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SPL: A Socratic Playground for Learning Powered by Large Language Model

Liang Zhang, Jionghao Lin, Ziyi Kuang, Sheng Xu, Xiangen Hu

TL;DR

The SPL study addresses the challenge of reproducing expert tutoring in dialogue-based ITS by employing GPT-4 and structured prompt engineering to enact the Socratic method in adaptive learning scenarios. It introduces a two-stage architecture: automatic scenario construction and an iterative, Socratic dialogue loop that personalizes tutoring across domains. Pilot results from essay-writing tasks indicate enhanced engagement, understanding, and motivation, with clear indications of potential advantages over traditional ITS. The work highlights scalable, flexible educational interactions powered by large language models while acknowledging limitations such as latency, hallucinations, and domain-specific gaps, and outlines future directions toward pre-training, multimodal content, and broader evaluation.

Abstract

Dialogue-based Intelligent Tutoring Systems (ITSs) have significantly advanced adaptive and personalized learning by automating sophisticated human tutoring strategies within interactive dialogues. However, replicating the nuanced patterns of expert human communication remains a challenge in Natural Language Processing (NLP). Recent advancements in NLP, particularly Large Language Models (LLMs) such as OpenAI's GPT-4, offer promising solutions by providing human-like and context-aware responses based on extensive pre-trained knowledge. Motivated by the effectiveness of LLMs in various educational tasks (e.g., content creation and summarization, problem-solving, and automated feedback provision), our study introduces the Socratic Playground for Learning (SPL), a dialogue-based ITS powered by the GPT-4 model, which employs the Socratic teaching method to foster critical thinking among learners. Through extensive prompt engineering, SPL can generate specific learning scenarios and facilitates efficient multi-turn tutoring dialogues. The SPL system aims to enhance personalized and adaptive learning experiences tailored to individual needs, specifically focusing on improving critical thinking skills. Our pilot experimental results from essay writing tasks demonstrate SPL has the potential to improve tutoring interactions and further enhance dialogue-based ITS functionalities. Our study, exemplified by SPL, demonstrates how LLMs enhance dialogue-based ITSs and expand the accessibility and efficacy of educational technologies.

SPL: A Socratic Playground for Learning Powered by Large Language Model

TL;DR

The SPL study addresses the challenge of reproducing expert tutoring in dialogue-based ITS by employing GPT-4 and structured prompt engineering to enact the Socratic method in adaptive learning scenarios. It introduces a two-stage architecture: automatic scenario construction and an iterative, Socratic dialogue loop that personalizes tutoring across domains. Pilot results from essay-writing tasks indicate enhanced engagement, understanding, and motivation, with clear indications of potential advantages over traditional ITS. The work highlights scalable, flexible educational interactions powered by large language models while acknowledging limitations such as latency, hallucinations, and domain-specific gaps, and outlines future directions toward pre-training, multimodal content, and broader evaluation.

Abstract

Dialogue-based Intelligent Tutoring Systems (ITSs) have significantly advanced adaptive and personalized learning by automating sophisticated human tutoring strategies within interactive dialogues. However, replicating the nuanced patterns of expert human communication remains a challenge in Natural Language Processing (NLP). Recent advancements in NLP, particularly Large Language Models (LLMs) such as OpenAI's GPT-4, offer promising solutions by providing human-like and context-aware responses based on extensive pre-trained knowledge. Motivated by the effectiveness of LLMs in various educational tasks (e.g., content creation and summarization, problem-solving, and automated feedback provision), our study introduces the Socratic Playground for Learning (SPL), a dialogue-based ITS powered by the GPT-4 model, which employs the Socratic teaching method to foster critical thinking among learners. Through extensive prompt engineering, SPL can generate specific learning scenarios and facilitates efficient multi-turn tutoring dialogues. The SPL system aims to enhance personalized and adaptive learning experiences tailored to individual needs, specifically focusing on improving critical thinking skills. Our pilot experimental results from essay writing tasks demonstrate SPL has the potential to improve tutoring interactions and further enhance dialogue-based ITS functionalities. Our study, exemplified by SPL, demonstrates how LLMs enhance dialogue-based ITSs and expand the accessibility and efficacy of educational technologies.
Paper Structure (18 sections, 5 figures, 3 tables)

This paper contains 18 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dialogue-based Intelligent Tutoring Systems
  4. LLMs for Enhancing ITSs
  5. Socratic Playground for Learning (SPL)
  6. System Architecture
  7. System Prompt Engineering
  8. System Highlights
  9. System Evaluation Through Pilot Testing
  10. Experimental Design
  11. Results
  12. Discussion
  13. Limitations
  14. Future Works
  15. Conclusion
  16. ...and 3 more sections

Figures (5)

  • Figure 1: An Example Interactive Dialogue Interface of SPL System.
  • Figure 2: The Socratic Playground for Learning System Architecture.
  • Figure 3: Average Scores for Q1 to Q10.
  • Figure 4: The Semantics Visualization for Q11 about Favourite Features of SPL.
  • Figure 5: Percentage Distribution of Scores for Q1 to Q10.