# Teaching Categories to Human Learners with Visual Explanations

arXiv: https://arxiv.org/abs/1802.06924 · HTML: https://mlsanity.com/paper/1802.06924/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 30 of 30)

- [What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods](https://mlsanity.com/paper/2112.04417/) (2021)
- [The effectiveness of feature attribution methods and its correlation with automatic evaluation scores](https://mlsanity.com/paper/2105.14944/) (2021)
- [Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations](https://mlsanity.com/paper/2307.08678/) (2023)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2202.08816/) (2022)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Visual correspondence-based explanations improve AI robustness and human-AI team accuracy](https://mlsanity.com/paper/2208.00780/) (2022)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints](https://mlsanity.com/paper/1906.00429/) (2019)
- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022)
- [Towards Explainable Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2306.13041/) (2023)
- [Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition](https://mlsanity.com/paper/2107.06538/) (2021)
- [Teaching an Active Learner with Contrastive Examples](https://mlsanity.com/paper/2110.14888/) (2021)
- [Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching](https://mlsanity.com/paper/2102.03919/) (2021)
- [Iterative Teaching by Label Synthesis](https://mlsanity.com/paper/2110.14432/) (2021)
- [Iterative Teaching by Data Hallucination](https://mlsanity.com/paper/2210.17467/) (2022)
- [Assistive Teaching of Motor Control Tasks to Humans](https://mlsanity.com/paper/2211.14003/) (2022)
- [Making a Bird AI Expert Work for You and Me](https://mlsanity.com/paper/2112.02747/) (2021)
- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)
- [ML-Based Teaching Systems: A Conceptual Framework](https://mlsanity.com/paper/2305.07681/) (2023)
- [Innovations in trigger and data acquisition systems for next-generation physics facilities](https://mlsanity.com/paper/2203.07620/) (2022)
- [L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement](https://mlsanity.com/paper/2412.09765/) (2024)
- [Formal Models of Active Learning from Contrastive Examples](https://mlsanity.com/paper/2506.15893/) (2025)
- [Argumentative Explanations for Pattern-Based Text Classifiers](https://mlsanity.com/paper/2205.10932/) (2022)
- [Visual Knowledge Tracing](https://mlsanity.com/paper/2207.10157/) (2022)
- [Improved Robust Algorithms for Learning with Discriminative Feature Feedback](https://mlsanity.com/paper/2209.03753/) (2022)
- [Graphical Perception of Saliency-based Model Explanations](https://mlsanity.com/paper/2406.07702/) (2024)
- [AutoExpert: Automating 3D LiDAR Annotation from Expert-Crafted Guidelines](https://mlsanity.com/paper/2506.02914/) (2025)
- [Discriminative Feature Feedback with General Teacher Classes](https://mlsanity.com/paper/2510.07245/) (2025)

## Abstract (arXiv)

We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods.