Using AI tools at university

Play

Play

View Transcript

This video will introduce you to the basics of artificial intelligence.

Have you ever wondered what AI is all about?

This video will give you an understanding of the fascinating world of Artificial Intelligence and why, as a university student, it's worth your attention.

Artificial Intelligence, or AI, is a revolutionary field of science that aims to create intelligent machines that can learn and perform tasks on par with humans.

But AI isn't just one thing.

In fact, there are three main types of AI. They are ANI, AGI, and ASI. Only one type of AI, ANI, has been achieved to date.

ANI or Artificial Narrow Intelligence refers to AI systems that perform a specific task remarkably well but lack the ability to succeed outside their domain. This type of AI is known as ‘weak AI’ and includes voice assistants, image recognition, and content generators.

ANI is prevalent in our daily lives and can be found in various applications we interact with regularly. If you’ve ever asked Siri a question, unlocked your phone with your face, or used Chatbots, you’ve experienced ANI.

Let's move on to Artificial General Intelligence or AGI. This is the next level of AI, wherein machines possess an intelligence akin to humans. They would be able to perform broad tasks beyond their domain and exhibit reasoning, creativity, and problem-solving abilities. This type of AI is still only a possibility.

Lastly, there's ASI or Artificial Super Intelligence. ASI would possess abilities far beyond human capacity and could solve problems a human would never understand.

Due to these immense capabilities, it is theorised that ASI would be capable of either securing or threatening the future of humanity.

However, at this time, ASI is only a hypothetical future stage of AI development.

This video provided a brief introduction to the fascinating world of AI. As students, you are most likely to use GenAI, a type of Artificial Narrow Intelligence that uses prompts to generate output.

If you’d like to know more, continue working through the rest of this page to learn how GenAI works and how you can use it responsibly and effectively. And don’t forget to speak to a Language and Learning advisor if you need more help.

Now that you know more about the types of AI, try the task below. Match the type of AI with the answer that best describes it.

Now we have explored the different types of AI, let's focus on Generative AI. This branch of artificial intelligence is becoming increasingly popular with university students because it can generate content such as text, images, and audio.

Watch the video below to find out more about GenAI.

Play

Play

View Transcript

This video will explore using GenAI tools at university.

Generative AI or GenAI is a type of artificial intelligence that focuses on the creation and generation of new content.

With GenAI the possibilities are endless. This is a constantly evolving and expanding field, with new technologies appearing every day.

GenAI tools such as ChatGPT, Midjourney and AIVA can be used to write text, design visual works, and produce music.

If you want to use GenAI output in your studies, it is important you behave with academic integrity. This includes evaluating and referencing all your information and sources.

Remember, GenAI is still only ANI, or weak AI. Gen AI models are trained using large amounts of data, and so they’re limited by the size and quality of the data they learnt from.

For example, many generative text tools are based on Natural Language Processing models. These models write output by predicting what word is most likely to come next and creating patterns. These tools, like all GenAI tools, are not capable of reasoning or critical thinking.

Output from GenAI is often biased, inaccurate, or has limited understanding. In fact, if GenAI tools such as ChatGPT don’t know the answer, they’ll just make one up. This is known as an ‘AI hallucination’ and it can be a serious issue when you are trying to find accurate information.

So how do we avoid falling for AI-generated ‘fake news’? There is no easy answer, but developing critical thinking skills is essential if you want to be able to evaluate the information you receive.

As university students, you should always analyse information, verify sources, and be critical in your research.

Ask yourself, next time you’re using one of these tools, do you trust the information it’s giving you? Because in the end, GenAI is just machine learning, and it is your responsibility to use it the right way, as a tool, not a shortcut.

This video introduced you to the basics of GenAI. If you want to know more about how to use it responsibly and effectively in your studies, keep working through this page. And don’t forget to speak to a Language and Learning advisor if you need more help.

To check your understanding of GenAI, try the activity below.

When using GenAI, you need to know about ethical risks and work towards responsible use. Relying on GenAI models weakens your ability to develop critical thinking and research skills. It is also important to recognise that using GenAI for research or study at a university has ethical risks. 

Familiarise yourself with some of the key ethical concerns below: 

Now that you know some of the ethical concerns to consider when using GenAI, complete the activity by matching each scenario to an ethical concern.

The rules are simple. Turn cards over to find matching pairs.

How did you do? If you want another chance, reset to see if you can beat your time. 

Using GenAI responsibly means knowing when you should and shouldn’t use it. If using GenAI tools prevents you from developing key skills, you need to reconsider how you use these tools.

Read the hotspots below to find out more about developing key skills.

If you rely on GenAI tools, you may never learn how to study and engage with learning on your own.

Try the activity below to see if you can pick out the dos and don’ts of using GenAI responsibly.

Reflect on your use of GenAI in the past and consider how you are going to use it in the future. Ask yourself: “By using GenAI, am I missing the opportunity to learn a key skill?”

All AI systems, including GenAI, have limitations. Knowing a tool's strengths and weaknesses allows you to evaluate the output more thoroughly. 

While GenAI output may look impressive, it is vital to remember these tools do not possess genuine reasoning or comprehension skills. GenAI tools generate responses based on the most likely interpretation of a question. 

Consider the following: 

• How was the tool trained, and when did the training end? 

• Does the tool take steps to be transparent and accountable? 

• What challenges does the tool face when trying to provide specific information? 

• Does the tool handle ambiguity or uncertainty well? 

• How do these limitations affect the accuracy and reliability of the output? 

GenAI systems reflect the biases present in training data. Being vigilant and identifying potential biases is a key part of critically evaluating GenAI output. 

When using GenAI you need to be able to identify whether output is balanced and inclusive. 

Consider the following: 

• Can you see any signs of bias in the output? For example: Is there a gender bias? Does the tool assign certain traits to either gender, i.e., assumes doctors are male, and nurses are female?

• What information does the tool provide about its training data? 

• How does the tool handle sensitive or controversial topics? 

• How does it handle language-specific translations, slang, and cultural references? 

• What steps has the tool taken to address potential bias and does it provide disclaimers? 

Contextual analysis is a key step in the critical evaluation of GenAI output. You need to be able to evaluate how well the GenAI tool has understood your question and decide whether the output is useful for your studies. 

Consider the following: 

• Does the output address the main points of your question? 

• Does the output contain specific and relevant information, or is it general? 

• Has the tool presented the information in a structured and coherent manner?

• Can you identify any missing information or gaps in the output? 

• Has the tool misinterpreted or misunderstood the question?

  For example, a GenAI tool was asked about Sorry Business, an Indigenous term that describes the mourning period after a family member dies. The output from this tool talked about ‘saying sorry’ and apologising. The tool was unable to understand the cultural context of the question and generated output based on its best guess.

To verify if the information is correct, you need to be able to cross-check output using reliable sources. Verifying and cross-referencing will help you understand the material better and ensure you avoid inaccurate or biased information. By verifying your information and cross-checking your sources, you behave with academic integrity.

Consider the following: 

• What are the consequences of relying solely on GenAI for information? 

• Are you cross-checking information provided by GenAI with independent sources? 

• If you found biased or false information, did you look for different viewpoints and resources? 

• What are some reliable sources, such as academic journals, textbooks, and authoritative websites, you can use to check your information? 

• How are you making sure information from reliable sources matches the output from GenAI?

• If the information from GenAI does not match reliable source(s), how are you going to fix the issue? 

Writing an effective input or prompt will help you get the best results from GenAI tools. A prompt guides the AI tool and can change the type and quality of content a tool generates.

Being able to write effective prompts can take a lot of practice. To get accurate and useful outputs, it helps to understand how GenAI tools use your prompts to create content.

When writing a prompt, you first need to consider the type of tool and how it works. Different types of tools have different capabilities and need different prompts. You wouldn’t ask text GenAI tools to draw a picture or audio GenAI to write a paragraph.

Understanding how a GenAI tool processes information can help you write a prompt that works for that type of model. Certain tools will respond better to different prompt types.

Learn about the different prompt types in the hotspot below.

Now that you know a bit more about the types of prompts, can you find all of them in the activity below?

When writing prompts for GenAI, think of yourself as a programmer. When you create a prompt, you are ‘programming’ the machine to perform a task.

You need to guide the tool to generate the output you want. The more specific you can make your prompt, the more useful your output will be.

Instead of asking a general question, provide details and context. Include background information and examples to help guide the GenAI output if you can

Think about a recent assignment you have done or one that you need to do. What sort of information do you want to know? And how can GenAI tools help you find it?

Check out the example below. Consider what makes each prompt weak, average, or strong.

Complete the quiz below to see if you can identify the features of a strong prompt.

Having a strong prompt is key to getting great results. Even though it’s best to start with a strong prompt, it’s okay to work your way up to it.

If you're not familiar with your topic or need ideas, starting with a weak prompt can be helpful. It allows you to gather initial ideas and search terms before focusing your search.

A weak prompt will likely give you broad and general information. You need to refine the information to turn it into a strong prompt. You can adjust your prompts and obtain more specific results by identifying key terms and themes.

So, how can you refine your prompts? Check out the short video below to find out.

Play

Play

View Transcript

This video will show you how to turn your weak GenAI prompts into strong ones.

When you first start looking for information, you may not know much about your topic. Writing a strong prompt can be hard if you don’t know exactly what you’re looking for because the quality of GenAI output depends on how well you can ask your question.

That’s why it’s important to know how to build on your prompts so you can turn a weak prompt into a strong one. Let’s have a look at an example.

This student is looking for information about the causes of the French Revolution. On the screen, you can see the three different prompts the student developed to meet their information needs. One is weak, one is average, and one is strong.

So how do we get from here to here? Let’s have a look.

When we run the weak prompt through ChatGPT, we can see that although we get lots of information in the output, the tool only provides general information with broad topics. It would be very hard to write an essay that covers every point.

However, we can use this weak prompt to our advantage. When you run a search with a weak prompt, it can help to treat it the same as you would a Google search.

The ideas you take from a weak prompt’s output can be used to craft a better prompt. In this case, we can see that the causes of the French Revolution were mainly divided into social, economic, and political factors.

Let’s focus on that, and we can run the average prompt.

You can see that the content is more focused. It divides output into the three different factors and gives background information for topics within each type. However, it is still an unspecific prompt, and the information is quite generic.

We can now mine the output as we did for the weak search and use the information to create a strong prompt.
Have a read through the output and see if you can’t find some keywords or specific topics you want to focus on.

For example, in this output, the GenAI tool mentioned the Enlightenment ideas, the financial crisis, and the Estates system as factors.

Using these specific topics, we can now create a strong prompt. Let’s run this prompt and see what kind of output we now receive.

You can see from this example that the output the tool generates is now specific and focused on these three topics. It provides more details and information about these subjects, which you can then use to research further using credible resources.

It’s as simple as that. Using the output GenAI tools generate from weak and average prompts; you can begin building a strong prompt that will provide useful and specific output.

It’s important that you remember that GenAI output is NOT a finished product you can submit.

Try and use the output as a starting point for your own research.

Find keywords and topics relevant to your assignment and research them further using credible resources.

Do not copy, paste, and submit any GenAI content as if it was your own work. This is academic misconduct and can have serious consequences.

Remember to show academic integrity in your research. Any information, ideas, or content that GenAI tools generate should be analysed, verified, and referenced.

If you want to learn more about prompts and responsible use of GenAI, work through the rest of this page. And remember to reach out and speak to a Language and Learning advisor if you need more help.

If you use AI tools in your assignments, you must cite and reference all output where it appears.

Referencing is a key part of behaving with academic integrity at university.

Any ideas, information, or content that you did not create yourself must be cited and referenced correctly to acknowledge the original source.

Acceptable use and permissions may change depending on your unit, course, or faculty. Please confirm what is permitted with your lecturer or unit coordinator to make sure you are behaving with academic integrity.

Check out these GenAI reference examples:

CDU APA Example:

OpenAI. (2023). ChatGPT (version GPT-3.5). [Large language model]. https://openai.com/blog/chatgpt/

See the CDU APA Referencing Guide for more information.

CDU Harvard Example:

OpenAI 2023, ChatGPT (version GPT-3.5), large language model, https://openai.com/blog/chatgpt/

See the CDU Harvard Referencing Guide for more information.

Note: These examples may not reflect the required format for your unit or course. Check with your unit content, course outline or lecturer to make sure you are using the correct referencing and acknowledgement format.

You could be committing plagiarism if you don’t reference GenAI use and output. This misconduct can have serious consequences for you and your academic future.

Have a look at the Academic Integrity page of the Student Code of Conduct Site. This page will walk you through the different types of academic breaches and the consequences of academic misconduct at university.

You might also be interested in the Academic Integrity Study Skills page, which provides explanations and tips to avoid academic misconduct.

Most universities align with national guidelines for academic integrity at tertiary (university) institutions. Check out these Tertiary Education Quality and Standards Agency slides or visit their website for more information about behaving with academic integrity.

Chan, K. (2023, June 14). How Europe is leading the world in the push to regulate artificial intelligence. Time. https://time.com/6287116/european-union-artificial-intelligence-law/

Dianati, S., & Laudari, S. (2023, August 7). An introduction to prompting generative AI like ChatGPT for teaching and learning. Times Higher Education.https://www.timeshighereducation.com/campus/introduction-prompting-generative-ai-chatgpt-teaching-and-learning

GPT-3. (2020, September 8). A robot wrote this entire article. Are you scared yet, human? The Guardian. https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3

Skeat, J., & Ziebell, N. (2023, June 23). ‘A study buddy’ that raises ‘serious questions’: How uni students approached AI in their first semester with ChatGPT. The Conversation. https://theconversation.com/a-study-buddy-that-raises-serious-questions-how-uni-students-approached-ai-in-their-first-semester-with-chatgpt-207915

Mahari, R., Fjeld, J., & Epstein, Z. (2023, June 16). Generative AI is a minefield for copyright law. The Conversation. https://theconversation.com/generative-ai-is-a-minefield-for-copyright-law-207473

McKnight, L. (2022, October 14). Eight ways to engage with AI writers in higher education. Times Higher Education. https://www.timeshighereducation.com/campus/eight-ways-engage-ai-writers-higher-education

Nichols, M. (2023. July 19). UN Security Council meets for first time on AI risks. Reuters. https://www.reuters.com/technology/un-security-council-meets-first-time-ai-risks-2023-07-18/

Rogers, R. (2023, January 21). How to spot AI-generated art, according to artists. Wired. https://www.wired.com/story/how-to-spot-generative-ai-art-according-to-artists/

Jovanovic, M., & Campbell, M. (2022). Generative artificial intelligence: Trends and prospects. Computer, 55(10), 107–112. https://doi.org/10.1109/MC.2022.3192720

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, Article 100056. https://doi.org/10.1016/j.caeai.2022.100056

Ng, J., Haller, E., & Murray, A. (2022). The ethical chatbot: A viable solution to socio-legal issues. Alternative Law Journal, 47(4), 308–313. https://doi.org/10.1177/1037969X221113598

Popenici, S. (2023). The critique of AI as a foundation for judicious use in higher education. Journal of Applied Learning & Teaching, 6(2). https://doi.org/10.37074/jalt.2023.6.2.4

Mason, J., Lefrere, P., Peoples, B. E., Lee, J., & Shaw, P. (2023). Artificial intelligence and evolution of the virtual university. In M. Sankey, H. Huijser, & R. Fitzgerald (Eds.), Technology-enhanced learning and the virtual university (1 ed., Vol. 1, pp. 1-22). Springer Singapore. https://doi.org/10.1007/978-981-19-9438-8_28-1

Popenici, S. (2022). Artificial intelligence and learning futures: Critical narratives of technology and imagination in higher education (1st ed.). Routledge. https://doi.org/10.4324/9781003266563

Verdegem, P. (Ed.). (2021). AI for Everyone? Critical perspectives. (2021). University of Westminster Press. https://doi.org/10.16997/book55

Voeneky, S. (Ed.). (2022). Cambridge handbook of responsible artificial intelligence: Interdisciplinary perspectives (1st ed.). Cambridge University Press.

ABC News. (2023, March 18). OpenAI CEO, CTO on risks and how AI will reshape society [Video]. YouTube. https://www.youtube.com/watch?v=540vzMlf-54

CBS News. (2023, June 1). The ChatGPT Revolution CBS Reports [Video]. YouTube. https://www.youtube.com/watch?v=tcFAlZIQ3NI

Forbes. (2023, February 17). Generative AI is about to reset everything, and yes it will change your life [Video]. YouTube. https://www.youtube.com/watch?v=WY518YRfs5M

Google Cloud Tech. (2023, May 9). Introduction to generative AI [Video]. YouTube. https://www.youtube.com/watch?v=G2fqAlgmoPo

Harvard Business Review. (2023, August 17). A non-techie’s 10-minute guide to using GenAI [Video]. YouTube. https://www.youtube.com/watch?v=1IHBIijdxY8

The Economist. (2023, March 24). Beyond ChatGPT: What chatbots mean for the future [Video]. YouTube. https://www.youtube.com/watch?v=dctcfxw13AQ

Ted-Ed. (2022, December 7). How will AI change the world? [Video]. YouTube. https://www.youtube.com/watch?v=RzkD_rTEBYs

Now that you know a bit more about the ethical and effective use of GenAI at university, try this branching scenario. Select the choices you would make and read through the different outcomes.

Reflect on your learning.

If you would like more support, visit the Language and Learning Advisors page.