The Cognitive and Artificial Intelligence Evaluation (CAIE) framework offers a structured and domain-independent approach to assessing the intelligence of artificial and information systems comprehensively. This research has successfully categorized 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. By employing this methodology, researchers have been able to determine system maturity and developmental potential effectively while gaining systematic insights into strengths and weaknesses across cognitive domains.
The practical validation through use-case analysis has showcased the adaptability of CAIE to various technological contexts, facilitating a consistent comparison between AI and non-AI systems. By treating cognitive features as measurable and comparable attributes, the framework introduces a cohesive mechanism for benchmarking, scalability, and strategic development. The key contribution of this work lies in advancing academic research and its real-world implementation by delivering a tool that combines cognitive insights with practical relevance, bridging theoretical evaluation concepts with practical methods for designing and improving intelligent systems.
This study does not involve the generation or analysis of datasets. Several relevant citations and references provide further insights and context to the topic. The authors have declared no conflicts of interest, and the content is licensed under a Creative Commons Attribution 4.0 International License, allowing for sharing, adaptation, distribution, and reproduction with appropriate credit to the original author(s) and source. Additional permissions may be necessary for material not covered by the license. The study was conducted by Attila Márton Putnoki and Tamás Orosz, with contributions from both authors. It was published on January 28, 2026, in the Artificial Intelligence Review journal.
For further details and insights into the assessment of cognitive and artificial intelligence evaluation frameworks, readers are encouraged to access the full article on the journal’s website.
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