AI-Enhanced Mobile Ecosystems
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT2006 - AI Driven Software Engineering with Structured Prompting
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
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:
- Design AI-powered mobile applications using modern frameworks and AI-assisted development tools;
- Synthesise AI-driven features such as predictive analytics, natural language processing, and adaptive UI/UX in mobile applications;
- Compose AI-enhanced security mechanisms to protect mobile applications from emerging threats and vulnerabilities;
- Adapt mobile applications for performance, scalability, and cross-platform efficiency using AI-assisted techniques;
- Analyse 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.
Laboratory Work: Students complete practical mobile application development tasks and submit a lab report documenting their findings and outcomes.
Assignment: Includes structured milestone reviews and in-class progress checks. Student's idividual 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
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
Laboratory Work: Students complete practical mobile application development tasks and submit a lab report documenting their findings and outcomes. 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.
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):