COIT13244 - Advanced Artificial Intelligence

Showing: 2026 HE Term 1
General Information

Unit Synopsis

The unit introduces you to contemporary AI technologies that extend beyond foundational artificial intelligence. Building on your prior knowledge of introductory AI and machine learning, you will study the practical use and guided implementation of selected modern AI approaches, including applied deep learning, natural language processing, generative AI, large language model applications, and AI agent workflows. You will examine how AI models represent, process, and generate information from text, structured data, knowledge sources, and selected multimodal inputs. Key Natural Language Processing (NLP) concepts such as text representation, sequence modelling, language understanding, and language generation will be first covered before progressing to practical applications of large language models in conversational systems, intelligent assistants, retrieval-supported applications, and generative AI tools. Through guided programming activities and applied projects, you will develop the skills required to design, implement, and evaluate functional AI-enabled applications.

Details

Level Undergraduate
Unit Level 3
Credit Points 6
Student Contribution Band SCA Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Pre-requisite: COIT12213 Introduction to Artificial Intelligence

Pre-requisite: STAT11048 Essential Statistics

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 1 - 2026


Attendance Requirements

All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Undergraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

Assessment Task Weighting
1. Practical Assessment 25%
2. Practical Assessment 35%
3. Project (applied) 40%

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

Previous Feedback

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Unit Learning Outcomes

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

  1. Demonstrate an understanding of contemporary AI technologies, including deep learning, natural language processing, generative AI, large language models, and AI agents
  2. Apply deep learning techniques to develop AI models for selected real-world problems involving text and structured data from multimodal knowledge sources
  3. Design and implement natural language processing and large language model-enabled applications, such as conversational systems, intelligent assistants, and generative AI applications
  4. Evaluate the performance, limitations, and ethical implications of the neural networks-based and language-based models in real-world applications.

The Australian Computer Society (ACS), the professional association for Australia's ICT sector, recognises the Skills Framework for the Information Age (SFIA). SFIA defines levels of responsibility and skills, and it is adopted by organisations, governments and individuals in many countries. SFIA is increasingly being used when developing job descriptions and role profiles. SFIA can be used by individuals to create a personal skills profile. ACS recognises the SFIA and provides MySFIA tool to help ACS members build a skills profile.

This unit contributes to the following workplace skills as defined by SFIA 9 (the SFIA code is included):

  • Analytical classification and coding (ANCC)
  • Application Support (ASUP)
  • Artificial intelligence (AI) and data ethics (AIDE)
  • Data science (DATS)
  • Machine learning (MLNG)
  • Programming/software development (PROG)
  • Software design (SWDN)
Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Practical Assessment • • •
2 - Practical Assessment • • •
3 - Project (applied) • •
Alignment of Graduate Attributes to Learning Outcomes
• Introductory Level
• Intermediate Level
• Graduate Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Communication • •
2 - Problem Solving • • • •
3 - Critical Thinking • • •
4 - Information Literacy •
6 - Information Technology Competence •
8 - Ethical practice •
10 - First Nations Knowledges •
Alignment of Assessment Tasks to Graduate Attributes
• Introductory Level
• Intermediate Level
• Graduate Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8 9 10 10