Enhancing Exploratory Learning through Exploratory Search with the Emergence of Large Language Models
It addresses the problem of information confusion for learners in educational contexts, but appears incremental as it adapts existing models like Kolb's with LLM integration.
This study tackled the challenge of learners effectively using information in the era of large language models (LLMs) by developing a new theoretical model combining exploratory search strategies with exploratory learning theories, aiming to promote deep cognitive skill development in students.
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.