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

arXiv: https://arxiv.org/abs/2408.08894 · HTML: https://mlsanity.com/paper/2408.08894/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models](https://mlsanity.com/paper/2309.15088/) (2023)
- [Do LLM Agents Exhibit Social Behavior?](https://mlsanity.com/paper/2312.15198/) (2023)
- [Large Search Model: Redefining Search Stack in the Era of LLMs](https://mlsanity.com/paper/2310.14587/) (2023)
- ["The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education](https://mlsanity.com/paper/2401.12453/) (2024)

## Built on by (most cited first, 2 of 2)

- [Enhancing Healthcare Recommendation Systems with a Multimodal LLMs-based MOE Architecture](https://mlsanity.com/paper/2412.11557/) (2024)
- [FMT:A Multimodal Pneumonia Detection Model Based on Stacking MOE Framework](https://mlsanity.com/paper/2503.05626/) (2025)

## Abstract (arXiv)

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.