AI and personalized learning: bridging the gap with modern educational goals
Project Overview
The document explores the transformative potential of generative AI in education, particularly in enhancing personalized learning experiences. It critiques traditional AI-driven systems that prioritize domain-specific knowledge and performance metrics, often overlooking critical aspects such as learner agency, self-regulation, and cognitive engagement. By addressing these gaps, the authors advocate for a hybrid model that integrates generative AI with teacher-facilitated learning, aiming to create a more comprehensive educational approach. This proposed model aligns with the OECD Learning Compass 2030 framework, emphasizing the importance of fostering holistic educational experiences that support diverse learner needs. The findings suggest that such an integration could lead to improved engagement and outcomes, positioning generative AI as a valuable tool in the evolution of educational practices.
Key Applications
Generative AI for personalized and collaborative learning
Context: K-12 and higher education settings, including formal education environments, with a focus on enhancing personalized learning experiences and meeting diverse learner needs.
Implementation: Integration of generative AI tools, such as ChatGPT, into educational frameworks to support self-regulated learning, cognitive engagement, and reciprocal interaction between teachers and AI, fostering a collaborative learning environment.
Outcomes: Improvement in learner agency, cognitive engagement, development of self-regulation skills, and support for collaborative learning.
Challenges: Risks of metacognitive laziness, dependency on AI for learning tasks, potential negative impact on critical thinking, and the need for careful design to ensure AI enhances rather than detracts from learning autonomy and engagement.
Implementation Barriers
Implementation barrier
Current AI-driven personalized learning systems focus too narrowly on performance metrics and domain-specific knowledge, hindering broader educational goals.
Proposed Solutions: Shift towards a more holistic educational framework that integrates general competencies and learner agency alongside AI technologies.
Cognitive barrier
Generative AI can promote dependency, leading to decreased critical thinking and metacognitive engagement among learners.
Proposed Solutions: Implement design strategies that encourage active learning and self-regulation, ensuring AI tools support rather than replace cognitive processes.
Technological barrier
Generative AI systems often lack transparency and robustness, complicating their integration into educational settings.
Proposed Solutions: Develop clearer guidelines for the use of generative AI in education, focusing on pedagogical principles that enhance learning.
Project Team
Kristjan-Julius Laak
Researcher
Jaan Aru
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kristjan-Julius Laak, Jaan Aru
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18