The Role of Artificial Intelligence (AI) in Adaptive eLearning System (AES) Content Formation: Risks and Opportunities involved
Project Overview
The document explores the transformative impact of generative AI in education, particularly through Adaptive eLearning Systems (AES) that enhance personalized learning experiences. It highlights the significance of Intelligent Tutoring Systems (ITS) and Adaptive Hypermedia in tailoring educational content to individual learner needs, thereby improving engagement and outcomes. Key applications of generative AI include the automatic generation of customized learning materials, real-time feedback mechanisms, and adaptive assessments that adjust to students' progress and learning styles. The document also addresses the potential risks associated with AI in education, such as biases in content creation and the importance of ensuring the relevance and quality of learning materials. Additionally, it emphasizes the opportunities that AI presents for educators to foster a more inclusive and effective learning environment, ultimately aiming to enhance student success through tailored educational strategies. Overall, the findings underscore the necessity of integrating generative AI thoughtfully within educational frameworks to maximize its benefits while mitigating associated risks.
Key Applications
Adaptive eLearning Systems (AES)
Context: The educational context involves personalized eLearning for students, utilizing AI to adapt content based on learner needs.
Implementation: Implemented through Intelligent Tutoring Systems (ITS) and Adaptive Hypermedia, which aggregate content based on learners' web logs and preferences.
Outcomes: Benefits include personalized learning experiences, improved engagement, and better alignment of content to learners' needs.
Challenges: Challenges include risks of inappropriate content retrieval and the reliance on web logs which may not provide educationally relevant resources.
Implementation Barriers
Content barrier
AI-based systems may retrieve inappropriate, obscene, or derogatory content due to reliance on learner's web log data and the risk of failing to filter out such materials.
Proposed Solutions: Developing better filtering mechanisms, implementing strict content moderation protocols, and using advanced filtering algorithms to maintain content integrity and ensure that only educational materials are retrieved.
Project Team
Suleiman Adamu
Researcher
Jamilu Awwalu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Suleiman Adamu, Jamilu Awwalu
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