Generative AI and Its Impact on Personalized Intelligent Tutoring Systems
Project Overview
Generative AI is revolutionizing educational technology by powering Intelligent Tutoring Systems (ITS) that leverage large language models (LLMs) to create personalized learning experiences through dynamic content generation, real-time feedback, and adaptive learning pathways. Key applications of this technology encompass automated question generation, personalized feedback mechanisms, and interactive dialogue systems that enhance student engagement and learning outcomes. However, the integration of generative AI in education also presents challenges, including the need to ensure pedagogical accuracy, address biases in AI-generated content, and sustain learner engagement. Future directions for this field point towards the incorporation of multimodal AI approaches and the development of emotional intelligence in educational tools, aiming to create more holistic and effective learning environments. Overall, the findings suggest that while generative AI holds significant promise for enhancing educational experiences, careful consideration is needed to navigate the complexities and challenges associated with its implementation.
Key Applications
Adaptive Learning Feedback and Assessment
Context: Intelligent Tutoring Systems (ITS) for various subjects, including mathematics and language learning, aimed at providing personalized assessment and feedback to learners.
Implementation: Generative AI, particularly LLMs like GPT-4, are employed to generate tailored questions, provide context-specific feedback, and simulate human-like conversations based on learner performance and input.
Outcomes: ['Enhances learner engagement and understanding through personalized assessments and feedback.', "Promotes active learning and deeper understanding by adapting to the learner's needs."]
Challenges: ['Limited adaptability of traditional static question banks.', 'Need for real-time analysis and adaptability to evolving learner needs.', 'Requires complexity management to match learner proficiency.']
Implementation Barriers
Pedagogical Accuracy
Ensuring the accuracy of AI-generated educational content.
Proposed Solutions: Implement robust validation mechanisms and expert oversight.
Bias and Equity
Inherent biases in AI systems may perpetuate inequalities.
Proposed Solutions: Use diverse datasets and involve stakeholders from varied backgrounds in development.
Learner Engagement
Maintaining learner engagement with AI-generated content.
Proposed Solutions: Incorporate gamification and adaptive learning pathways to sustain interest.
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