AI-Enhanced Mobile Ecosystems

Unit code: NIT3012 | 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
NIT2004 - OOP Programming with Gen AI Co Pilot; or
NIT2006 - AI Driven Software Engineering with Structured Prompting
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

In this unit, you will design intelligent mobile applications using AI-powered features such as predictive analytics, adaptive UI/UX, and natural language processing. You will integrate secure and scalable AI components using modern frameworks like TensorFlow Lite and ML Kit, and optimise performance for cross-platform delivery. Case studies provide real-world context to evaluate current trends in AI-enhanced mobile ecosystems. By the end of the unit, you will be able to develop scalable, secure, and intelligent mobile apps: preparing you for careers in mobile development, AI integration, or cross-platform systems engineering.

Learning Outcomes

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

  1. Design and develop AI-powered mobile applications using modern frameworks and AI-assisted development tools;
  2. Implement AI-driven features such as predictive analytics, natural language processing, and adaptive UI/UX in mobile applications;
  3. Design and implement AI-enhanced security mechanisms to protect mobile applications from emerging threats and vulnerabilities;
  4. Optimise mobile applications for performance, scalability, and cross-platform efficiency using AI-assisted techniques;
  5. Analyse and apply AI-driven mobile technology trends through case studies of real-world applications individually and as part of a team.

Assessment

For Melbourne campuses

Assessment type: Laboratory Work
|
Grade: 20%
Lab report on mobile application development exercise
Assessment type: Assignment
|
Grade: 45%
Assignment AI enhanced mobile app development and demonstration verified by oral Q&A.
Assessment type: Case Study
|
Grade: 35%
Scenario based problem solving with Q&A verification.

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.

Other locations

For students studying at Henan University
Assessment type: Laboratory Work
|
Grade: 25%
Lab report on mobile application development exercise
Assessment type: Assignment
|
Grade: 25%
Assignment AI enhanced mobile app design and development verified by oral Q&A.
Assessment type: Examination
|
Grade: 50%
Invigilated rinal written examination
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. Examination: Invigilated, time-bound exam includes unseen data sets or problem types. Students must demonstrate deep understanding by explaining reasoning, performing analysis, and applying concepts to new contexts.

Students engage in interactive development labs, collaborative mobile app projects, and critical evaluation of real-world applications. Learning is supported by AI-enabled tools and frameworks, allowing students to prototype, test, and secure their apps using contemporary technologies. Case-based activities help students analyse emerging industry trends and apply them in professional development workflows.

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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