发表机构
TU Dortmund University; Lamarr Institute for Machine Learning and Artificial Intelligence（多特蒙德工业大学; 拉马尔机器学习与人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对ML教育中黑箱问题导致的AI信任校准不足，提出整合跨模态迁移、计算思维与解释性思维的ICE-T教学框架，将信任校准作为教育目标以实现恰当的AI依赖。
AI 中文摘要
机器学习（ML）教育面临两个持续且相互关联的障碍：许多教育工具将ML呈现为不透明的黑箱，使学习者仅获得表面理解，而这种不透明性也阻碍用户形成对AI系统进行恰当依赖所需的校准信任。我们提出ICE-T，这是一种教学框架，整合了三个相互强化的方面：基于布鲁纳的动作性、形象性和象征性表征模式的跨模态迁移，通过使用-修改-创造（Use-Modify-Create）流程实现的计算思维，以及由过程模型支持的解释性思维。将该框架与算法厌恶、AI素养和心智模型形成的实证文献，以及K-12 ML活动格局的系统综述相联系，我们认为这三个方面提供了信任校准文献所确定的恰当依赖的驱动因素的认知机制：表征丰富性、分级过程控制和错误情境化能力。在此基础上，我们提出将信任校准作为明确的教育目标，ICE-T则是实现该目标的有原则且可扩展的手段。
英文摘要
Machine learning (ML) education faces two persistent and connected obstacles: many educational tools present ML as an opaque black box, which leaves learners with a superficial understanding, and this same opacity prevents users from forming the calibrated trust that appropriate reliance on AI systems requires. We present ICE-T, a didactic framework that integrates three mutually reinforcing facets: intermodal transfer grounded in Bruner's enactive, iconic, and symbolic modes of representation, computational thinking operationalized through the Use-Modify-Create progression, and explanatory thinking supported by a process model. Connecting the framework to the empirical literature on algorithm aversion, AI literacy, and mental model formation, and to systematic reviews of the K-12 ML activity landscape, we argue that the three facets supply the cognitive mechanisms that the trust calibration literature identifies as drivers of appropriate reliance: representational richness, graduated process control, and the capacity to contextualize errors. On this basis, we propose that trust calibration be treated as an explicit educational objective, with ICE-T as a principled and scalable means of achieving it.
