ICE-T框架通过三种教学要素帮助学习者建立对AI系统的适当信任，解决了机器学习教育中的黑盒问题。
研究人员提出ICE-T教学框架，整合了三种互补要素：基于布鲁纳的具身、图像和符号表征的跨模态迁移，通过使用-修改-创建进阶实现的计算思维，以及由过程模型支持的解释性思维。该框架连接了算法厌恶、AI素养和心智模型形成的实证文献，以及K-12机器学习活动的系统综述。ICE-T框架提供了认知机制，包括表征丰富性、渐进式过程控制和错误情境化能力，这些是信任校准文献中确定适当依赖性的驱动因素。
Addressing Trust in AI Systems through Education: A Didactic Perspective
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.