This course teaches introductory programming, fundamental programming language and computer science concepts, and computational problem solving illustrated with applications common in science and engineering, such as data analysis, visualisation, and image processing. We will take a different approach, including prompting and working with GenAI, with emphasis on testing, debugging and problem decomposition, and using GenAI to help understand code.
These skills will empower students to write software with the aid of GenAI. Assessment will include exam(s) without the aid of GenAI. Students will also create larger software projects with the aid of GenAI. The course does not require any prior knowledge of programming or computer science. It is designed to be a stand-alone computing unit.
Students who wish to study computing courses at a higher level should check course prerequisites as they may need to complete COMP6710 or COMP7710 in addition to COMP6730.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Apply advanced programming constructs and structured programming methodologies to develop reliable software systems.
- Explain key concepts in AI-assisted programming, including Large Language Models (LLMs), prompting, problem decomposition, and top-down design.
- Apply AI-assisted programming workflows and advanced prompt-engineering techniques to guide, refine, and improve code generated by AI assistants.
- Analyse the computational complexity of programs, and apply debugging and testing techniques, including black-box and white-box testing.
- Evaluate and improve code quality using critical judgement.
- Design and implement complete programs that solve practical problems using structured programming methodologies and AI-assisted prompting.
- Critically evaluate the role, benefits, limitations, and risks of generative AI in software development projects
Other Information
This is an introductory course to computing and programming with Python. It teaches programming using Large Language Models as part of programming. This approach is currently a topic of research in learning for computing, being investigated at a small number of Universities worldwide. Current approaches to AI-assisted programming, and LLMs generally, are still being actively researched. We will teach students how to effectively use tools that are the topic of ongoing research into their programming work-flow. Students will gain an appreciation of the strengths and weaknesses of current LLM models in the context of AI-assisted programming.
Indicative Assessment
- Homework, Assignments and Lab Tests (20) [LO 1,2,3,4]
- Programming Project (20) [LO 1,2,3,4,5,7]
- Final Exam and Mid-semester Test (60) [LO 1,2,3,4,5,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Lectures, tutorials, assignments, self-study & exam prep for a total of 130 hours.
Inherent Requirements
None
Requisite and Incompatibility
Prescribed Texts
There are no prescribed texts.
Preliminary Reading
- Leo Porter and Daniel Zingaro, Learn AI-Assisted Python Programming, Second Edition, Published by Manning, Distributed by Simon & Schuster.
- Downey, A., Wentworth, P., Elkner, J., & Meyers, C. (2016). How to think like a computer scientist: learning with python 3.
- Sundnes, J. (2020). Introduction to scientific programming with Python (p. 148). Springer Nature.
Assumed Knowledge
Students are assumed to have achieved a level of knowledge of mathematics comparable to at least ACT Mathematics Methods or NSW Mathematics or equivalent.
No programming, Computer Science or IT experience or skills are required, this course is designed for students without a computing background.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2026 | $5520 |
- International fee paying students
| Year | Fee |
|---|---|
| 2026 | $7020 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 8706 | 27 Jul 2026 | 03 Aug 2026 | 31 Aug 2026 | 30 Oct 2026 | In Person | N/A |