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：学习理论历来随着学习条件的演变而改变。生成式与主动式人工智能创造了一种全新条件，使学习者能够将解释、写作、问题解决及其他认知工作委托给可生成、推荐并有时代表学习者采取行动的系统。这给学习理论带来了根本性挑战：成功的表现不能再被假定为学习的标志。学习者在人工智能支持下可能高效完成任务，但同时发展出较少的理解、较弱的判断力以及有限的迁移能力。我们认为，现有学习理论并未完全涵盖这一问题。行为主义、认知主义、建构主义与联通主义仍具重要性，但它们无法直接解释人工智能辅助下的表现何时能转化为人类持久的能力。我们提出Agentivism，这是一种面向人机交互的学习理论。Agentivism将学习界定为通过选择性向人工智能委托任务、对人工智能贡献进行认知监测与验证、对人工智能辅助输出进行重构性内化以及在支持减少情境下实现迁移，从而达成的能力持续增长。Agentivism的重要性在于阐释了当智能委托变得轻而易举且人机交互日益成为人类学习持久且不断扩展的组成部分时，学习如何仍能发生。