Enhancing students' CV writing skills using Llama 3 large language model
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Other Title
Authors
Yang, Yong
Author ORCID Profiles (clickable)
Degree
Master of Applied Technologies (Computing)
Grantor
Unitec, Te Pūkenga – New Zealand Institute of Skills and Technology
Date
2024
Supervisors
Song, Lei
Sharifzadeh, Hamid
Sharifzadeh, Hamid
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
students
CV writing
curriculum vitae (CV)
vocational guidance
artificial intelligence (AI)
large language models
CV writing
curriculum vitae (CV)
vocational guidance
artificial intelligence (AI)
large language models
ANZSRC Field of Research Code (2020)
Citation
Yang, Y. (2024). Enhancing students' CV writing skills using Llama 3 large language model (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Applied Technologies (Computing)). Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology
https://hdl.handle.net/10652/6797
Abstract
RESEARCH QUESTION
1. Can AI tools effectively help students match their qualifications to specific job requirements through interactive CV writing?
2. Can using the AI tool enhance students' understanding of the basic content of CVs and align their expression of skills with job expectations?
ABSTRACT
In recent years, Artificial Intelligence (AI) technology has had a far-reaching impact on many industries. In the education field, AI is reshaping teaching and learning models to make the learning experience more personalized. And it also has a significant role in improving equity and access to educational resources.
Currently, AI has many applications in education, but there is still a gap in providing support for students preparing for the work. This study focuses on the role of AI in facilitating education, especially in enhancing students' Curriculum Vitae (CV) writing skills. To fill this gap, we have developed an AI-driven CV writing tool that combines Support Vector Machine (SVM) Modeling and Llama 3 Large Language Model (LLM) to provide continuous feedback and improvement to students.
Our approach consists of training an Support Vector Machine model using a dataset, rating CV content by quality, and then using LLM to optimize student responses based on predefined criteria. Iterative rating and cyclic optimization enabled students to refine their CVs until satisfactory results were achieved. In addition, student feedback was collected through a questionnaire that provided insights into the tool's usability, its impact on learning, and the quality of the AI-generated content.
A total of 77 students' feedback on the CV writing program was collected, and they commented positively on the ease of use of the CV and the improvement in CV writing skills. Suggestions for improvement included refining the interface, adding automation features, expanding templates, improving content accuracy, and pointing out strengths and improvement areas.
The findings suggest that an AI-driven tool like the one developed in this study can assist students in learning and applying CV writing skills by providing a structured and interactive learning experience that reduces reliance on templates. This study highlights the potential of AI in personalizing education, making it a reliable tool for job readiness, and the need for further research to optimize the use of AI in education.
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