Human-Centric eXplainable AI in Education
Project Overview
The document examines the integration of Human-Centric eXplainable AI (HCXAI) in education, highlighting the transformative potential of large language models (LLMs) in improving trust, transparency, and learning outcomes among students and educators. It emphasizes that explainability is essential for fostering user engagement and tackling ethical issues such as bias and privacy, which are critical in educational settings. The document identifies several challenges associated with implementing HCXAI, such as the complexity of AI models and the diverse needs of users, which can hinder effective application in educational contexts. To address these challenges, it proposes frameworks designed to guide the development of AI systems that are both user-friendly and capable of meeting the varied requirements of educators and learners. Ultimately, the findings suggest that when designed with explainability and user engagement in mind, generative AI can significantly enrich the educational experience, leading to improved outcomes while also addressing ethical considerations.
Key Applications
Large Language Models (LLMs) for personalized learning and assessment
Context: K-12 classrooms, higher education, and corporate training environments
Implementation: LLMs analyze student data to tailor educational content and recommendations, provide feedback, and assist in question generation.
Outcomes: Enhanced learning outcomes, improved engagement, and personalized educational experiences.
Challenges: Complexity of AI models leading to a lack of transparency and interpretability, which can undermine trust among users.
Implementation Barriers
Technical
The inherent complexity of AI models, particularly LLMs, makes them difficult to interpret and understand. This includes challenges in developing more interpretable AI models and the need for visualization tools.
Proposed Solutions: Develop more interpretable AI models, create visualization tools, establish standardized metrics for evaluating interpretability, and provide training for educators to interpret and communicate AI insights.
User Diversity
Diverse user needs regarding AI literacy can lead to challenges in understanding and utilizing AI-generated insights.
Proposed Solutions: Develop user-friendly explanation interfaces and provide training for educators to interpret and communicate AI insights.
Integration
Integrating HCXAI systems into traditional educational practices can be challenging due to established pedagogical structures.
Proposed Solutions: Engage stakeholders in collaborative redesign of pedagogical approaches to incorporate AI tools effectively.
Project Team
Subhankar Maity
Researcher
Aniket Deroy
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Subhankar Maity, Aniket Deroy
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai