C): A quantitative assessment of a user's shift from procedural "how-to" knowledge to structural "why" understanding. Key Findings: Preliminary data analysis indicates that AI-assisted learning environments utilizing "Adaptive Friction"—intentional delays or Socratic questioning—result in a 14. 2% higher C compared to direct-answer retrieval models. This work serves as a foundational blueprint for developers and educators to transition from "Chat-based" AI to "Cognitive-first" learning architectures. Keywords: Artificial Intelligence in Education (AIEd), Large Language Models, Cognitive Load Theory, Learning Analytics, Neurova, MindTrace. is th

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