Artificial Intelligence (AI) is becoming an essential part of our daily lives, helping with tasks like voice assistants, recommendation systems, and even education. But how do these AI systems make decisions? Many AI models, including ChatGPT, function like a “black box”—they give answers without explaining how they arrived at them. This is where Explainable AI (XAI) comes in. XAI helps us understand why AI makes the choices it does, making it more trustworthy and useful, especially in education.

What is Explainable AI (XAI)?

Explainable AI (XAI) refers to AI systems that provide clear explanations for their decisions. Instead of just giving an answer, an XAI model helps users understand why that answer was chosen. This makes AI more reliable, fair, and easier to improve.

Key benefits of XAI:

1. Transparency – Users can see how AI reaches conclusions, preventing blind trust in incorrect answers. For example, a study by Doshi-Velez & Kim (2017) found that users were more likely to trust AI when given explanations for its decisions.
2. Trustworthiness – Knowing why AI made a decision makes people more confident in using it. In financial applications, AI-powered credit scoring models with explainable features help users understand loan approvals (Lipton, 2018).
3. Fairness – XAI can help detect and reduce bias, ensuring AI is fair to all users. For instance, IBM’s AI Fairness 360 toolkit provides tools to identify and mitigate bias in machine learning models (Bellamy et al., 2019).
4. Better Learning – In education, students can see how AI arrives at answers, making learning more effective. Research has shown that AI tutors providing step-by-step reasoning help students develop stronger problem-solving skills (Holstein et al., 2019).

Why is ChatGPT Considered a Black Box?

Many AI models, including ChatGPT, are called black box systems because they generate answers without explaining their reasoning. This can be a problem in education, where students need to understand concepts rather than just receive answers.

Challenges with black-box AI like ChatGPT:

• Lack of Transparency – Users don’t know why a particular answer was given. For example, students using AI for homework assistance may receive correct answers but fail to grasp the underlying logic, leading to surface-level understanding (Doshi-Velez & Kim, 2017).
• Inconsistency – The same question may produce different answers each time. In an AI-powered language learning app, this could confuse students if grammar explanations differ between sessions, making learning less structured (Holstein et al., 2019).
• Possible Bias – AI may unknowingly favor certain perspectives or miss important viewpoints. For instance, history learning AI tools might present events from one cultural perspective while overlooking others, leading to a biased educational experience (Binns, 2018).
• Hard to Fix Errors – Without knowing why AI made a mistake, it’s difficult to improve its accuracy. In automated essay grading, students may receive a low score but no clear explanation for what needs improvement, reducing their ability to learn from feedback (Lipton, 2018).

How Can AI Become More Explainable?

Researchers and developers are working on making AI more understandable. Here are some ways they’re improving explainability:

• Simple Explanations – AI can provide a step-by-step breakdown of how it reached an answer. For example, in an AI-assisted mathematics tutoring system, students can see a breakdown of each step taken to solve an equation, helping them understand problem-solving techniques.
• Highlighting Key Factors – AI can show which words or data points were most important in making its decision. In AI-powered essay grading tools, the system can highlight key sentences that influenced the overall grade, allowing students to focus on areas for improvement (Lundberg & Lee, 2017).
• User Feedback – Allowing users to correct mistakes helps AI improve over time. AI language learning apps, for instance, allow students to mark explanations as helpful or confusing, leading to more accurate and personalised learning experiences (Holstein et al., 2019).
• Visual Aids – Graphs or diagrams can help explain AI-generated results. In science education, AI-driven simulations can visually demonstrate complex concepts such as molecular interactions, making abstract topics easier to grasp (Samek, Wiegand, & Müller, 2017).

By implementing these methods, AI in education can become a more effective tool for both teaching and learning. For example, AI-powered history tools could highlight how different sources influenced an event summary, encouraging students to engage in critical thinking. Similarly, an AI-driven coding assistant could visually map out the logic behind programming errors, helping students debug their own code more effectively. As AI continues to evolve, integrating explainability will be crucial for maximising its benefits in education (Rai, 2019).

Why XAI is Important for Education

In education, understanding why an answer is correct is just as important as knowing the answer itself. XAI can help students and teachers by:

• Building Confidence – Students trust AI more when they understand how it works. For instance, AI-driven mathematics tutoring platforms provide step-by-step explanations of solutions, helping students follow the logic and trust AI-generated assistance.
• Improving Critical Thinking – Seeing explanations helps students analyse and question AI results. AI-driven debate assistants, for example, not only generate arguments but also justify their reasoning, allowing students to critically evaluate different perspectives.
• Making AI a Better Learning Tool – Instead of just answering questions, AI can become a tutor, guiding students through the learning process. AI-powered writing assistants provide grammar explanations and alternative phrasing suggestions, helping students refine their writing skills while understanding language rules (Lundberg & Lee, 2017).

Conclusion

AI is a powerful tool, but it needs to be transparent and explainable, especially in education, where understanding the learning process is critical. Explainable AI (XAI) is essential for ensuring that students develop core learning outcomessuch as problem-solving, critical thinking, and independent inquiry rather than just memorizing AI-generated answers. Without explainability, AI risks encouraging passive consumption of information rather than active engagement with learning.

XAI fosters lifelong learning skills by helping students understand the reasoning behind AI-driven insights, reinforcing the ability to evaluate and apply knowledge rather than just accepting outputs. For example, an AI-powered history assistant that explains its reasoning when presenting historical interpretations encourages students to assess sources and engage in evidence-based discussions (Binns, 2018). Similarly, an AI math tutor that provides step-by-step reasoning, rather than just the final answer, helps students develop problem-solving strategies they can apply in future learning contexts.

By making AI more understandable, we create learning environments that promote curiosity, analysis, and adaptability, skills that are crucial in an era of rapid technological advancements. As AI becomes more integrated into education, ensuring explainability will be a key factor in supporting effective teaching and meaningful learning experiences.

What are your thoughts on making AI more explainable?

References

Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., … & Zhang, Y. (2019). AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. IBM Journal of Research and Development, 63(4/5), 4:1-4:15. 

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159. 

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–16. 

Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. 

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 30, 4765–4774.

Rai, A. (2019). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141. 

Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.