EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education (2308.02773v1)

Abstract: EduChat (https://www.educhat.top/) is a large-scale LLM-based chatbot system in the education domain. Its goal is to support personalized, fair, and compassionate intelligent education, serving teachers, students, and parents. Guided by theories from psychology and education, it further strengthens educational functions such as open question answering, essay assessment, Socratic teaching, and emotional support based on the existing basic LLMs. Particularly, we learn domain-specific knowledge by pre-training on the educational corpus and stimulate various skills with tool use by fine-tuning on designed system prompts and instructions. Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e.g., GitHub https://github.com/icalk-nlp/EduChat, Hugging Face https://huggingface.co/ecnu-icalk ). We also prepare a demonstration of its capabilities online (https://vimeo.com/851004454). This initiative aims to promote research and applications of LLMs for intelligent education.

• The paper introduces EduChat, a large-scale LLM-based chatbot that adapts general language models to meet specialized educational needs.
• The system employs a two-stage process—pre-training on extensive educational materials followed by fine-tuning with targeted datasets—to effectively support open QA, Socratic teaching, and emotional support.
• Empirical evaluations on the C-Eval benchmark show EduChat's competitive performance, outperforming similarly-scaled models in delivering accurate, context-aware responses and personalized feedback.

Overview of EduChat: A Large-Scale LLM-Based Chatbot System for Intelligent Education

The paper "EduChat: A Large-Scale LLM-based Chatbot System for Intelligent Education" introduces a novel application of LLMs designed specifically to cater to the educational sector. The contribution of the EduChat system is notable in its effort to bridge the gap between generalized LLMs and the specialized needs of the educational domain, addressing both pedagogical and psychological aspects of learning.

Objectives and Challenges

EduChat aims to support personalized and equitable education by enhancing existing LLM capabilities with educational functions such as open question answering (QA), essay assessment, Socratic teaching, and emotional support. The paper identifies specific challenges, such as the inadequacy of LLMs in tackling educational tasks due to their general-purpose pre-training and the inherent risk of outdated knowledge. These challenges are addressed through specialized pre-training and retrieval-augmented mechanisms that allow EduChat to remain contextually relevant and accurate.

Methodology

To achieve a robust educational tool, EduChat employs a two-stage training process:

1. Pre-training: Utilizing an extensive corpus of educational materials, the model is pre-trained to develop domain-specific knowledge. This stage incorporates data from educational textbooks, psychology texts, and comprehensive instruction datasets to enforce the foundational abilities required for educational tasks.
2. Fine-tuning: The model is fine-tuned on task-specific datasets covering retrieval-augmented QA, Socratic teaching dialogs, emotional support, and essay assessment. This fine-tuning process seeks to simulate frontline teaching and counseling scenarios, thereby aligning the LLM’s capabilities with practical educational functions.

Key Features

EduChat distinguishes itself with several core functions:

• Retrieval-Augmented Open QA: By integrating real-time internet data retrieval, EduChat enhances the accuracy of its responses and addresses the shortcomings of hallucination commonly seen in LLMs. This ability ensures the model stays up-to-date and provides factual answers.
• Fine-Grained Essay Assessment: The system provides detailed feedback on essays, assessing various attributes such as grammar, content, and style. This detailed feedback aims to help students improve their writing proficiency with targeted insights.
• Socratic Teaching: Instead of supplying direct answers, the model engages learners through Socratic questioning, fostering critical thinking and independent learning in students.
• Psychology-Based Emotional Support: Incorporating elements from Rational Emotive Behavior Therapy (REBT), EduChat offers emotional support tailored to individual students’ needs, simulating the role of a psychological counselor.

Experimental Findings

Empirical evaluations of EduChat on the C-Eval benchmark highlight its effectiveness. EduChat shows competitive performance relative to other models in its parameter class, particularly outperforming similarly-scaled models like Chinese Alpaca-13B. The integration of retrieval mechanisms showcases an improvement in response accuracy and knowledge relevance.

Implications and Future Directions

The EduChat system exemplifies a significant step towards specialized AI applications in education. By adapting LLMs to the intricacies of educational needs, it opens up avenues for more personalized and context-aware educational technologies. Future developments could extend EduChat’s capabilities to include further nuanced educational functions such as career counseling and advanced teaching methods, broadening the scope of intelligent educational support.

In summary, the paper presents a sophisticated approach to tailoring LLM technology to specific educational applications, demonstrating the potential of AI in transforming teaching and learning processes. EduChat serves as a prototype that contributes to ongoing research and application in intelligent education, providing a framework for integrating domain-specific functionalities into LLMs.