FIT3191 Generative artificial intelligence
Faculty of Information Technology
FIT3191 Generative artificial intelligence is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology. It isn't offered in 2026. It needs FIT2112 and FIT2004.
- Credit points
- 6
- Offered in 2026
- Not offered
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before FIT3191
Prerequisites
Pass these before you enrol.
After FIT3191
No unit lists FIT3191 as a prerequisite in the 2026 handbook.
Overview
This unit covers the theoretical and practical foundations of Generative Artificial Intelligence (GenAI), building on prior knowledge of deep learning. The unit begins with essential Natural Language Processing (NLP) concepts that underpin modern generative systems, such as text representation, contextual embeddings, and sequence modelling. You then progress to large language models (LLMs), advanced generative techniques including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, as well as their integration into real-world applications such as conversational systems, summarisation, translation, and multimodal AI.
Throughout the unit, you will critically evaluate the architectures and training strategies that power generative systems, with strong emphasis on ethical, societal, and regulatory considerations. By the end of the unit, you will be able to design, apply, and evaluate generative AI solutions responsibly across various complex domains.
Offerings in 2026
The 2026 handbook lists no offerings for FIT3191.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Explain the core principles of NLP and Generative AI, and how these methods enable artificial intelligence systems to interpret and generate human language.
- 2
Analyse the architectures and training strategies that underpin large language models and other generative approaches.
- 3
Apply NLP and Generative AI in practical applications such as dialogue systems, translation, summarisation, and multimodal systems.
- 4
Evaluate generative models using theoretical foundations and appropriate metrics, considering accuracy, creativity, robustness, and limitations.
- 5
Assess the ethical, legal, and societal implications of NLP and generative AI technologies, including bias, fairness, safety, and governance.
Where it fits
FIT3191 is part of 2 areas of study in the 2026 handbook.
Contacts
- Chief Examiners
- Dr Vishnu Monn
Common questions
When is FIT3191 offered?
FIT3191 has no offerings listed in the 2026 handbook.
Which majors and minors include FIT3191?
FIT3191 is part of Artificial intelligence and Software engineering.
More details
- Credit points
- 6
- Level
- 3
- Study level
- Undergraduate
- Faculty
- Faculty of Information Technology
- Type
- Coursework
- EFTSL
- 0.125
- Student contribution
- SCA Band 2
- Study abroad
- Available
- Handbook years
- 20262027