Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive work to systems that can generate, recommend, and sometimes act on the learner's behalf. This creates a fundamental challenge for learning theory: successful performance can no longer be assumed to indicate learning. Learners may complete tasks effectively with AI support while developing less understanding, weaker judgment, and limited transferable capability. We argue that this problem is not fully captured by existing learning theories. Behaviourism, cognitivism, constructivism, and connectivism remain important, but they do not directly explain when AI-assisted performance becomes durable human capability. We propose Agentivism, a learning theory for human-AI interaction. Agentivism defines learning as durable growth in human capability through selective delegation to AI, epistemic monitoring and verification of AI contributions, reconstructive internalization of AI-assisted outputs, and transfer under reduced support. The importance of Agentivism lies in explaining how learning remains possible when intelligent delegation is easy and human-AI interaction is becoming a persistent and expanding part of human learning.

翻译：历史上，当学习条件发生变化时，学习理论也随之改变。生成式与代理型人工智能创造了一种新条件，允许学习者将解释、写作、问题解决及其他认知工作委托给能够代表学习者生成、推荐甚至执行任务的系统。这给学习理论带来了根本性挑战：成功的表现不再能被视为学习的标志。学习者可能在人工智能的支持下高效完成任务，但与此同时，他们发展出的理解力、判断力和可迁移能力却可能更弱。我们认为，现有学习理论并未完全捕捉到这一问题。行为主义、认知主义、建构主义和联通主义仍然重要，但它们未能直接解释人工智能辅助下的表现何时能够转化为持久的人类能力。我们提出代理主义，一种针对人机交互的学习理论。代理主义将学习定义为人与人工智能之间通过选择性委托、对人工智能贡献的认识论监控与验证、对人工智能辅助输出的重构性内化以及在减少支持条件下的迁移所实现的持久人类能力增长。代理主义的重要性在于，它解释了当智能委托变得容易且人机交互正日益成为人类学习持久且不断扩展的组成部分时，学习如何依然可能发生。