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

TL;DR

This paper proposes a new theoretical model of exploratory learning that integrates large language models to enhance students' information search, evaluation, and deep learning skills in the context of modern information retrieval.

Contribution

It introduces a novel theoretical framework combining exploratory search strategies with Kolb's learning model, incorporating LLMs to support student learning and interaction.

Findings

Theoretical model integrating LLMs into exploratory learning.

Enhanced understanding of how LLMs support deep cognitive skills.

Guidelines for implementing LLMs in educational search activities.

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.