AI and personalized learning: bridging the gap with modern educational goals

2024-04-03

arXiv.org

AI Summary

This study identifies a structural misalignment between current AI-driven personalized learning (PL) systems and the OECD’s *Learning Compass 2030* framework: contemporary PL overemphasizes disciplinary knowledge transmission while substantially neglecting learner agency, deep cognitive engagement, and cross-disciplinary competency development. To address this gap, the research conducts the first systematic comparative analysis of AI-PL practices against global educational goals, integrating critical evaluation, generative AI (e.g., ChatGPT) co-design, and human–AI collaborative pedagogical modeling. It proposes a novel “teacher–generative AI” dual-agency hybrid PL paradigm, positioning AI as a supportive collaborator—not a replacement for instruction—to reconfigure learning pathways around student agency, reflective practice, and higher-order competencies. The resulting framework offers a theoretically grounded and practically viable pathway for transforming AI’s role in education. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the goals outlined in the OECD Learning Compass 2030. Our analysis indicates a gap between the objectives of modern education and the technological approach to PL. We identify areas where the AI-based PL solutions could embrace essential elements of contemporary education, such as fostering learner's agency, cognitive engagement, and general competencies. While the PL solutions that narrowly focus on domain-specific knowledge acquisition are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of generative AI, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.

Problem

• AI-driven PL solutions misalign with modern education goals
• Gap exists between OECD 2030 objectives and PL technology
• PL needs holistic change beyond domain-specific knowledge tools

Innovation

• AI-driven PL aligns with OECD 2030 goals
• Hybrid model combines AI and teacher collaboration
• Generative AI enhances personalized learning solutions