Operationalizing Machine Consciousness: Development and Validation of a Multi-Dimensional Assessment Framework for Artificial Intelligence Consciousness Indicators

The rapid advancement of artificial intelligence systems exhibiting increasingly sophisticated cognitive capabilities has generated urgent scientific, philosophical, and ethical questions regarding the assessment of consciousness-relevant properties in artificial systems. Despite substantial theoretical progress in consciousness science—including Global Workspace Theory, Integrated Information Theory, Higher-Order Theories, Attention Schema Theory, and Predictive Processing frameworks—no validated, comprehensive assessment instrument has been developed for systematically evaluating consciousness indicators across diverse AI architectures. This dissertation addresses this critical methodological gap through a sequential mixed-methods research program comprising four integrated phases. Phase 1 employed systematic literature synthesis and a modified Delphi technique with 36 expert panelists representing consciousness science, artificial intelligence, philosophy of mind, and cognitive neuroscience. Through four iterative rounds achieving strong consensus (Kendall's W = .67, p < .001), researchers identified 58 consciousness indicators organized into 15 dimensions across five theoretical domains. Phase 2 developed three complementary assessment protocols—the Architectural Assessment Protocol, Computational Analysis Protocol, and Behavioral Assessment Battery—with pilot testing establishing preliminary reliability (mean ICC = .85). Phase 3 applied the framework to 52 diverse AI systems spanning transformer, recurrent, convolutional, hybrid, and specialized architectures, generating comprehensive consciousness indicator profiles. Phase 4 conducted rigorous psychometric validation, demonstrating excellent internal consistency (α = .94), a well-fitting five-factor structure (CFI = .94, RMSEA = .04), and strong criterion validity through convergence with expert judgments (r = .68–.79). Results revealed that transformer-based systems scored significantly higher on Global Workspace, Attention Schema, and Predictive Processing domains compared to recurrent and convolutional architectures (η² = .31–.48), while model scale showed moderate positive correlations with all domain scores (r = .41–.52). The resulting Multi-Dimensional Assessment Framework for Artificial Intelligence Consciousness Indicators (MDAF-AICI) provides researchers, developers, and policymakers with a theoretically grounded, psychometrically validated instrument for systematic consciousness indicator assessment. This framework advances the scientific study of machine consciousness while offering practical tools for ethical AI governance, contributing to responsible development of increasingly sophisticated artificial intelligence systems. Keywords: artificial intelligence consciousness, consciousness assessment, machine consciousness, consciousness indicators, integrated information theory, global workspace theory, AI ethics