提出新AI测评框架，评估机器能否像人一样成长。
The next question after Turing's question: Introducing the Grow-AI test
- 用六项标准+四类游戏，量化AI的自主智慧成长水平。
- 通过统一记录日志，实现评估可追溯、可复现。
- 适合关注AI进化路径与伦理发展的研究者参考。
本研究旨在扩展人工智能评估框架——GROW-AI（自主智慧的成长与实现），以回应“机器能否成长？”这一对图灵测试的自然延续问题。方法基于六项核心标准（C1-C6），每项通过特定‘游戏’评估，分为四个领域，涵盖人类维度及其在人工智能中的映射。所有实体决策与行为均记录于标准化的AI日志，作为计算综合得分的主要依据。采用专家先验法确定初始权重，全局评分——成长指数——为六项得分的算术平均值，并根据成熟度阈值进行解读。结果表明，该方法能对各类AI实体（机器人、软件代理、大语言模型）的‘成长’水平进行一致且可比较的评估。多游戏结构凸显其优势与薄弱环节，统一日志确保评估过程可追溯、可复现。该工作的原创性在于将人类‘成长’过程的概念系统性移植至人工智能领域，构建了融合心理学、机器人学、计算机科学与伦理学视角的集成化测评体系。通过此方法，GROW-AI不仅衡量性能，更捕捉AI向成熟演化的路径。
原文摘要 · Abstract (English)
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.
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