Toward enriched Cognitive Learning with XAI
Project Overview
The document discusses the Cognitive Learning with Explainable AI (CL-XAI) system, which leverages explainable AI (XAI) tools to improve cognitive learning in educational settings. It highlights the significance of providing learners with clear explanations to address knowledge gaps and enhance problem-solving abilities. The CL-XAI tool promotes co-learning by enabling human learners to engage with AI systems, receive counterfactual explanations, and deepen their comprehension of intricate concepts. By integrating XAI into cognitive learning, the system aims to foster a more enriching and effective educational experience, ultimately leading to improved learning outcomes. This innovative approach showcases the potential of generative AI to transform education by supporting personalized learning and making complex information more accessible, thereby equipping students with the skills necessary for success in a rapidly evolving digital landscape.
Key Applications
Cognitive Learning with Explainable AI (CL-XAI)
Context: Educational context for learners solving combinatorial problems through a game-inspired platform.
Implementation: The CL-XAI tool utilizes User Feedback-based Counterfactual Explanations to assist learners in making optimal decisions while solving tasks in a virtual environment.
Outcomes: Improved problem-solving skills, enhanced understanding of complex concepts, and effective mental model construction among learners.
Challenges: Ensuring the explanations are comprehensible and useful for learners, and evaluating the effectiveness of the XAI tool in real educational settings.
Implementation Barriers
Technical Barrier
The complexity of integrating XAI tools into existing educational frameworks.
Proposed Solutions: Developing user-friendly interfaces and ensuring that XAI tools are adaptable to various learning contexts.
User Engagement Barrier
Learners may struggle to engage with AI explanations effectively.
Proposed Solutions: Incorporating gamification and interactive elements to enhance learner motivation and involvement.
Project Team
Muhammad Suffian
Researcher
Ulrike Kuhl
Researcher
Jose M. Alonso-Moral
Researcher
Alessandro Bogliolo
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Muhammad Suffian, Ulrike Kuhl, Jose M. Alonso-Moral, Alessandro Bogliolo
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