Outline

Overview

AI literacy enables a set of competencies for individuals to evaluate AI technologies critically; communicate and collaborate effectively with AI; responsibly use AI as a tool, and effectively manage their education and professional roles in the age of AI. This Advanced Topics unit is designed to equip students with the knowledge and skills necessary to thrive alongside artificial intelligence. The imperative for this unit of study stems from the pervasive integration of AI into academic, professional, and personal life, requiring all citizens to understand and critically engage with these powerful tools. The content is structured around four core pillars: first, providing a foundational understanding of AI's scope and technical dimensions; second, building hands-on proficiency in interacting with generative AI technologies; third, instilling the principles of critical, ethical, and responsible AI usage; and finally, enabling students to analyze and articulate the broad implications of AI on society, ensuring graduates are prepared to responsibly engage with AI as users and/or technology developers.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	INFO4990
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Mary Lou Maher, marylou.maher@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam This is a closed book final exam.	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Individual submission of group work done during the Tutorial period This submission is a description, prepared by an individual student, of the group work done in the Workshop session each week.	10%	Multiple weeks	500 words	AI allowed
Outcomes assessed:
Written work	Weekly reflection on learning This submission is a reflection on what the student learned each week and what the student found confusing.	10%	Multiple weeks	100 words	AI allowed
Outcomes assessed:
Written work	Definitions and Use of AI This is a written report on the student's understanding of AI and its use in student learning.	25%	Week 06	5000 words	AI allowed
Outcomes assessed:
Written work	Case Study of AI Ethical and Social Impact This submission is a description and critical analysis of a mock case study on the ethical and social impact of AI.	25%	Week 13	5000 words	AI allowed
Outcomes assessed:

Assessment summary

Tutorial/Workshop	Individual submission of group work done during the Tutorial period	Weekly post-tutorial	15%
Weekly Reflections	Reflection on the learning outcome for each topic	Weekly post-tutorial	15%
Reports	Submitted online via Canvas, 5000 word report.	Weeks 9 and 13	50%
Final Exam	Closed book exam, 2 hours	Formal Exam Period	20%

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49 It is a policy of the School of Computer Science that in order to pass any unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to AI Literacy and Competency	Lecture (1 hr)
Week 02	AI and Machine Learning	Lecture (1 hr)
Week 02	AI and Machine Learning, Knowledge vs Data in AI	Workshop (2 hr)
Week 03	Information retrieval in web search engines vs generative systems	Lecture (1 hr)
Week 03	Information retrieval in web search engines vs generative systems	Workshop (2 hr)
Week 04	Tokens, Transformer Algorithms and Attention Mechanisms in Deep Learning	Lecture (1 hr)
Week 04	Tokens, Transformer Algorithms and Attention Mechanisms in Deep Learning	Workshop (2 hr)
Week 05	Training Large Language Models	Lecture (1 hr)
Week 05	Training Large Language Models	Workshop (2 hr)
Week 06	Interacting with Generative AI Models	Lecture (1 hr)
Week 06	Interacting with Generative AI Models	Workshop (2 hr)
Week 07	Prompt engineering for student learning	Lecture (1 hr)
Week 07	Prompt engineering for student learning	Workshop (2 hr)
Week 08	Interacting with Retrieval Augmented Generative Systems	Lecture (1 hr)
Week 08	Interacting with Retrieval Augmented Generative Systems	Workshop (2 hr)
Week 09	Academic and Professional Integrity and the Responsible Use of AI	Lecture (1 hr)
Week 09	Academic and Professional Integrity and the Responsible Use of AI	Workshop (2 hr)
Week 10	Security and Privacy in AI	Lecture (1 hr)
Week 10	Security and Privacy in AI	Workshop (2 hr)
Week 11	Ethical issues in AI, Integrity, Authorship and Ownership in the Age of AI	Lecture (1 hr)
Week 11	Ethical issues in AI, Integrity, Authorship and Ownership in the Age of AI	Workshop (2 hr)
Week 12	Perception and Societal Impact of AI	Lecture (1 hr)
Week 12	Perception and Societal Impact of AI	Workshop (2 hr)
Week 13	AI Policy and the Future of Work, Perception and Societal Impact of AI	Lecture (1 hr)
Week 13	AI Policy and the Future of Work Perception and Societal Impact of AI	Workshop (2 hr)

Attendance and class requirements

Attendance at all lectures is strongly encouraged.

Attendance at all workshop sessions is mandatory. Missing more than 2 sessions without approval may be a valid basis for failing in the unit.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

See canvas

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Identify and define the key concepts and principles underlying AI paradigms, such as machine learning, natural language processing, computer vision, robotics, and cognitive systems
LO2. Understand how to select tools and use Generative AI, including prompt engineering, to support and enhance their learning.
LO3. Understand how to critically examine the societal and ethical implications of AI, especially in educational contexts.
LO4. Analyse privacy, bias, algorithmic fairness, data security, and the impact of AI automation on education and employment.
LO5. Critically evaluate the advantages and limitations of employing AI.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The previous offering of the unit was on a different topic.

Additional information

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

All written assignments submitted in this unit of study may be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

INFO4994: AI Literacy and Competency

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links