This perspective piece explores the transformative potential and associated challenges of large language models (LLMs) in education and how those challenges might be addressed utilizing playful and game-based learning. While providing many opportunities, the stochastic elements incorporated in how present LLMs process text, requires domain expertise for a critical evaluation and responsible use of the generated output. Yet, due to their low opportunity cost, LLMs in education may pose some risk of over-reliance, potentially and unintendedly limiting the development of such expertise. Education is thus faced with the challenge of preserving reliable expertise development while not losing out on emergent opportunities. To address this challenge, we first propose a playful approach focusing on skill practice and human judgment. Drawing from game-based learning research, we then go beyond this playful account by reflecting on the potential of well-designed games to foster a willingness to practice, and thus nurturing domain-specific expertise. We finally give some perspective on how a new pedagogy of learning with AI might utilize LLMs for learning by generating games and gamifying learning materials, leveraging the full potential of human-AI interaction in education.

这篇视角文章探讨了大型语言模型（LLM）在教育领域的变革潜力和相关挑战，以及如何利用有趣的基于游戏的学习来解决这些挑战。虽然提供了许多机会，但目前法学硕士如何处理文本中包含的随机元素需要领域专业知识来对生成的输出进行批判性评估和负责任的使用。然而，由于其机会成本较低，教育领域的法学硕士可能会带来一些过度依赖的风险，从而潜在地、无意地限制了此类专业知识的发展。因此，教育面临着保持可靠的专业知识发展同时又不失去新兴机会的挑战。为了应对这一挑战，我们首先提出了一种注重技能实践和人类判断的有趣方法。借鉴基于游戏的学习研究，我们超越了这种有趣的描述，反思了精心设计的游戏在培养实践意愿方面的潜力，从而培养了特定领域的专业知识。最后，我们对人工智能学习的新教学法如何利用法学硕士通过生成游戏和游戏化学习材料进行学习，充分发挥人与人工智能互动在教育中的潜力提出了一些看法。