TUTORING: Instruction-Grounded Conversational Agent for Language
Learners
- URL: http://arxiv.org/abs/2302.12623v1
- Date: Fri, 24 Feb 2023 13:36:11 GMT
- Title: TUTORING: Instruction-Grounded Conversational Agent for Language
Learners
- Abstract summary: Tutoring bot is trained on a large scale of tutor-student conversations for English-language learning.
To mimic a human tutor's behavior, the tutor bot leverages diverse educational instructions and grounds to each instruction.
- Score: 2.2905464092400316
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, we propose Tutoring bot, a generative chatbot trained on a
large scale of tutor-student conversations for English-language learning. To
mimic a human tutor's behavior in language education, the tutor bot leverages
diverse educational instructions and grounds to each instruction as additional
input context for the tutor response generation. As a single instruction
generally involves multiple dialogue turns to give the student sufficient
speaking practice, the tutor bot is required to monitor and capture when the
current instruction should be kept or switched to the next instruction. For
that, the tutor bot is learned to not only generate responses but also infer
its teaching action and progress on the current conversation simultaneously by
a multi-task learning scheme. Our Tutoring bot is deployed under a
non-commercial use license at https://tutoringai.com.
Related papers
- GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns [55.28321614854968]
We introduce GeoDial, a dataset of over 1.3K teacher-student dialogs in the domain of geometry.<n>We propose a scalable annotation protocol that integrates dialog acts, visual highlighting, and feedback.
arXiv Detail & Related papers (2026-05-08T10:54:32Z) - TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors [24.54129847914925]
TeachingCoach is designed to support instructor professional development through real-time, conversational guidance.<n>It is built on a data-centric pipeline that extracts pedagogical rules from educational resources.<n>Expert evaluations show TeachingCoach produces clearer, more reflective, and more responsive guidance than a GPT-4o mini baseline.
arXiv Detail & Related papers (2026-03-18T18:35:53Z) - Letting Tutor Personas "Speak Up" for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization [45.40380629269521]
We show how tutor personas embedded in human tutor-student dialogues can be used to guide LLM behavior without relying on explicitly prompted instructions.<n>We find that a steering vector captures tutor-specific variation across dialogue contexts, improving semantic alignment with ground-truth tutor utterances and increasing preference-based evaluations.
arXiv Detail & Related papers (2026-02-07T17:44:07Z) - Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues [48.99818550820575]
Recent studies have shown that strategies used by tutors can have significant effects on student outcomes.<n>Few works have studied predicting tutor strategy in dialogues.<n>We investigate the ability of modern LLMs, particularly Llama 3 and GPT-4o, to predict both future tutor moves and student outcomes in dialogues.
arXiv Detail & Related papers (2025-07-09T14:47:35Z) - MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [76.1634959528817]
We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.
MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.
We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
arXiv Detail & Related papers (2025-02-26T08:43:47Z) - Multimodal Classification of Teaching Activities from University Lecture
Recordings [0.9790236766474201]
We present a multimodal classification algorithm that identifies the type of activity that is being carried out at any time of the lesson.
Some academic activities are more easily identifiable with the audio signal while resorting to the text transcription is needed to identify others.
arXiv Detail & Related papers (2023-12-24T08:33:30Z) - Large Language Model-Driven Classroom Flipping: Empowering
Student-Centric Peer Questioning with Flipped Interaction [3.1473798197405953]
This paper investigates a pedagogical approach of classroom flipping based on flipped interaction in large language models.
Flipped interaction involves using language models to prioritize generating questions instead of answers to prompts.
We propose a workflow to integrate prompt engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a quiz-prompt-discuss routine.
arXiv Detail & Related papers (2023-11-14T15:48:19Z) - Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking [10.363782876965221]
We envision a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences.
The insights obtained in this paper can guide future implementations of tutoring agents oriented toward teaching computational thinking and computer programming.
arXiv Detail & Related papers (2023-11-12T16:19:03Z) - Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Data [23.168347070904318]
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 using large language models to generate dialogues related to these topics.
arXiv Detail & Related papers (2023-09-28T19:14:18Z) - Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
We rigorously analyze various generative language models on two dialog tutoring datasets for language learning.
We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios.
Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
arXiv Detail & Related papers (2023-01-24T11:00:17Z) - Teacher Perception of Automatically Extracted Grammar Concepts for L2
Language Learning [91.49622922938681]
We present an automatic framework that automatically discovers and visualizing descriptions of different aspects of grammar.
Specifically, we extract descriptions from a natural text corpus that answer questions about morphosyntax and semantics.
We apply this method for teaching the Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed pedagogical resources.
arXiv Detail & Related papers (2022-06-10T14:52:22Z) - Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher.
We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
arXiv Detail & Related papers (2022-03-19T03:22:57Z) - Pedagogical Demonstrations and Pragmatic Learning in Artificial
Tutor-Learner Interactions [8.715518445626826]
In this paper, we investigate the implementation of such mechanisms in a tutor-learner setup where both participants are artificial agents in an environment with multiple goals.
Using pedagogy from the tutor and pragmatism from the learner, we show substantial improvements over standard learning from demonstrations.
arXiv Detail & Related papers (2022-02-28T21:57:50Z) - 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) - Iterative Teacher-Aware Learning [136.05341445369265]
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency.
We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
arXiv Detail & Related papers (2021-10-01T00:27:47Z) - Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms [50.19997675066203]
We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.
By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
arXiv Detail & Related papers (2020-05-16T02:17:04Z)
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.