Artificial intelligence (AI) holds significant potential for enhancing student learning. This reflection critically examines the promises and limitations of AI for cognitive learning processes and outcomes, drawing on empirical evidence and theoretical insights from research on AI-enhanced education and digital learning technologies. We critically discuss current publication trends in research on AI-enhanced learning and rather than assuming inherent benefits, we emphasize the role of instructional implementation and the need for systematic investigations that build on insights from existing research on the role of technology in instructional effectiveness. Building on this foundation, we introduce the ISAR model, which differentiates four types of AI effects on learning compared to learning conditions without AI, namely inversion, substitution, augmentation, and redefinition. Specifically, AI can substitute existing instructional approaches while maintaining equivalent instructional functionality, augment instruction by providing additional cognitive learning support, or redefine tasks to foster deep learning processes. However, the implementation of AI must avoid potential inversion effects, such as over-reliance leading to reduced cognitive engagement. Additionally, successful AI integration depends on moderating factors, including students’ AI literacy and educators’ technological and pedagogical skills. Our discussion underscores the need for a systematic and evidence-based approach to AI in education, advocating for rigorous research and informed adoption to maximize its potential while mitigating possible risks.

人工智能 （AI） 在增强学生学习方面具有巨大潜力。这种反思批判性地审视了人工智能对认知学习过程和结果的承诺和局限性，借鉴了人工智能增强教育和数字学习技术研究的经验证据和理论见解。我们批判性地讨论了人工智能增强学习研究的当前出版趋势，而不是假设固有的好处，我们强调教学实施的作用以及系统调查的必要性，这些调查建立在对技术在教学有效性中的作用的现有研究的见解之上。在此基础上，我们介绍了 ISAR 模型，与没有 AI 的学习条件相比，它区分了四种类型的 AI 对学习的影响，即反转、替代、增强和重新定义。具体来说，AI 可以在保持等效教学功能的同时替代现有的教学方法，通过提供额外的认知学习支持来增强教学，或重新定义任务以促进深度学习过程。但是，AI 的实施必须避免潜在的反转效应，例如过度依赖导致认知参与度降低。此外，成功的 AI 集成取决于调节因素，包括学生的 AI 素养以及教育工作者的技术和教学技能。我们的讨论强调了对人工智能在教育领域采取系统和循证方法的必要性，倡导严格的研究和知情采用，以最大限度地发挥其潜力，同时降低可能的风险。