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Unit Synopsis
In this unit, you will study advanced artificial intelligence technologies that drive modern intelligent systems. You will examine the theoretical foundations alongside the practical development of research‑informed AI models, with a focus on how such models learn from large and complex datasets. Through practical design and implementation activities, you will integrate disciplinary knowledge with contemporary research to create effective AI solutions. You will critically evaluate the ethical, social, and professional issues surrounding AI deployment. You will also develop the capability to communicate complex AI concepts, design rationales, implementation choices, and evaluation findings to both expert and general audiences using professional standards.
Details
| Level | Postgraduate |
|---|---|
| Unit Level | 9 |
| Credit Points | 6 |
| Student Contribution Band | SCA Band 2 |
| Fraction of Full-Time Student Load | 0.125 |
| Pre-requisites or Co-requisites |
Pre-requisite: COIT20277 Introduction to Artificial Intelligence. Co-requisite: STAT20029 Statistics for Managerial Decisions. 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 3 - 2027
Term 3 - 2027 Profile
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).
Recommended Student Time Commitment
Each 6-credit Postgraduate 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
To see assessment details from an earlier availability, please search via a previous term.
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%).
Past Exams
All University policies are available on the Policy web site, however you may wish to directly view the following policies below.
This list is not an exhaustive list of all University policies. The full list of policies are available on the Policy web site.
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.
Source: Student Feedback
The current weight of Assignment 3 (Applied Project) is 35%. This should be increased as it is a larger project.
We will evaluate and adjust assessments weight in the future offerings of the unit.
By redeveloping this unit in 2026, the weight of Assessment Item 3 will be increased.
Source: Self-reflection
Large Language Models (LLMs) represent a key direction in the latest AI developments. Since COIT29225 is part of the AI minor, incorporating one week of content introducing LLMs seems essential.
We will add one week content (ppt file) of introduction of LLMs.
One week of content about LLMs has been added to the unit materials.
Source: Student Feedback
Students would like the tutorial to include a quick debrief using real-world scenarios or analogies to make it easier to digest and understand.
In the next offering, the unit will add examples to the weekly tutorial questions that reflect real-world scenarios, helping students better understand the unit's key concepts.
In Progress
Source: Self-Reflection
It is increasingly necessary to include large language models (LLMs) as a dedicated weekly lecture topic in the course, as they have become a foundational technology across modern software development, data science, and artificial intelligence applications.
The lecture slides introducing LLMs should be revised to better explain how contemporary AI systems generate, reason with, and adapt language-based knowledge in real-world contexts. The lecture should emphasise intuitive explanations, visual system diagrams, and concrete use cases, complemented by simplified examples and demonstrations.
In Progress
To see Learning Outcomes from an earlier availability, please search via a previous term.