Introducing Conceptual Framework of Gamified Moral Reasoning Evaluation and Testing for Human Agentic-AI Co-Alignment
Keywords:
Moral Reasoning, Gamification, AI Ethics, Human–AI Alignment, Ethical Decision ModelingAbstract
Current methods for assessing human moral reasoning, based on neo-Kohlbergian theory (e.g., DIT-2), offer valuable psychometric insight but remain static, decontextualized, and limited in their ability to capture the evolution of reasoning abilities along a continuum. Agentic AI alignment and safety frameworks are also evolving, but remain primarily compliance-driven and evaluate adherence to ethical principles without modeling the underlying processes of moral judgment and adaptation. This paper adopts a design-oriented integrated methodology, where interdisciplinary literature is synthesized to propose a conceptual Gamified Moral Reasoning Evaluation Framework that addresses these issues by integrating classical and neo-Kohlbergian theories of moral development, game-theoretic reasoning models, and AI-driven adaptive scenario generation. The framework comprises five layers: theoretical foundations, gamification engine, adaptive scenario generation using LLMs, dual human–AI evaluation modules connected through a co-alignment bridge, and continuous learning mechanisms, forming a dynamic ecosystem for assessing and nurturing moral reasoning in both humans and AI systems. The framework enables multi-dimensional assessment through measurable indicators, including decision patterns, response latency, and schema progression across adaptive scenarios, and convergences/divergences in human–AI moral reasoning. These dimensions support longitudinal analysis of moral reasoning evolution, extending beyond static instruments such as DIT-2. A preliminary technology readiness assessment indicates that core enabling components, such as LLMs, game engines, and adaptive learning architectures, currently operate at TRLs 6–8, enabling near-term prototyping and validation. The paper offers a practically grounded pathway toward evidence-based tools for studying and fostering human–AI moral co-alignment in ethically complex and socially consequential domains.
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