This course is available so that students can pursue, with a small class, topics that are not covered in the regular curriculum that will significantly increase their knowledge of some aspect of computer science. The The topic will vary from instance to instance in response to the research interests and expertise of Academics and sessional staff. 

The topic for each instance will be advertised on the School of Computing website in the first few weeks before the semester

The activities in the course will be a combination of lectures, workshops, reading, writing and project work, as appropriate to the topic. These activities will be specified in the class summary.

 

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. plan and execute project work and/or a piece of research and scholarship in the Special Topic
2. demonstrate an understanding of the body of knowledge and theoretical concepts within the topic in written and/or oral form to a variety of audiences
3. understand the Special topic and its role in modern Computing and applied contexts.

Research-Led Teaching

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.

Required Resources

N/A.

Recommended Resources

•  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.

Staff Feedback

Students will be given feedback in the following forms in this course:

• Written comments
• Verbal comments
• Feedback to the whole class, to groups, to individuals, focus groups

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Gen AI Tools are ALLOWED:

 

“The use of Generative AI Tools (e.g., ChatGPT) is permitted in this course, given that proper citation and prompts are provided, along with a description of how the tool contributed to the assignment. Guidelines regarding appropriate citation and use can be found on the ANU library website https://libguides.anu.edu.au/generative-ai

 Marks will reflect the contribution of the student rather than the contribution of the tools. Further guidance on appropriate use should be directed to the convener for this course.”

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction, Functional abstraction, Introduction to LLM for programming
2	Values, types, expressions, and functions, comments, docstrings
3	Control flow: branching and iteration
4	LLM workflow, testing, debugging, sequences (lists)
5	Sequences (tuple, string), Files and Data Science
6	Problem Decomposition, code quality
7	Sets and Dictionaries and Large programming example
8	Image Processing Part 1
9	Image Processing Part 2
10	Computational Complexity, searching and sorting, recursion vs iteration
11	Command-line processing, and Object Oriented Programming
12	Review

Tutorial Registration

We will have tutorial/lab sessions on Friday 9:00 am - 10:30 am at CSIT N1115/1116; see details on Canvas course page.

Assessment Summary

Assessment task	Value	Due Date	Learning Outcomes
Weekly Lab Quizes	5 %	*	1,2,3,4,5,6,7
Homework assignment 1	5 %	22/03/2026	1,2,3,7
Homework assignment 2	10 %	03/05/2026	1,2,3,7
Mid-semester test	20 %	*	1,2,3,4,5,6,7
Major Project	20 %	17/05/2026	1,2,3,4,5,6,7
Final Exam	40 %	*	1,2,3,4,5,6,7

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Policy and General Information
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

No particular participation requirements, however there is a weekly quiz held in some of the Labs.

Examination(s)

Mid term test and Final Exam

Assessment Task 1

Weekly Lab Quizes

A short quiz will be held in the Labs of the first half of semester (weeks 2-4). Worth a total of 5%. The quiz with the lowest mark of the three will be dropped. No extension, no make up.

Assessment Task 2

Homework assignment 1

Assignment 1 (programming project) will mainly test Python coding skills. You will need to submit your Python code as well as a report documenting the work and results. No make up.

Assessment Task 3

Homework assignment 2

Assignment 2 (programming project) will mainly test Python coding skills. You will need to submit your Python code as well as a report documenting the work and results. No make up.

Assessment Task 4

Mid-semester test

The mid-semester test is designed to test the understanding of the topics covered in lectures up to and including week 4. It is a Lab based test. The test date is to be assigned by central timetabling (likely held in week 5/6).

Assessment Task 5

Major Project

A larger programming project that will test skills of design, programming, debugging and documentation in Python. You will need to submit your Python code as well as a report documenting the work and results. No make up.

Assessment of the project will include a viva conducted by the student's tutor, to discuss the solution and demonstrate understanding of their work.

Assessment Task 6

Final Exam

The final exam is designed to test the understanding of the topics covered for the whole semester. It is a Lab based exam. The exam date is to be assigned by central timetabling.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

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.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

1. The due dates for all assignments and project will be given in the Ed platform and/or Canvas.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

No resubmissions.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students