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ICE-T: a didactic framework integrating intermodal transfer, computational thinking, and explanatory thinking for ML education

Design · All designs
Evidence · 2 claims (2 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

ICE-T is a multi-faceted didactic framework for teaching machine learning whose name mnemonically captures its components: Intermodal Transfer grounded in Bruner's enactive-iconic-symbolic principle, Computational Thinking operationalized through the Use-Modify-Create progression, and Explanatory Thinking supported by the PETSP-ML process model. The article states the framework "unifies the three didactic facets described above into a coherent, actionable framework for ML education". The three facets are treated as orthogonal dimensions of a single learning unit: EIS modes structure how a concept is represented, UMC structures the degree of student autonomy, and PETSP-ML structures the temporal sequence, with which combinations are realized left as a deliberate educator design decision.

Design Implications

Context

Requirements

  • Educators deliberately decide which combinations of representational mode, UMC stage, and process phase are realized at each point of a learning unit

Constraints

  • The connection between ICE-T and trust calibration is, at this stage, theoretical and has not itself been evaluated; its empirical grounding is limited to the K-12 landscape

Target Learners

  • K-12 students with little or no prior exposure to programming

Learning Goals

  • conceptual understanding of machine learning
  • computational thinking
  • explanatory thinking about ML systems
  • calibrated trust in AI systems

Claims

Examples

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Key Sources

  • Pierre Haritz, Hendrik Krone, and Thomas Liebig. (2026). Addressing Trust in AI Systems through Education: A Didactic Perspective. https://arxiv.org/abs/2609.02453
Page metadata
Field Value
type design
id ice-t-didactic-framework-ml
title ICE-T: a didactic framework integrating intermodal transfer, computational thinking, and explanatory thinking for ML education
description ICE-T is a multi-faceted didactic framework for teaching machine learning whose name mnemonically captures its components: Intermodal Transfer grounded in Bruner's enactive-iconic-symbolic principle, Computational Thi...
status review
generated by process:settle-candidates on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — pierre-haritz-2026

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