A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers
- URL: http://arxiv.org/abs/2507.05292v1
- Date: Sat, 05 Jul 2025 15:21:30 GMT
- Title: A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers
- Authors: Kaiqi Yang, Hang Li, Yucheng Chu, Ahreum Han, Yasemin Copur-Gencturk, Jiliang Tang, Hui Liu,
- Abstract summary: I-VIP (Intelligent Virtual Interactive Program) is an intelligent tutoring platform for teacher professional development.<n>It is driven by large language models (LLMs) and supported by multi-agent frameworks.
- Score: 31.33027139506322
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Professional development (PD) serves as the cornerstone for teacher tutors to grasp content knowledge. However, providing equitable and timely PD opportunities for teachers poses significant challenges. To address this issue, we introduce I-VIP (Intelligent Virtual Interactive Program), an intelligent tutoring platform for teacher professional development, driven by large language models (LLMs) and supported by multi-agent frameworks. This platform offers a user-friendly conversational interface and allows users to employ a variety of interactive tools to facilitate question answering, knowledge comprehension, and reflective summarization while engaging in dialogue. To underpin the functionality of this platform, including knowledge expectation analysis, response scoring and classification, and feedback generation, the multi-agent frameworks are leveraged to enhance the accuracy of judgments and mitigate the issue of missing key points.
Related papers
- \ extsc{SimInstruct}: A Responsible Tool for Collecting Scaffolding Dialogues Between Experts and LLM-Simulated Novices [21.67295740032255]
SimInstruct is a scalable, expert-in-the-loop tool for collecting scaffolding dialogues.<n>Using teaching development coaching as an example domain, SimInstruct simulates novice instructors via LLMs.<n>Our results reveal that persona traits, such as extroversion and introversion, meaningfully influence how experts engage.
arXiv Detail & Related papers (2025-08-06T13:16:10Z) - Evaluating Machine Expertise: How Graduate Students Develop Frameworks for Assessing GenAI Content [1.967444231154626]
This paper examines how graduate students develop frameworks for evaluating machine-generated expertise in web-based interactions with large language models (LLMs)<n>Our findings reveal that students construct evaluation frameworks shaped by three main factors: professional identity, verification capabilities, and system navigation experience.
arXiv Detail & Related papers (2025-04-24T22:24:14Z) - INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises.<n>We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
arXiv Detail & Related papers (2025-04-24T15:47:20Z) - 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.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>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) - Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors [0.5762045049964718]
Apprentice Tutor Builder (ATB) is a platform that simplifies tutor creation and personalization.
Instructors can utilize ATB's drag-and-drop tool to build tutor interfaces.
We conducted a user study with 14 instructors to evaluate the effectiveness of ATB's design with end users.
arXiv Detail & Related papers (2024-04-11T16:14:23Z) - DIALIGHT: Lightweight Multilingual Development and Evaluation of
Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems.
It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level.
Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
arXiv Detail & Related papers (2024-01-04T11:27:48Z) - 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) - ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions [53.07022684941739]
We present ChoiceMates, an interactive multi-agent system designed to address these needs.<n>Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process.
arXiv Detail & Related papers (2023-10-02T16:49:39Z) - Automated Distractor and Feedback Generation for Math Multiple-choice
Questions via In-context Learning [43.83422798569986]
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and reliable form of assessment.
To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers.
We propose a simple, in-context learning-based solution for automated distractor and corresponding feedback message generation.
arXiv Detail & Related papers (2023-08-07T01:03:04Z) - 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) - 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.