"Explainable AI for Education: Recent Trends and Challenges" is a comprehensive exploration of the intersection between artificial intelligence (AI) and education. In this book, we delve into the critical need for transparency and interpretability in AI systems deployed within educational contexts.

Key Themes

Understanding AI in Education:

We provide a concise overview of AI techniques commonly used in educational settings, including recommendation systems, personalized learning, and assessment tools.

Readers will gain insights into the potential benefits and risks associated with AI adoption in education.

The Black-Box Problem:

AI models often operate as "black boxes," making it challenging to understand their decision-making processes.

We discuss the implications of this opacity and emphasize the importance of explainability.

Explainable AI (XAI) Techniques:

From rule-based approaches to neural network interpretability, we explore various methods for making AI models more transparent.

Examples and case studies illustrate how XAI can enhance educational outcomes.

Ethical Considerations:

As AI becomes more integrated into education, ethical dilemmas arise.

We address issues related to bias, fairness, and accountability, emphasizing responsible AI practices.

Future Directions:

Our book looks ahead, considering the evolving landscape of AI and its impact on education.

We propose research directions and practical steps to promote XAI adoption in educational institutions.