Enhancing Educational Efficiency: Generative AI Chatbots and DevOps in Education 4.0
Project Overview
The document explores the incorporation of generative AI and DevOps methodologies in computer science education, particularly in the context of developing Content Management Systems (CMS). Over a span of three academic years, an innovative pedagogical framework was established that blended traditional educational practices with AI tools such as ChatGPT to improve learning efficiency and outcomes. This approach utilized structured sprints focused on essential topics including Object-Oriented PHP, theme development, and plugin development, ultimately leading to enhanced educational efficiency and a stronger alignment with the principles of Education 4.0. The findings indicate that the integration of generative AI not only supports the learning process but also fosters a more engaging and effective educational experience, preparing students for the complexities of modern software development.
Key Applications
Use of AI tools for coding assistance and collaborative software development.
Context: Higher education courses on Content Management Systems and software development practices targeting computer science students, focusing on both individual coding tasks and collaborative project work.
Implementation: Implemented through structured sprints where AI tools like ChatGPT were utilized for code generation, debugging, and problem-solving, alongside Git for version control and collaborative project management.
Outcomes: Increased student engagement, higher rates of successful project completion, improved coding skills, enhanced collaborative skills among students, and better alignment with industry practices.
Challenges: Dependency on AI tools for learning, potential biases in AI outputs, ongoing training needs for the AI model, learning curve associated with Git tools, and potential issues with version control conflicts.
Implementation Barriers
Technical Barrier
Challenges associated with integrating AI tools into the curriculum, including the reliability of AI-generated content and the need for ongoing training and evaluation of AI outputs to mitigate biases and inaccuracies.
Proposed Solutions: Ongoing training and evaluation of AI outputs to mitigate biases and inaccuracies.
Cultural Barrier
Resistance from educators and students to adopt new teaching methodologies and technologies, necessitating training and workshops to familiarize faculty and students with AI and DevOps tools.
Proposed Solutions: Providing training and workshops to familiarize faculty and students with AI and DevOps tools.
Resource Barrier
High resource intensity required to implement and maintain the new technologies and methodologies. Leveraging existing resources and gradually scaling implementation based on feedback and outcomes is essential.
Proposed Solutions: Leveraging existing resources and gradually scaling implementation based on feedback and outcomes.
Project Team
Edis Mekić
Researcher
Mihailo Jovanović
Researcher
Kristijan Kuk
Researcher
Bojan Prlinčević
Researcher
Ana Savić
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Edis Mekić, Mihailo Jovanović, Kristijan Kuk, Bojan Prlinčević, Ana Savić
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai