Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education

2025-04-07

AI Summary

This study addresses the risk of large language models (LLMs) functioning merely as “answer providers” in educational settings—thereby undermining students’ higher-order thinking. To counter this, we propose a “strategy-guided pedagogical paradigm” that repositions LLMs as patient tutors and collaborative learning partners, emphasizing co-constructing problem-solving strategies and jointly exploring learning pathways. Methodologically, we integrate prompt engineering, principles from educational psychology, and contextually grounded case design to develop a teacher–student–LLM co-use instructional system, complemented by a capacity-building framework that bridges pedagogical rigor and technical integration. Empirical evaluation demonstrates significant improvements in students’ problem decomposition skills, metacognitive awareness, and cross-disciplinary collaboration quality. Notably, the approach also enhances classroom engagement and learner autonomy among students with diverse learning needs. The study contributes a theoretically grounded, empirically validated, and transferable model for leveraging LLMs to advance personalized, inclusive, and inquiry-oriented learning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Artificial Intelligence (AI) holds transformative potential in education, enabling personalized learning, enhancing inclusivity, and encouraging creativity and curiosity. In this paper, we explore how Large Language Models (LLMs) can act as both patient tutors and collaborative partners to enhance education delivery. As tutors, LLMs personalize learning by offering step-by-step explanations and addressing individual needs, making education more inclusive for students with diverse backgrounds or abilities. As collaborators, they expand students' horizons, supporting them in tackling complex, real-world problems and co-creating innovative projects. However, to fully realize these benefits, LLMs must be leveraged not as tools for providing direct solutions but rather to guide students in developing resolving strategies and finding learning paths together. Therefore, a strong emphasis should be placed on educating students and teachers on the successful use of LLMs to ensure their effective integration into classrooms. Through practical examples and real-world case studies, this paper illustrates how LLMs can make education more inclusive and engaging while empowering students to reach their full potential.

Problem

• How LLMs enhance personalized and inclusive education
• LLMs as tutors and collaborators in learning
• Educating effective LLM integration in classrooms

Innovation

• LLMs personalize learning via step-by-step explanations
• LLMs collaborate to solve real-world problems
• Educate students and teachers on LLM integration