# Toward enriched Cognitive Learning with XAI

arXiv: https://arxiv.org/abs/2312.12290 · HTML: https://mlsanity.com/paper/2312.12290/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2108.01737/) (2021)
- [Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning](https://mlsanity.com/paper/2205.03398/) (2022)
- [HAMLET: Interpretable Human And Machine co-LEarning Technique](https://mlsanity.com/paper/1803.09702/) (2018)
- [Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)](https://mlsanity.com/paper/2211.00103/) (2022)

## Built on by (most cited first, 1 of 1)

- [Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain](https://mlsanity.com/paper/2402.06673/) (2024)

## Abstract (arXiv)

As computational systems supported by artificial intelligence (AI) techniques continue to play an increasingly pivotal role in making high-stakes recommendations and decisions across various domains, the demand for explainable AI (XAI) has grown significantly, extending its impact into cognitive learning research. Providing explanations for novel concepts is recognised as a fundamental aid in the learning process, particularly when addressing challenges stemming from knowledge deficiencies and skill application. Addressing these difficulties involves timely explanations and guidance throughout the learning process, prompting the interest of AI experts in developing explainer models. In this paper, we introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by XAI, focusing on two key research objectives: exploring how human learners comprehend the internal mechanisms of AI models using XAI tools and evaluating the effectiveness of such tools through human feedback. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle combinatorial problems to enhance problem-solving skills and deepen their understanding of complex concepts, highlighting the potential for transformative advances in cognitive learning and co-learning.