The Cognitive Impact Index (CII) is a multidimensional measurement framework designed to evaluate how interaction with artificial intelligence systems influences human cognitive processes. While most AI evaluation approaches focus on system outputs such as accuracy and efficiency, this framework shifts the unit of analysis to human cognition, examining how reasoning, effort, and critical engagement are shaped through AI use. The model operationalises cognitive impact across five measurable dimensions: cognitive effort, reasoning ownership, critical engagement, generative contribution, and dependency risk. These dimensions are integrated into a composite index that classifies AI interactions along a continuum from cognitive enhancement and support to substitution and long-term erosion. Grounded in established theories including cognitive offloading, automation bias, and cognitive load theory, the framework introduces a process-oriented approach to understanding human–AI interaction. A classroom-based empirical demonstration (N = 150) illustrates how different modes of AI use produce significantly different cognitive outcomes, with AI-assisted interaction supporting cognition while AI-generated substitution correlates with reduced engagement and increased dependency. The Cognitive Impact Index provides researchers, educators, and policymakers with a structured method for measuring and governing the cognitive consequences of AI integration. It establishes a foundation for future empirical validation, longitudinal analysis of cognitive change, and the development of human-centered AI evaluation models.
Glen Prajjwal Rai (2026) studied this question.