Course coordinator
Professor Janet Wiles
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
The course will draw on Ben Shneiderman’s text book “Human Centred AI (2022). It will be taught collaboratively, including interdisciplinary perspectives. Lecturing staff are expected to be primarily from the Human Centred Computing Discipline with invited lectures.
Topics to include:
1. Human-centred AI in real world systems – studying the human viewpoint
2. Human-centred AI: Empowering People, Expanding Possibilities
3. AI approaches: Human Emulation and Useful Applications (strengths and limitations of deep learning and generative AI; and alternative AI approaches)
4. Governance Structures for Human-Centered AI
5. Designing HCAI systems “with (not for)” communities (methodologies such as participatory design)
6. HCAI with Indigenous knowledges
7. Ethics of HCAI
Practical topics in tutorials and assignments could include:
• Stopping AI-Driven Misinformation and Criminals
• Supporting Environmental Protection, Social Justice And Human Rights
• Compassion in Caring for Our Older Adults
• HCAI in Urban robotics
• Beyond Robots: Notbots and Newbots
This is a fast moving field, and the course will explore recent as well foundational material in each topic.
Course requirements
Assumed background
Programming in Python and a basic understanding of Design Thinking concepts;
Plus # 16 of prior credit (equivalent to 1 year of study). This requirement ensures that students have some technical experience to draw on for the discussion aspects of the course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or ENGG1001) and DECO1100
Incompatible
You can't enrol in this course if you've already completed the following:
DECO7281
Jointly taught details
This course is jointly-taught with:
- DECO7281
Lectures, applied classes and assignments are jointly taught.
Course contact
Course staff
Lecturer
Professor Janet Wiles
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course aims to develop foundational knowledge and skills in human-centred AI, combining the technical aspects of AI with human-centered design and ethics, for impact in real-world contexts. It will cover real-world case studies in practical applications of AI in healthcare, education, and decision-support systems, where the aims are to assist humans with AI support, rather than replacing them. The course includes studying the role of human values in technology and their incorporation into AI system design and deployment.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the core principles of HCAI and differences from traditional AI.
LO2.
Evaluate the role of human-AI collaboration in enhancing decision-making, productivity, and creativity.
LO3.
Identify potential ethical issues in AI systems, including bias, fairness, privacy, and transparency.
LO4.
Analyse human cognitive and behavioural factors that impact interactions with AI systems.
LO5.
Evaluate AI features that facilitate human-AI collaboration for effective teamwork in fields like healthcare, education, or creative industries.
LO6.
Critique AI systems in terms of inclusivity and accessibility.
LO7.
Discuss and critique the societal, cultural, and economic benefits and risks of AI systems
LO8.
Explore novel human-centered approaches to AI in fields such as healthcare, education, art, or public policy.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution |
Active participation in Applied Class activities
|
16% |
4/08/2025 - 13/10/2025
Students must attend and participate in their scheduled applied class. Written submissions are due at the end of each session. |
| Project |
Assignment
|
34% |
21/10/2025 3:00 pm |
| Presentation |
Assignment (Interview)
|
Pass/Fail |
23/10/2025 - 14/11/2025 |
| Examination |
End of semester exam
|
50% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Active participation in Applied Class activities
- Hurdle
- Identity Verified
- In-person
- Mode
- Activity/ Performance, Written
- Category
- Participation/ Student contribution
- Weight
- 16%
- Due date
4/08/2025 - 13/10/2025
Students must attend and participate in their scheduled applied class. Written submissions are due at the end of each session.
- Learning outcomes
- L02, L03, L04, L05, L06, L07, L08
Task description
Participate in applied classes and complete submission material, which will include presenting oral reports or work, participating in discussions, and submission of written material. Participation material is due at the end of your scheduled applied class.
Feedback will be given during or following the applied classes in weeks 2-4 prior to the Census Date in week 5.
AI tools can be used in applied classes where allowed, which will be specified in each applied class sheet and must be acknowledged. Depending on the activity, appropriate acknowledgement could include a cover sheet, a section with a statement about AI use, and/or citing sources in a reference list.
Hurdle requirements
In order to pass the course, students must gain at least 50% (that is 8/16) of the applied class marks. Failure to meet this requirement will result in the final grade being capped at a 3, regardless of performance in other assessment items.Submission guidelines
Participation material is due at the end of your scheduled applied class.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
To accommodate unforeseen circumstances such as illness, your grade will be based on the best 8 out of 10.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
As time is provided within the applied class to complete all work, no late submissions will be accepted and a 100% late penalty applies.
This has been approved by the Associate Dean (Academic).
Assignment
- Hurdle
- Mode
- Oral, Written
- Category
- Project
- Weight
- 34%
- Due date
21/10/2025 3:00 pm
- Other conditions
- Student specific, Secure.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07, L08
Task description
The assignment will involve researching a set topic in HCAI, and preparing a report. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tool via an oral interview after submission.
AI tools can be used in this assessment where allowed, which will be specified in the assignment sheet and must be acknowledged. Depending on the activity, appropriate acknowledgement could include a coversheet, a section with a statement about AI use, and/or citing sources in a reference list.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
In order to pass the Assignment, students must gain at least a Pass for both the Assignment AND Assignment Interview.Submission guidelines
Submit to Blackboard and Gradescope.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 days as feedback will be provided within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment (Interview)
- Hurdle
- Identity Verified
- In-person
- Mode
- Oral
- Category
- Presentation
- Weight
- Pass/Fail
- Due date
23/10/2025 - 14/11/2025
- Other conditions
- Secure.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07, L08
Task description
This item forms the secure element of the Assignment.
The oral interview is to be completed in-person.
If you understand your own assignment and can explain your work, you have nothing to fear from this process. If you legitimately use permitted code from other sources (following the usage/referencing requirements in the assignment specification), then you are expected to understand that code.
If you are not able to adequately explain the content of your project as requested at the interview, then your assignment mark will be scaled down based on the level of understanding.
Interview invitations will be issued by email to your student email account at any time up until the date of submission until week one of exams. Failure to respond to an interview invitation by the deadline stated in the invitation (which will be at least one week after the invitation is sent) or failure to attend a scheduled interview will result in zero marks for the assignment unless exceptional circumstances can be demonstrated with supporting evidence.
The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
In order to pass the Assignment, students must gain at least a Pass for both the Assignment AND Assignment Interview.Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
If you are unable to attend your allocated interview, one reschedule is permitted, which will occur in Week 13. To apply for a reschedule, you need to apply for an extension via my.UQ.
If your extension request is approved, please contact the Course Coordinator to arrange a make-up interview.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Consistent with industry practice around presentations to clients/industry partners, no late submissions will be accepted and a 100% late penalty applies.
This has been approved by the Associate Dean (Academic)
End of semester exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Other conditions
- Secure.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
The end-of-semester exam will include short questions and multiple choice questions on the course material.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Marks | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Final marks will be calculated by adding the percentage marks (unrounded) for the applied classes, assignment and exam. Final marks will be rounded to the nearest whole number prior to calculating grades.
In order to pass the course, students must gain at least 50% (that is 8/16) of the applied class participation. Failure to meet this requirement will result in their overall mark capped at 49% and their final grade capped at 3.
Students who do not meet the 50% hurdle requirement on the applied class participation and do not receive an overall grade of 50% or more will have their overall mark capped at 46% and their final grade capped at 2.
The course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is not available for some items in this course.
Students who do not meet the 50% hurdle requirement on the applied class participation will not be eligible for supplementary assessment. For students who are eligible, the course coordinator will determine if the supplementary exam will be oral or written depending on which components of the course were not met.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Laptop for tutorial work | In-class participation will require a laptop (bring your own device BYOD) during tutorial sessions. | own item needed |
| Access to online genAI tools (free will be sufficient) | Free genAI tools will be used in the tutorials and for the assignment. Students will also be able to use paid versions if they choose. | |
| Internet access through UQ VPN | Internet access to library resources requires UQ VPN. |
Additional learning resources information
Students who don't have access to a device should check out the BYOD page:
https://support.my.uq.edu.au/app/answers/detail/a_id/3086
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lecture with in-class interaction Weekly lectures will be held from weeks 1-13. These will provide essential background and preparation material for the tutorials and should be attended in person where possible or watched prior to the tutorial. Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08 |
Multiple weeks From Week 2 To Week 13 |
Applied Class |
Applied Class Weekly Applied Classes will be held from weeks 2 to 13. Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: