# Explanatory machine learning for sequential human teaching

arXiv: https://arxiv.org/abs/2205.10250 · HTML: https://mlsanity.com/paper/2205.10250/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 106 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 13 of 13)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [TED: Teaching AI to Explain its Decisions](https://mlsanity.com/paper/1811.04896/) (2018)
- [Beneficial and Harmful Explanatory Machine Learning](https://mlsanity.com/paper/2009.06410/) (2020)
- [Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models](https://mlsanity.com/paper/1910.10944/) (2019)
- [Feature Statistics Guided Efficient Filter Pruning](https://mlsanity.com/paper/2005.12193/) (2020)
- [From explanation to synthesis: Compositional program induction for learning from demonstration](https://mlsanity.com/paper/1902.10657/) (2019)
- [Iterative Classroom Teaching](https://mlsanity.com/paper/1811.03537/) (2018)
- [Playgol: learning programs through play](https://mlsanity.com/paper/1904.08993/) (2019)

## Built on by (most cited first, 5 of 5)

- [The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns](https://mlsanity.com/paper/2402.17431/) (2024)
- [A Model for Intelligible Interaction Between Agents That Predict and Explain](https://mlsanity.com/paper/2301.01819/) (2023)
- [Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models](https://mlsanity.com/paper/2405.06724/) (2024)
- [An Algebraic Framework for Hierarchical Probabilistic Abstraction](https://mlsanity.com/paper/2502.21216/) (2025)
- [Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy](https://mlsanity.com/paper/2509.00961/) (2025)

## Abstract (arXiv)

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.