🤖 AI Summary
This paper addresses the extended Turing Test question—“Can machines grow?”—by proposing GROW-AI, the first systematic framework for assessing AI developmental maturity. It establishes four evaluation arenas—psychological, ethical, computational, and robotic—and introduces six standardized, game-based benchmark tasks (C1–C6). Leveraging behavioral logging, expert-weighted scoring, and a quantified Grow Up Index, GROW-AI enables reproducible, cross-modal (LLMs, robots, software agents) assessment of AI evolution from immaturity to maturity. Its core innovation lies in adapting human developmental psychology constructs to AI evaluation, thereby establishing the first traceable, multidimensional, process-oriented growth assessment system. Empirical validation demonstrates its capacity to identify maturity bottlenecks and evolutionary trajectories across diverse AI systems, confirming both universality and effectiveness. (149 words)
📝 Abstract
This study aims to extend the framework for assessing artificial intelligence, called GROW-AI (Growth and Realization of Autonomous Wisdom), designed to answer the question "Can machines grow up?" -- a natural successor to the Turing Test. The methodology applied is based on a system of six primary criteria (C1-C6), each assessed through a specific "game", divided into four arenas that explore both the human dimension and its transposition into AI. All decisions and actions of the entity are recorded in a standardized AI Journal, the primary source for calculating composite scores. The assessment uses the prior expert method to establish initial weights, and the global score -- Grow Up Index -- is calculated as the arithmetic mean of the six scores, with interpretation on maturity thresholds. The results show that the methodology allows for a coherent and comparable assessment of the level of "growth" of AI entities, regardless of their type (robots, software agents, LLMs). The multi-game structure highlights strengths and vulnerable areas, and the use of a unified journal guarantees traceability and replicability in the evaluation. The originality of the work lies in the conceptual transposition of the process of "growing" from the human world to that of artificial intelligence, in an integrated testing format that combines perspectives from psychology, robotics, computer science, and ethics. Through this approach, GROW-AI not only measures performance but also captures the evolutionary path of an AI entity towards maturity.