Webinar insights: Master and Graduate Diploma in Information Technology (Artificial Intelligence)

Introduction

Maggie Hill: Hello, everyone. Welcome to this evening’s webinar. My name is Maggie Hill and I’ll be your host. Tonight’s webinar is focused on QUT Online’s Master and Graduate Diploma in Information Technology, with a focus on artificial intelligence.

The webinar is designed to provide you with an overview of the course from both an academic and practical perspective.

Before we begin, I should let you know that this session is being recorded, and we’ll make that recording available to you via email at a later date.

Acknowledgement of Country

Maggie Hill: I’d also like to acknowledge the traditional owners of the lands on which we are meeting, as well as the Turrbal and Yuggera people, who are the First Nations owners of the lands where QUT now stands.

I pay my respects to Elders past and present and acknowledge the important role Aboriginal and Torres Strait Islander peoples continue to play in the knowledge, leadership and care of communities.

Speakers and webinar structure

Maggie Hill: This webinar is designed to help you decide whether studying artificial intelligence through QUT Online is the right fit for your goals. During the webinar, you’ll hear more about the academic content and learning outcomes, as well as what it’s like to study these courses in an online environment.

We’re joined by Cliff Harris, Academic Delivery Lead, who will discuss course content and career pathways. We’re also joined by Waikay Lau, Academic Program Lead, who will discuss the online learning experience.

After we’ve heard from Cliff and Waikay, there will be an opportunity to ask any questions that haven’t been addressed. We want you to finish this evening’s webinar feeling informed and able to decide whether this course is the right fit for you.

Introducing Cliff Harris

Maggie Hill: Without any further ado, I’d like to welcome our first speaker, Cliff Harris. Cliff brings a strong focus on ensuring that students develop practical, job-ready skills in artificial intelligence.

His work centres on designing learning experiences that connect theory with real-world application. Cliff, thanks for being here.

Cliff Harris: Thanks, Maggie. Thanks for having me.

AI skills employers are looking for

Maggie Hill: Let’s start by talking about what we’re seeing in the current AI landscape. We’re in the middle of a major shift, which most people online would know about. What does this mean for the kinds of skills employers are looking for in the workforce?

Cliff Harris: It’s a great question. For context, I’m an ethical hacker as well as the Academic Delivery Lead at QUT Online. I help develop, implement and deliver the course content, but I also have that real-world perspective of what’s being used in organisations.

I was speaking with someone who was interested in one of the QUT Online courses, and they said they didn’t want to learn programming. They were more interested in learning how to prompt with AI.

That made me think about YouTube influencers and what people often see online. My answer was that it would probably end poorly for their career if they went down that path.

AI hallucinates. AI gets things confidently wrong. If you can’t read the code it produces, or you don’t understand the technical concepts behind it, you can have a bad experience.

We’re not talking about people coding small things privately at home. This is about your career trajectory and how you see yourself moving into an organisation.

If you’re using AI and you don’t actually understand how to code, it’s very easy to get things wrong. Employers have figured that out, from what I see when I engage with them.

AI, to me, is a force multiplier. It multiplies what you know and can make you dramatically faster. It can help you understand things, but if you don’t know the thing to start with, then you’re essentially multiplying zero by zero.

Employers want technical skills. They want machine learning, programming, data analysis and cybersecurity in the AI space.

They also want those skills paired with critical thinking and sound judgment. They want people who can evaluate what AI produces and spot the risks. That’s what the industry is looking for, not someone who can just type in a prompt and get AI to do things for them.

Career pathways in artificial intelligence

Maggie Hill: Technical understanding, rather than a self-taught prompting approach, is critical if people want to build a career in this space. Can you talk in more detail about some of the career pathways in artificial intelligence, including technical roles and roles that sit at the intersection of business and technology?

Cliff Harris: Absolutely. Some AI roles are highly technical, such as machine learning, programming and building AI systems. That’s a very technical field, and we cover that as part of the master’s program.

It’s a great pathway, but it’s just one of many. We’re seeing real growth in AI consulting, AI product management, governance and compliance, responsible AI, digital strategy and AI cybersecurity.

Many organisations need someone to build AI, but they also need people who understand integration. When businesses approach AI professionally, they need a holistic approach. It’s not just about building AI; it’s about integrating it properly into organisations.

Some of the best AI practitioners I’ve spoken to haven’t necessarily come from a technical background. They’ve come from business, healthcare, finance, education and government. It’s about bringing those other skill sets into the AI space, and organisations are seeking those skills.

Why study AI through QUT Online?

Maggie Hill: If someone is interested in building a career in this space, why would you recommend these QUT Online courses? What makes them stand out?

Cliff Harris: I’m probably a little biased, given that I work in this space, but there is a strong practical application in these courses.

For instance, in one of our machine learning units, we cover the foundations, including regression, classification and how models learn, through to neural networks and computer vision.

Those concepts can sound intimidating to someone who doesn’t understand them, and they do take work to get your head around. But the learning content is built to lead you through it and help you understand how AI models work.

Students build things with their hands. Week after week, they work on projects, so it’s very hands-on. You learn how to use AI and how to build upon that knowledge.

There are modules on why AI makes mistakes, adversarial attacks and how AI can be attacked. There are also ethics and governance areas.

We try to build something that is not only technical but also covers the foundational aspects of AI, including ethics, governance and human-centred design. It’s broad in what it aims to cover.

Graduate Diploma versus Master’s degree

Maggie Hill: It would be great if you could talk about the Graduate Diploma versus the Master’s degree. How do they differ in terms of content?

Cliff Harris: The Graduate Diploma gives you the core foundations. It covers the programming aspects you need to understand.

That goes back to the earlier discussion: you can’t get AI to do things well if you don’t understand the technical concepts behind it. Having a basic understanding of programming is definitely a plus, and that’s why we include it in the diploma.

The course then leads into other technical aspects, such as machine learning, cybersecurity and software lifecycle management. It builds from there.

You can absolutely complete the Graduate Diploma on its own. Taking it further with the Master’s adds more depth and breadth to that foundation.

Background and experience needed

Maggie Hill: Is there any background or baseline experience that people need if they want to study either of these courses?

Cliff Harris: Having a passion for it is definitely a plus. There’s a lot of work involved, and what you put into it is what you’ll get out of it.

I’ve worked with students from different career paths, including physiotherapy and healthcare. They’ve brought their background knowledge into this space and have really prospered.

They’ve had fantastic outcomes and have already moved into the sector.

Maggie Hill: Thank you so much, Cliff. I really appreciate you sharing those insights. Cliff will join us for the Q&A segment at the end, so if you’ve got any questions for him, feel free to pop them in the chat now and we’ll get to those at the end of the session.

Cliff Harris: You’re welcome.

Introducing Waikay Lau

Maggie Hill: I’d now like to welcome our second speaker, Waikay Lau. Waikay is the Academic Program Lead and plays a key role in shaping the online learning experience for students in these programs.

He works closely with teaching teams to ensure students are supported, engaged and confident throughout their studies. Welcome, Waikay. Thank you for joining us.

Waikay Lau: Thank you, Maggie. It’s very good to be here.

What online study looks like

Maggie Hill: To start with, can you talk to us about what studying online looks like in this course, and how students engage with the learning materials and activities?

Waikay Lau: Absolutely, Maggie. A lot of people think online study means reading information, answering questions, reading more information and answering more questions, all on their own. But it’s really not like that at QUT Online.

At QUT Online, delivery is very interactive and structured. We have four teaching periods each year. Each teaching period is like a term and runs for about 10 weeks, including a consolidation week in the middle and an assessment week at the end.

There are eight weeks of learning. Students move through weekly modules with materials to read, videos to watch and real-life case scenarios to analyse. They then put that learning into action through activities and discussions.

Those activities and discussions are where students interact with their peers and their Online Learning Advisor. The Online Learning Advisor posts weekly messages, answers questions, facilitates peer-to-peer discussions and hosts a live collaboration session every two weeks.

These sessions are shaped by what students want to hear. They might focus on a topical subject, a concept students want more information about or, close to assessment time, how to approach an assignment.

The sessions are recorded and available to help students work through the situations they encounter. The QUT Online learning space is interactive, but if students prefer to read through what others are saying and writing, that’s fine too. Students can find their own pace in how they engage with online study.

Peer connection and town halls

Maggie Hill: What about students engaging with each other directly? Can you talk about how they connect in the online environment and what role town halls play?

Waikay Lau: One of the most frequent questions we get from students is whether they will get to meet people. That’s a valid question because building relationships and making connections is one of the key benefits of online study.

When people share a common experience, a bond can form. One of my favourite student experiences is our town hall. These are live sessions where we bring in industry guest speakers.

These are people at the peak of their IT careers who talk to students about how they got into IT, how to make connections in the IT world, how to get the job they want and what they see happening in the fast-changing world of AI.

The industry guest speakers come from different areas, including heavy industry, mining, data science, AI, recruitment agencies and professional organisations. They want to talk to students and often welcome contact with them.

That’s what makes QUT Online interesting: the ability to build connections with peers, Online Learning Advisors and industry guest speakers through town halls.

Assessment and real-world AI practice

Maggie Hill: The town halls sound really interesting, and I can imagine students would get a lot out of them. Another thing prospective students may have top of mind is assessment. Can you tell us about the different types of assessments students can expect and how they reflect real-world AI practice?

Waikay Lau: Assessment is always front of mind for students. I’m often asked what the point of assessment is when AI can get things right. Cliff answered that in his opening comments. AI can do a lot, but it can also hallucinate.

To be a professional in the AI field, you need to know the difference between fact and fantasy. You need technical skills and sound judgment. Assessment helps make sure you know your stuff so you can detect when AI is hallucinating and make it work for you.

At QUT Online, there isn’t just one type of assessment. It depends on the unit you’re studying, which keeps the experience interesting and reflects the different skills needed in the workplace.

Across the Graduate Diploma in IT and AI, and into the Master’s, students may work on reports, problem-solving tasks, coding activities and reflections. They may work individually or in teams.

That mixture is intentional because it replicates the workplace. You’re not just sitting in an exam. You’re solving problems, collaborating with others and communicating your ideas clearly.

There are exams at QUT Online, and they give students an opportunity to consolidate their knowledge and build confidence under pressure, which is also valuable professionally.

We also often have oral assessments. Communication is important. Students need to be able to articulate technical problems, especially when decision-makers may not have the same depth of technical knowledge.

Presenting ideas, thinking on your feet and pitching solutions are skills students need in day-to-day work. Assessment is part of learning, but it’s designed to help build real-world, transferable skills students will use in their careers.

Support for online students

Maggie Hill: It sounds like there is a lot of support available if students need it.

It would also be good to understand, particularly for students who might be new to online study or returning to study after a long time away, how they can access support and what that support looks like.

Waikay Lau: That’s a really good question. QUT Online IT programs are postgraduate courses, so many students are working full-time, have family responsibilities and may have been away from formal study for a few years. The support is designed with that in mind.

Right from the start, course consultants work with students to understand where they want to go in the IT space and which discipline may suit them best.

Alongside AI, QUT Online also offers study areas such as cybersecurity, data science, software development, computer science and business analysis. Course consultants can help students decide which path is the best fit.

Once students begin, there are teams of people providing support throughout the study journey.

Student services can help students plan their study, manage unexpected circumstances, understand which units to take next and decide whether they need to take a break.

Probably the most important person students encounter online is the Online Learning Advisor, or OLA.

Cliff used to be an OLA. He is also an ethical hacker, teaches in the program and helps design the course. That means students are learning from people with current industry experience.

Many OLAs work full-time in their professional field and bring that subject matter expertise into the online classroom.

OLAs are the first point of contact for questions. They can help students understand how the online environment works, use Canvas, manage their time and know what to expect each week.

The course itself is also designed to build confidence. Students work through each weekly module and complete questions and activities that test their understanding.

Students can move from feeling hesitant to building confidence because they are supported throughout the course. If an OLA cannot answer a question directly, they can direct students to the right support team.

We have students across Australia, and we know different students have different circumstances. We do our best to make sure they are looked after throughout the course.

Maggie Hill: Thank you so much. I really appreciate you talking us through the different supports available and giving us a clear understanding of what online learning looks like for these courses. Thanks, Waikay.

Waikay Lau: You’re welcome.

Question and answer session

Maggie Hill: We’ve now reached the Q&A portion of the webinar. If you have questions that haven’t been answered by the presenters this evening, feel free to pop them in the chat and we’ll answer them for you.

The first question: Is the degree primarily aimed at producing AI engineers or developers, or does it also suit leaders focused on AI strategy and business outcomes? Cliff, could you answer that?

Cliff Harris: Absolutely. There is definitely a managerial and strategic aspect to it. The course starts with programming and the fundamentals of how AI is built, then moves into machine learning and advanced core units.

If you’re leading a team of people working in AI, there are design and IT systems concepts that help you understand what the technical team is doing.

Toward the end of the program, students also work on industry projects designed with industry partners. These projects can include project management, team leadership and technical design components.

There are components in the program that suit someone who wants to manage a team. It gives you a strong technical understanding of what is happening as well.

Course length and study load

Maggie Hill: We’ve just had a question about the length of both courses. The Master’s is two years full-time, or part-time equivalent.

The Graduate Diploma is one year full-time, or part-time equivalent.

Technical skills and job readiness

Maggie Hill: The next question is whether the program gives students the technical skills to produce AI solutions with tools available on the market.

Will students be job ready, or is the course more focused on the strategic and management components of AI?

Cliff Harris: From what I’ve seen, students moving through the course are approaching job readiness.

Some short courses outside the university sector may teach a specific tool.

In this program, we teach the underlying foundations of how to build AI infrastructure, train models and adapt those models to the requirements of a business.

Suitability for students with an IT background

Maggie Hill: We have quite a few questions coming through. The next one is whether these courses are suited to someone with an IT background. Cliff, is there anything you would add?

Cliff Harris: Absolutely. It depends on where you come from, but I see many students with IT backgrounds come through the program.

Some are systems engineers or systems administrators who want to upskill and build knowledge in artificial intelligence.

The program includes design aspects, software lifecycle management and the current ways of thinking about AI and IT systems.

Students may move from a network background into programming and AI, building on the skills they already have.

I’ve also seen programmers come through who want to develop more knowledge in security, computer systems and design.

Building on existing AI project experience

Maggie Hill: Building on that, would the course be suitable for someone already working in IT and on projects that use AI components?

Cliff Harris: Definitely. If you’re already working with AI, you may have insight into what a team is doing.

The course helps build knowledge across the programming aspects, managing and building local large language models, cybersecurity, governance and human-centred design.

Adding extra units during a teaching period

Maggie Hill: This next question is for Waikay. For part-time students, can they add more units during the teaching period, particularly if they’re doing the Graduate Diploma?

Waikay Lau: At QUT Online, a full-time load is two units per teaching period.

With four teaching periods in a year, students can complete up to eight units per year on a full-time load.

That structure is designed around course progression, including prerequisite units that need to be completed before students can move on to the next stage.

A part-time load is one unit per teaching period, while a full-time load is two units.

There may be exceptions where a student requests three units in a teaching period, but that is rare because of the progression built into the course.

Balancing work and part-time study

Maggie Hill: Waikay, do you have any practical tips for balancing full-time work with part-time study so students do not fall behind?

Waikay Lau: Each teaching period has eight weeks of learning. The best advice I can give is do not move too far ahead, and do not fall behind.

If it is week one, focus on understanding the week one material early in the week.

That gives you time to interact with the discussion board, join the collaboration session and build a full understanding of that week’s knowledge before moving on to week two.

This also gives you time to ask questions on the discussion board, listen to what other students are saying and learn through those interactions.

Depending on your work schedule, family and life commitments, set aside dedicated time each week for study.

Treat it like a job by setting dedicated study hours. For example, you might study from 7pm to 9pm after dinner, when things are quieter.

If you study for two hours on Monday, Tuesday and Wednesday, you can usually complete the module and then use the rest of the week to ask questions and engage with the discussion board.

Reading other students’ posts can help you think about things you may not have considered.

If you have a specific question and feel too shy to ask publicly, you can email the OLA or join the collaboration session and ask there.

That approach gives you more flexibility than simply reading content and completing activities on your own.

That would be my best suggestion for tackling online study.

Coding languages and practical skills

Maggie Hill: The next question is probably best for Cliff. What coding languages are used in the diploma, and how do the theoretical components complement the practical skills?

Cliff Harris: Python is the main language we use.

Python is widely used across cybersecurity, data analytics and AI. It also has strong libraries, including TensorFlow.

If you don’t know Python, it is relatively straightforward to pick up. With a few weeks of work, you can start to understand the basics and core concepts.

We also understand that some students may not already have programming knowledge, so we provide materials within the unit to help them build those skills as they progress.

In terms of theory and practice, there is always theory behind why certain things are done, but we do not dwell on theory for its own sake.

These are real-world, practical units, so we try to make them as hands-on as possible.

At the same time, we explain why certain things are done so students understand the reasoning behind the practical work.

With theory, you can go down a lot of rabbit holes. We aim to provide enough theory to ground the practical skills students are developing.

Keeping pace with changes in AI

Maggie Hill: Another question: A lot of people say the pace of AI is moving so fast that study can quickly become outdated. What do you have to say about that view?

Cliff Harris: There’s no denying that AI is moving quickly.

The way we’ve designed the course is not about teaching one specific tool. It is about teaching foundations, programming and core concepts that students can continue to build on.

The industry will continue to move at a rapid pace, but the foundations taught in the course are designed to remain useful into the future.

Next steps and closing remarks

Maggie Hill: We have answered the majority of the questions now. Some touch on similar areas, so if we have not answered yours directly, I hope it has been covered through one of Cliff or Waikay’s responses.

In terms of next steps, if you are interested in studying this course, we recommend heading to online.qut.edu.au, where you can download a course brochure.

You can also reach out to the QUT Online team on 1300 104 196 during business hours if you have specific questions about the course or practical details that were not covered in this session.

I’ve found this session really informative and interesting, and I want to say a big thank you to Cliff and Waikay for giving up their time on a Monday evening to share these insights.

Thank you to both of you, and thank you to everyone who joined us this evening. We really appreciate your interest.

We hope to see you studying online in the not-too-distant future.

As mentioned earlier, we’ll send through the link to this recording in case there is anything you would like to revisit.

Thanks very much, everyone, and have a fantastic Monday evening.

Waikay Lau: Thank you, everyone. Thank you, Maggie.

Cliff Harris: Thanks, Maggie. Thank you.

 

This transcript is based on a recorded webinar and has been lightly edited for readability, clarity and conciseness. We’ve removed filler words, false starts, and repeated phrases (such as “um” and “ah”) without changing the meaning. All information remains accurate to the recording.