# Addressing Trust in AI Systems through Education: A Didactic Perspective

arXiv: https://arxiv.org/abs/2609.02453 · HTML: https://mlsanity.com/paper/2609.02453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control](https://mlsanity.com/paper/2303.12896/) (2023)
- [What Explains Teachers' Trust of AI in Education across Six Countries?](https://mlsanity.com/paper/2312.01627/) (2023)
- [Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities](https://mlsanity.com/paper/2406.03480/) (2024)
- [ICE-T: A Multi-Faceted Concept for Teaching Machine Learning](https://mlsanity.com/paper/2411.05424/) (2024)

## Abstract (arXiv)

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.