Outline

Overview

This unit introduces students to principles and fundamentals of interface design in emerging technologies. Following a human-centered design approach, this unit of study provides students with an understanding of industry standard principles for user interface design. Students will gain practical skills for prototyping and testing interactive interfaces through the application of tools and techniques for a variety of different platforms. For example, students will learn how to create low-fidelity prototypes using wireframes for web and mobile user interfaces. The aim is to develop appreciation of visual design principles and their impact on the usability and user experience of interactive products.

Unit details and rules

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

Teaching staff

Coordinator	Kiran Ijaz, kiran.ijaz@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
In-class quiz	A1 Weekly Quiz There will be ten quizzes delivered via Canvas over the course of semester. Quizzes will be open in Week 2 - Week 9 and Week 10 - Week 12.	10%	Multiple weeks	60 minutes	AI allowed
Outcomes assessed:
Creative work	A2 Design of Mood Board Design a Mood Board of the chosen topic to represent the concepts	10%	Week 05 Due date: 04 Sep 2025 at 23:59 Closing date: 11 Sep 2025	1 page max	AI allowed
Outcomes assessed:
Presentation	A3 Conceptual Design Video Presentation 2-mins video presentation & presentation slides of up to 15 pages	20%	Week 06 Due date: 11 Sep 2025 at 23:59 Closing date: 18 Sep 2025	2 minutes Video	AI allowed
Outcomes assessed:
Presentation	A4 Final Design Demonstration Demonstrate final design to peer group and teaching staff	25%	Week 13 Due date: 03 Nov 2025 at 19:00 Closing date: 03 Nov 2025	5 minutes	AI allowed
Outcomes assessed:
Written work	A5 Final Design Usability evaluation & interactive prototype-Individual Report	35%	Week 13 Due date: 06 Nov 2025 at 23:59 Closing date: 13 Nov 2025	10 pages maximum	AI allowed
Outcomes assessed:
= 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

In this unit, you will need to complete ten quizzes, three design assignments, and one in-class demonstration of your design project. The weighting of these assessments and their due dates are detailed in the table below. All assessments in this unit must be completed individually.

In-class quizzes (A1) will be available on Monday mornings and must be submitted by Tuesday 11.59pm. They will be delivered via Canvas.

The first design assignment will be a Mood Board of your chosen topic (A2).

The second design assignment (A3) will be delivered as a 2-minute video presentation combined with slides.

A4 will be a in-class demonstration of your design assignment progress.

The third design assignment (A5) will be submitted as a design report.

All assignments must be uploaded by the due dates specified on Canvas.

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 of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

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 submission of work (1) It is expected that unless a simple extension, special consideration or disability adjustment has been granted, students will submit all assessment items for a unit of study on the due dates specified. If an assessment is submitted or otherwise completed within a period of extension granted by successful application for simple extension, special consideration or an assessment adjustment determined by Disability Services, no academic penalty will be applied to that piece of assessment. (2)If an extension is not sought, not granted, or is granted but work is submitted after the extended due date, the late submission of assessment will result in an academic penalty as follows: Late submission is allowed within the10 days past the due date, with 5% penalty applied per day.

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 Interface Design	Lecture and tutorial (3 hr)
Week 02	Human-Centred Design & Principles of Good UI Design	Lecture and tutorial (3 hr)
Week 03	Visual Design Principles I: Layout & Hierarchy	Lecture and tutorial (3 hr)
Week 04	Visual Design Principles II: Colour, Typography & Iconography	Lecture and tutorial (3 hr)
Week 05	User Research & Personas	Lecture and tutorial (3 hr)
Week 06	Information Architecture & Navigation	Lecture and tutorial (3 hr)
Week 07	Wireframing & Sketching UI Concepts	Lecture and tutorial (3 hr)
Week 08	Mobile UI Design	Lecture and tutorial (3 hr)
Week 09	Design Guidelines	Independent study (3 hr)
Week 10	Design Systems & Component Libraries (GL)	Lecture and tutorial (3 hr)
Week 11	Usability Testing & Feedback Loops	Lecture and tutorial (3 hr)
Week 12	Accessibility in UI Design	Lecture and tutorial (3 hr)
Week 13	Industry Panel	Lecture and tutorial (3 hr)

Attendance and class requirements

Students are required to meet the School of Architecture, Design and Planning's minimum attendance requirement of 90% of tutorials. That is, two unexplained absences. Tutors will keep attendance and must be advised by email, in advance, if you are unable to attend due to illness or misadventure.

For absences that coincide with a group assessment task, you will need to apply for special consideration and must meet the eligibility criteria to have your absence approved. Once approved, your component of the group assessment task will be rescheduled. If special consideration is not approved, you will receive an Absent Fail for the unit. An absent fail means, even if you submit all other assignments for the unit you will still fail the unit as you will have not completed all of the mandatory assessment tasks.

In person lecture attendance and recorded lecture views will be monitored, as most of the foundational content for this unit is ONLY delivered in lectures. Tutorials do not repeat lecture material. Keywords, concepts, and methods that are defined and explained in lectures are expected to be applied in tutorials, assessments, and other units in Bachelor of Design (Interaction Design) program.

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. Plan, document and execute research with rigour;
LO2. Assess the requirements of an interface design problem and translate them into an actionable set of interface design tasks;
LO3. Understand and apply interface design principles and concepts;
LO4. Apply a variety of design approaches and techniques for ideation;
LO5. Use a variety of tools to prototype and develop interfaces;
LO6. Recruit participants and conduct usability evaluations on lo and hi-fidelity prototypes;
LO7. Present research findings and possible solutions;
LO8. Plan and document an interface design project;

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.

This unit has been updated to reflect it's original objective of introducing web design to a non-design audience.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

DECO2102: Introduction to Interface Design

Semester 2, 2025 [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