# Transferring Domain Knowledge with (X)AI-Based Learning Systems

arXiv: https://arxiv.org/abs/2406.01329 · HTML: https://mlsanity.com/paper/2406.01329/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 76 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [Near-Optimally Teaching the Crowd to Classify](https://mlsanity.com/paper/1402.2092/) (2014)
- [Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals](https://mlsanity.com/paper/2203.12892/) (2022)
- [A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making](https://mlsanity.com/paper/2205.05126/) (2022)
- [Machine Explanations and Human Understanding](https://mlsanity.com/paper/2202.04092/) (2022)
- [On the Influence of Explainable AI on Automation Bias](https://mlsanity.com/paper/2204.08859/) (2022)
- [Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study](https://mlsanity.com/paper/2304.08861/) (2023)
- [Mapping the Landscape of Artificial Intelligence Applications against COVID-19](https://mlsanity.com/paper/2003.11336/) (2020)
- [Becoming the Expert - Interactive Multi-Class Machine Teaching](https://mlsanity.com/paper/1504.07575/) (2015)
- [A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM](https://mlsanity.com/paper/2005.06616/) (2020)
- [Human vs. supervised machine learning: Who learns patterns faster?](https://mlsanity.com/paper/2012.03661/) (2020)
- [Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice](https://mlsanity.com/paper/2310.02108/) (2023)
- [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022)
- [On the Influence of Cognitive Styles on Users' Understanding of Explanations](https://mlsanity.com/paper/2210.02123/) (2022)

## Built on by (most cited first, 3 of 3)

- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [Towards Human-Understandable Multi-Dimensional Concept Discovery](https://mlsanity.com/paper/2503.18629/) (2025)
- [Development of Mental Models in Human-AI Collaboration: A Conceptual Framework](https://mlsanity.com/paper/2510.08104/) (2025)

## Abstract (arXiv)

In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However, training novices by experts is costly and time-consuming, increasing the need for alternatives. Explainable artificial intelligence (XAI) has conventionally been used to make black-box artificial intelligence systems interpretable. In this work, we utilize XAI as an alternative: An (X)AI system is trained on experts' past decisions and is then employed to teach novices by providing examples coupled with explanations. In a study with 249 participants, we measure the effectiveness of such an approach for a classification task. We show that (X)AI-based learning systems are able to induce learning in novices and that their cognitive styles moderate learning. Thus, we take the first steps to reveal the impact of XAI on human learning and point AI developers to future options to tailor the design of (X)AI-based learning systems.