AI Driven Software Engineering with Structured Prompting

Unit code: NIT2006 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
N/A
Overview
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Overview

In this unit, you will explore core software engineering principles and learn to apply Agile, Scrum, and DevOps methodologies throughout the software development lifecycle. You will use UML-based modelling and AI-driven tools like GitHub Copilot to support coding, documentation, and design tasks. You are expected to integrate prompt engineering techniques to optimise the use of generative AI for software development and project management. You will learn how to structure effective prompts to guide AI tools in generating accurate and relevant outputs. By the end of the unit, you can confidently contribute to AI-enabled software design or pursue further study in advanced software architecture.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Apply software engineering principles and UML modelling techniques to design scalable and maintainable software architectures.
  2. Analyse the integration of AI-assisted software development within Agile, Scrum, and DevOps frameworks to enhance efficiency.
  3. Design UML diagrams to model system behaviour, architecture, and workflows.
  4. Implement software applications using version control, AI-assisted coding tools, and automated testing frameworks with structured prompt engineering.
  5. Collaborate in the evaluation of AI-driven tools and structured prompting techniques to improve software development efficiency and quality.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Practical knowledge test
Assessment type: Assignment
|
Grade: 40%
Software design and UML visual modelling project with Q&A verification.
Assessment type: Case Study
|
Grade: 40%
AI-driven SE evaluation case study with verified live interaction.

Assignment: Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.

Case Study: Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.

Required reading

Selected readings are provided on VU Collaborate.

As part of a course

This unit is studied as part of the following course(s):

Bachelor of Data Science
NBDS | Bachelor degree | Duration: 3 years

Refer to the course page for information on how to apply for the course. VU takes care to ensure the accuracy of this unit information, but reserves the right to change or withdraw courses offered at any time. Please check that unit information is current with the Student Contact Centre.

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