Cognitive and artificial intelligence evaluation framework

Abstract The Cognitive and Artificial Intelligence Evaluation (CAIE) framework provides a structured and domain-independent methodology for assessing the intelligence of artificial and information systems in a broader perspective. The primary achievement of this research is the categorization of over ninety cognitive features into six evaluation zones, supported by a two-stage scoring model that combines detailed feature-level analysis with higher-level structural interpretation. This approach has proven effective in identifying system maturity and developmental potential, offering systematic insights into both strengths and weaknesses across cognitive domains. The practical validation through use-case analysis demonstrates that CAIE is adaptable to diverse technological contexts, enabling consistent comparison between AI and non-AI systems. By treating cognitive features as measurable and comparable attributes, the framework introduces a coherent mechanism for benchmarking, scalability, and strategic development. The main contribution of this work lies in advancing both academic research and real-world implementation by delivering a cognitively informed, practically relevant tool that bridges theoretical evaluation concepts with actionable methods for designing and improving intelligent systems.