WANG Hongtai* and YEO Wee Kiang
Department of Information Systems and Analytics,
School of Computing (SOC), NUS
Wang, H., & Yeo, W. K. (2024). Leveraging multi-agent generative AI for next-generation education and career development [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-hwang-wkyeo/
SUB-THEME
Opportunities from Generative AI
KEYWORDS
Educational Support, Career Advisory, Generative AI, Multi-Agent System, Large Language Model
CATEGORY
Lightning Talk
INTRODUCTION
In today’s complex education and career planning landscape, students face challenges in navigating vast resources and making informed decisions. Traditional career advisory services often lack the necessary personalisation. This project aims to leverage AI technologies, particularly large language models (LLMs) and multi-agent systems, to develop a novel solution that provides tailored guidance and support.
The system’s functionality is divided into two core functionalities: Tutor and Career Advisory. Together, these features create a cohesive system that guides students through both academic and career development with personalised solutions.
KEY CAPABILITIES
Tutor Section
- Tutorial Teaching: This function introduces subject matter concepts progressively, from simple to complex, with clear examples for better understanding.
- Question-and-Answer: This function allows users to ask questions about the course materials and receive contextually relevant answers.
- Providing Review Questions: This function generates questions based on provided materials, testing students’ mastery of concepts through customised review questions.
Career Advisory Section
- Generate Interview Questions. Generates tailored interview questions based on job descriptions by analysing required skills and knowledge.
- Analyse Shortcomings. Compare the student’s resume and the job description to highlight skill and knowledge gaps.
- Generate Cover Letters. Automatically examine the job description and user’s resume, extract the most relevant resume highlights, and generate a cover letter emphasizing the user’s advantages for the role.
- Assist Users in Gaining Essential Skills. Creates technology tutorials tailored to job descriptions, providing links to online courses and materials to enhance learning.
METHODOLOGY
Architecture
The architecture of this project is designed as a dialogue system based on a generative AI model. The core component is a LLM agent that acts as a traffic controller and interacts with the user. Based on the user’s input prompt, this agent will discern whether the user’s queries are related to Tutor or Career Advisory and thereafter provide appropriate responses accordingly.
If the user’s request is straightforward, the LLM agent handles it directly. For more complex requirements, the agent collects detailed information about the user’s needs and activates specialised agent groups (Cheng et al., 2024) designed to address specific issues. An agent group consists of multiple LLM agents created using Autogen (Wu et al., 2023) that is responsible for one specific task. These specialised agent groups include:
- Education Agent Groups: Responsible for tasks such as coaching users with course materials, answering their questions, and providing review questions.
- Career Agent Groups: Responsible for generating interview questions, analysing the user’s shortcomings, generating cover letters, and providing learning resources.
Figure 1. Diagram of our system with LLM agent and role-based groups.
KEY FINDINGS
Interface Design
In the Tutor section, users engage directly with agent tutors through a system that allows seamless switching between instructional content and question-and-answer sessions.
Figure 2. Our AI platform assists users in learning Operations Management through dialogue.
The Career section offers unrestricted communication with AI-powered LLM agents, providing essential career advisory. This includes generating personalised tables that identify skill gaps relative to job requirements and assisting in crafting tailored cover letters to improve job applications.
Figure 3. User interface for generating a comparison table between job requirements and applicant qualifications.
SIGNIFICANCE OF THE PROJECT
- Enhanced Personalisation: Tailored learning experiences with progressive subject matter introduction, customized Q&A support, and personalised review questions. Career guidance includes tailored interview questions, skill gap analysis, and personalized cover letters.
- Improved Accessibility: 24/7 support with easy access to relevant online courses and materials.
- Efficiency: Streamlined assistance through specialized agent groups, providing quick and precise help. Identifies skill gaps and offers targeted learning recommendations.
- Career Preparation: Generates mock interview questions and skill gap analyses for better preparation. Automatically creates personalised cover letters and offers resume advice.
REFERENCES
Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., & He, X. (2024). Exploring large language model-based intelligent agents: Definitions, methods, and prospects. ArXiv. abs/2401.03428.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A.H., White, R.W., Burger, D., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. ArXiv. abs/2308.08155v2.