Outline

Overview

This unit takes an interdisciplinary approach to think strategically about a selection of key issues that emerge from human-technology interactions. This unit shows how the human-technology interface impacts on what people do with technology (whether a computer program, or a physical equipment), and especially on mistakes that might be made, which can threaten physical safety, social well-being, privacy, and other human needs. The unit will analyse risks that arise from poor or malicious interface design, how one can evaluate these risks, some different ways to limit the risks, and the ethical implications of this. Students will learn about physiological, cognitive, social and cultural aspects of human interaction; diversity among people (including cultural norms etc) impacting both what they aim to do, and how they 'read' instructions, discover affordances and actually use systems. The unit deals with fairness, accountability and transparency of sophisticated interfaces. The unit will provide insights that are important for future leaders, both of technology creation activities and of organizations that include the users. An interdisciplinary approach to evaluating these systems provides an opportunity for collaboration and identification of many factors that would otherwise not typically be considered by the designers of the system. This leads to a collective effort to improve current systems and for future systems to be designed that not only consider better functionality and usability, but also their impact on people, society and the environment across time and space.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	must be in the Dalyell stream
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Must be in the Dalyell stream
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Alan Fekete, alan.fekete@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 2 hr, mix of question types	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Early feedback quiz Multichoice quiz covering material from weeks 1,2,3 #earlyfeedbacktask	3%	Week 03 Due date: 15 Mar 2026 at 23:59 Closing date: 15 Mar 2026	Online (should take approx 10 minutes)	AI allowed
Outcomes assessed:
Case studies	Draft safety incident analysis report Produce draft report, so other group members can provide feedback	0%	Week 05 Due date: 29 Mar 2026 at 23:59 Closing date: 29 Mar 2026	as appropriate (eg 5 pages)	AI allowed
Outcomes assessed:
Evaluation	Peer feedback Provide feedback to other group members on their draft reports	0%	Week 06 Due date: 05 Apr 2026 at 23:59 Closing date: 05 Apr 2026	as appropriate	AI allowed
Outcomes assessed:
Case studies	Safety incident analysis report Each member of a group produces an individual written report	15%	Week 07 Due date: 19 Apr 2026 at 23:59 Closing date: 26 Apr 2026	as appropriate (eg 5 pages)	AI allowed
Outcomes assessed:
Written work	Usable security observational study Carry out "think aloud" study of user with security feature of a system, then write report on it	10%	Week 10 Due date: 10 May 2026 at 23:59 Closing date: 17 May 2026	as appropriate (eg 5 pages)	AI allowed
Outcomes assessed:
Contribution	Contribution to group Member's contribution to observational study, evidenced in several ways	5%	Week 10 Due date: 10 May 2026 at 23:59 Closing date: 17 May 2026	n/a	AI allowed
Outcomes assessed:
Presentation	Security incident presentation oral presentation on a security incident or concern, submitted as recording	15%	Week 12 Due date: 24 May 2026 at 23:59 Closing date: 31 May 2026	5-8 minutes	AI allowed
Outcomes assessed:
Portfolio or journal	Hand-written summaries summarize class content from the previous week, identifying main topics and confusions	2%	Weekly	Half-page per week	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Early feedback quiz: online, multiple choice test of material from lectures and tutorials of weeks 1-3
Each week, student produces short, hand-written summary of class content from previous week, and identifying any aspects that are confusing. This is scanned and uploaded to canvas as pdf.
Safety incident analysis report: Done as individual work within the context of multidisciplinary groups. Each member produces an individual report that summaries and reflects on causes and lessons from a different safety incident. Each member reads another’s draft report and provides feedback on it.
Usable security observational study: done in multidisciplinary groups which perform “think aloud” observation of users interacting with a security feature in a system, and building on the observations provide report on identified issues regarding usability and security. Members each take distinctive roles within the group. All members receive the same score from the report, except if contribution is inadequate.
Contribution to group: individual score for a group member, reflecting how well they contributed to the group in which they did the usable security observational study, as evidenced in a variety of ways, including each members submitted report on the group's processes.
Security incident presentation: oral presentation on a security incident or threat, its causes and lessons. Recorded as a video and submitted by upload in Canvas.

Detailed information for each assessment can be found on Canvas two weeks before due date.

Use of AI or other assistance in writing: when allowed, this must be acknowledged (including giving details of whatever was produced by tools, so we can assess student contribution to the submission)

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	Work across the semester has been exemplary. Demonstrates the highest level of understanding, critical thinking, and creativity.
Distinction	75 - 84	Work that meets the assignment requirements to a very high standard over the course of the semester.
Credit	65 - 74	Work that meets the assignment requirements to a good standard over the course of the semester.
Pass	50 - 64	Work that meets the assignment requirements to a minimum standard over the course of the semester.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late work is not accepted for early feedback quiz, hand-written summaries, draft incident analysis report, peer feedback, final exam. For other weighted assessments, late work is accepted only up to 7 days after the initial due date; within that period we apply university standard penalties.

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	Administrivia; safety thinking and security thinking; socio-technical systems	Lecture (2 hr)
Week 02	Users and their characteristics, the diversity of users; AI overview	Lecture (2 hr)
Week 02	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 03	Improving safety; AI safety from personal to societal impact	Lecture (2 hr)
Week 03	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 04	Organisations; Guest lecture	Lecture (2 hr)
Week 04	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 05	Dealing with uncertainty; AI correctness	Lecture (2 hr)
Week 05	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 07	Security concepts; AI for security	Lecture (2 hr)
Week 07	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 08	Authentication	Lecture (2 hr)
Week 08	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 09	System security and malware	Lecture (2 hr)
Week 09	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 10	Cryptology mechanisms	Lecture (2 hr)
Week 10	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 11	Information security and privacy; secure AI	Lecture (2 hr)
Week 11	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 12	Network security; guest lecture on regulation	Lecture (2 hr)
Week 12	A mix of case study description, discussions, practice in applying concepts, practice for assessments	Tutorial (2 hr)
Week 13	Reflections, review, and exam preview	Lecture (2 hr)
Week 13	Exam preparation	Tutorial (2 hr)
Weekly	Reading, preparation, contribution on discussion boards, working on assessments, etc. 6-8 hours per week.	Self-directed learning (102 hr)

Attendance and class requirements

Study commitment: Standard unit of study workload at this university should be from 1.5 to 2 hours per credit point which means 9-12 hours for a normal 6 credit point unit of study. For units that are based on research or practical experience, hours may vary

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.

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. communicate clearly and with impact, through oral presentations and written reports
LO2. work in multidisciplinary teams, and with users of different backgrounds
LO3. demonstrate knowledge of case studies where safety of mechanical systems was inadequate, and awareness of approaches that could have reduced these risks
LO4. demonstrate knowledge of case studies where security of computer systems was compromised, e.g. physically, remotely, operationally (esp. social engineering); and awareness of approaches that could have reduced these risks
LO5. demonstrate awareness of processes that can be used to reflect on incidents, identifying contributing issues, and proposing changes that might improve safety or security
LO6. collect observations of users interacting with a system, and in particular to use a think-aloud approach, to identify issues with the usability, security or safety of the system
LO7. demonstrate experience in safety thinking and security thinking, Understand the different sources of risk, understand the balance between incident likelihood, incident damage, and cost; and experience with threat modelling and risk analysis as tools to choose this balance for a given system
LO8. demonstrate awareness of the major challenges for designing safe secure and usable systems, including factors associated with individual users, cultural and organisational contexts
LO9. demonstrate knowledge of a core set of cognitive, physiological, organisational, and other key human factors and their implications for interface design, system safety, and security.

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.

In 2026, we are increasing the coverage of AI-related issues in safety and security. The main student complaint in 2025 was late return of assignments, and the amount of work required for the assignments; in 2026, extra effort will be put to grade quickly. In 2025, students learned well, I hope this continues in 2025.

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

Early feedback task

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ENGD2001: Protecting People Who Use Technology

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

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links