EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education

• URL: http://arxiv.org/abs/2308.02773v1
• Date: Sat, 5 Aug 2023 02:55:35 GMT
• Title: EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
• Authors: Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie Zhou, Liang He, Xipeng Qiu
• Abstract summary: EduChat is a large-scale language model (LLM)-based chat system in the education domain. Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms.
• Score: 44.2456523088426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: EduChat (https://www.educhat.top/) is a large-scale language model (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.

Related papers

• From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents [78.15899922698631]
  MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom. We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
  arXiv  Detail & Related papers  (2024-09-05T13:22:51Z)
• ChatEd: A Chatbot Leveraging ChatGPT for an Enhanced Learning Experience in Higher Education [1.835530250800342]
  This work introduces an innovative architecture that combines the strengths of ChatGPT with a traditional information retrieval based framework to offer enhanced student support in higher education.
  arXiv  Detail & Related papers  (2023-12-29T19:11:55Z)
• Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges [60.62904929065257]
  Large language models (LLMs) offer possibility for resolving this issue by comprehending individual requests. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering.
  arXiv  Detail & Related papers  (2023-12-27T14:37:32Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education [0.0]
  This paper explores the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We develop a question bank from the course material and assess the bot's ability to provide accurate, insightful responses. We demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the bot's capabilities.
  arXiv  Detail & Related papers  (2023-09-09T13:37:22Z)
• Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix) [43.55994393060723]
  Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots. We present a case study with a simple system that enables mixed-turn chatbots interactions. We examine ChatGPT's ability to pursue multiple interconnected learning objectives, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles.
  arXiv  Detail & Related papers  (2023-06-18T22:23:18Z)
• VideoChat: Chat-Centric Video Understanding [80.63932941216129]
  We develop an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large language models via a learnable neural interface. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications.
  arXiv  Detail & Related papers  (2023-05-10T17:59:04Z)
• A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions [0.0]
  ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities.
  arXiv  Detail & Related papers  (2023-04-29T11:25:43Z)
• Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture [15.19996462016215]
  This paper proposes an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent.
  arXiv  Detail & Related papers  (2022-12-14T22:57:44Z)
• Few-Shot Bot: Prompt-Based Learning for Dialogue Systems [58.27337673451943]
  Learning to converse using only a few examples is a great challenge in conversational AI. The current best conversational models are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL) We propose prompt-based few-shot learning which does not require gradient-based fine-tuning but instead uses a few examples as the only source of learning.
  arXiv  Detail & Related papers  (2021-10-15T14:36:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.