Curriculum-Driven Edubot: A Framework for Developing Language Learning
Chatbots Through Synthesizing Conversational Data
- URL: http://arxiv.org/abs/2309.16804v1
- Date: Thu, 28 Sep 2023 19:14:18 GMT
- Title: Curriculum-Driven Edubot: A Framework for Developing Language Learning
Chatbots Through Synthesizing Conversational Data
- Authors: Yu Li, Shang Qu, Jili Shen, Shangchao Min and Zhou Yu
- Abstract summary: We present Curriculum-Driven EduBot, a framework for developing a chatbots that combines the interactive features of chatbots with the systematic material of English textbooks.
We begin by extracting pertinent topics from textbooks and then using large language models to generate dialogues related to these topics.
Our approach offers learners an interactive tool that aligns with their curriculum and provides user-tailored conversation practice.
- Score: 24.856067418517398
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chatbots have become popular in educational settings, revolutionizing how
students interact with material and how teachers teach. We present
Curriculum-Driven EduBot, a framework for developing a chatbot that combines
the interactive features of chatbots with the systematic material of English
textbooks to assist students in enhancing their conversational skills. We begin
by extracting pertinent topics from textbooks and then using large language
models to generate dialogues related to these topics. We then fine-tune an
open-source LLM using our generated conversational data to create our
curriculum-driven chatbot. User studies demonstrate that our chatbot
outperforms ChatGPT in leading curriculum-based dialogues and adapting its
dialogue to match the user's English proficiency level. By combining
traditional textbook methodologies with conversational AI, our approach offers
learners an interactive tool that aligns with their curriculum and provides
user-tailored conversation practice. This facilitates meaningful student-bot
dialogues and enriches the overall learning experience within the curriculum's
pedagogical framework.
Related papers
- LLM Roleplay: Simulating Human-Chatbot Interaction [52.03241266241294]
LLM-Roleplay is a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction.
We collect natural human-chatbot dialogues from different sociodemographic groups and conduct a human evaluation to compare real human-chatbot dialogues with our generated dialogues.
arXiv Detail & Related papers (2024-07-04T14:49:46Z) - Book2Dial: Generating Teacher-Student Interactions from Textbooks for
Cost-Effective Development of Educational Chatbots [37.304476231479725]
We propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks.
We highlight various quality criteria that such dialogues should fulfill and compare several approaches relying on either prompting or fine-tuning large language models.
Our findings offer insights for future efforts in synthesizing conversational data that strikes a balance between size and quality.
arXiv Detail & Related papers (2024-03-05T20:12:05Z) - 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) - ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented
Instruction Tuning for Digital Human [76.62897301298699]
ChatPLUG is a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
We show that modelname outperforms state-of-the-art Chinese dialogue systems on both automatic and human evaluation.
We deploy modelname to real-world applications such as Smart Speaker and Instant Message applications with fast inference.
arXiv Detail & Related papers (2023-04-16T18:16:35Z) - User Adaptive Language Learning Chatbots with a Curriculum [55.63893493019025]
We adapt lexically constrained decoding to a dialog system, which urges the dialog system to include curriculum-aligned words and phrases in its generated utterances.
The evaluation result demonstrates that the dialog system with curriculum infusion improves students' understanding of target words and increases their interest in practicing English.
arXiv Detail & Related papers (2023-04-11T20:41:41Z) - 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) - Using Chatbots to Teach Languages [43.866863322607216]
Our system can adapt to users' language proficiency on the fly.
We provide automatic grammar error feedback to help users learn from their mistakes.
Our next step is to make our system more adaptive to user profile information by using reinforcement learning algorithms.
arXiv Detail & Related papers (2022-07-31T07:01:35Z) - KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains.
We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
arXiv Detail & Related papers (2022-05-11T16:01:03Z) - 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.