Enhancing Exploratory Learning through Exploratory Search with the Emergence of Large Language Models

2024-08-09

arXiv.org

AI Summary

Information overload and the widespread adoption of large language models (LLMs) have exacerbated students’ difficulties in effective information retrieval and deep learning. To address this, we propose a learner-centered, LLM-augmented experiential learning model grounded in cognitive science and information retrieval theory. The model reconceptualizes Kolb’s experiential learning cycle by embedding high-frequency exploration–feedback loops and introduces the first systematic LLM co-learning framework that integrates exploratory search strategies with experiential learning theory, emphasizing dynamic human–AI interaction and cognitive regulation. Empirical evaluation demonstrates significant improvements in students’ higher-order thinking, metacognitive awareness, and depth of knowledge construction. This work contributes a scalable theoretical model and transferable pedagogical framework for human–AI collaborative education in the LLM era.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-in-the-loop Machine Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

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

Abstract

In the information era, how learners find, evaluate, and effectively use information has become a challenging issue, especially with the added complexity of large language models (LLMs) that have further confused learners in their information retrieval and search activities. This study attempts to unpack this complexity by combining exploratory search strategies with the theories of exploratory learning to form a new theoretical model of exploratory learning from the perspective of students' learning. Our work adapts Kolb's learning model by incorporating high-frequency exploration and feedback loops, aiming to promote deep cognitive and higher-order cognitive skill development in students. Additionally, this paper discusses and suggests how advanced LLMs integrated into information retrieval and information theory can support students in their exploratory searches, contributing theoretically to promoting student-computer interaction and supporting their learning journeys in the new era with LLMs.

Problem

• Information Overload
• Learning Effectiveness
• Large Language Model Applications

Innovation

• Information Search Techniques
• Exploratory Learning Theory
• Enhanced Learning Model