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A free online course explaining the basics of AI technologies.

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In spring 2018, MinnaLearn and the University of Helsinki came together with the aim of helping people to be empowered, not threatened, by artificial intelligence. Together, they built the Elements of AI to teach the basics of AI to people from a wide range of backgrounds.

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A free online introduction to artificial intelligence for non-experts

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Learn more about MinnaLearn's and the University of Helsinki's AI course - no programming or complicated math required.

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AI Colonisation and Mātauranga Sovereignty

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AI Colonisation and Mātauranga Sovereignty

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This korero discusses the positive impacts of generative AI with te reo Māori and future considerations for Māori Peoples with generative AI in our traditional settings such as marae and pōwhiri

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Te Kete o Karaitiana Taiuru (Blog) - Artificial Intelligence

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Te Kete o Karaitiana Taiuru (Blog) - Artificial Intelligence

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Blog of Karaitiana Taiuru exploring topics about Māori AI, Data and Merging Tech Ethicist.

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Artificial Intelligence & Robotics Archives - Taiuru & Associates Ltd

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Māori cultural considerations with AI, Algorithms, Big Data and Robotics are discussed here as well as ethics and treaty of Waitangi values.

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A selected list of text- and image-generating AI tools, ideas for creating prompts, and detectors for AI-created text.

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A selected list of text- and image-generating AI tools, ideas for creating prompts, and detectors for AI-created text.

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Massachusetts Library have selected a list of text- and image-generating AI tools, ideas for creating prompts, and detectors for AI-created text.

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Text-generating AI - General Tools

Text-generating AI - Specialized Tools for Librarians, Educators, and Researchers

Want to discover even more tools built on GPT? Try searching or browsing these compiled lists of tools:

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LibGuides: ChatGPT and Generative Artificial Intelligence: Generative AI Tools, Prompts, and Detectors

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This guide accompanies MLS continuing education opportunities about generative artificial intelligence. Links to some of the most popular generative AI tools, ideas for prompting the tools, and detectors

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How do we protect Māori data in the era of generative AI?

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How do we protect Māori data in the era of generative AI?

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Article about the power of artificial intelligence and the proper use of Māori data to improve wellbeing, productivity and economic performance.

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How do we protect Māori data in the era of generative AI?

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I'm a firm believer in the power of artificial intelligence to improve our wellbeing, productivity and economic performance.

This new wave of generative AI applications is making that potential apparent to people all over the world with large language models (LLMs) underpinning a range of compelling applications that grows by the week.

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How do we protect Māori data in the era of generative AI?

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I'm a firm believer in the power of artificial intelligence to improve our wellbeing, productivity and economic performance.

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Ortiz, S (2023): What is generative AI and why is it so popular? Here's everything you need to know.

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Ortiz, S (2023): What is generative AI and why is it so popular? Here's everything you need to know.

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Generative AI is a hot topic right now, but what actually is it? Here you will find some answers.

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What is generative AI and why is it so popular? Here's everything you need to know

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Generative AI is a hot topic right now, but what actually is it? We have the answers.

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Diaz, M (2023). What is AI? Everything to know about artificial intelligence.

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Diaz, M (2023). What is AI? Everything to know about artificial intelligence.

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This website cover everything from machine learning and general AI to neural networks.

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What is AI? Everything to know about artificial intelligence

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If you want to know about the fascinating and fast-developing technologies of artificial intelligence, we cover everything from machine learning and general AI to neural networks.

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(Tertiary Education Quality and Standards Agency): covering resources and guides on academic integrity, assessment and incorporation of AI into the classroom.

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(Tertiary Education Quality and Standards Agency): covering resources and guides on academic integrity, assessment and incorporation of AI into the classroom.

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This resources by TQSA aim to assist higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI).

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These resources aim to assist higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI). In addition to resources developed by TEQSA, we have also included good practice advice from other sources.

This area is rapidly changing and evolving. If you have developed or found a useful resource you wish to share, please let us know via our webform.

TEQSA resources

Generative artificial intelligence for higher education webinar series

TEQSA, in association with Deakin University, produced this series of webinars which address what is known at present about generative AI, implications for learning, teaching and assessment and how such technologies might transform higher education into the future.

Academic integrity, assessment and AI

Generative AI in research

Incorporating AI in your classes

Engaging with students about AI

International approaches

Understanding generative AI

These resources contain information to assist you with building your understanding about generative AI.
TEQSA makes the information on this webpage available to assist higher education providers, ELICOS providers and foundation program providers in building good practice. It has been obtained from a range of external sources and has not been generated by or on behalf of TEQSA unless otherwise noted. You should read, and carefully consider, the disclaimer before accessing any of the material.

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These resources aim to assist higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI). In addition to resources developed by TEQSA, we have also included good practice advice from other sources. This area is rapidly changing and evolving.

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This course by Google teaches you about what Artificial Intelligence is. You’ll cut through the hype and learn about AI and Machine Learning.

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A course for anyone to learn what AI is and how it works.

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Introduction to Generative AI Learning Path by Google

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Introduction to Generative AI Learning Path by Google

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This learning path by Google provides an overview of generative AI concepts, from the fundamentals of large language models to responsible AI principles.

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01

Introduction to Generative AI

This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI...

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Qwiklabs provides real Google Cloud environments that help developers and IT professionals learn cloud platforms and software, such as Firebase, Kubernetes and more.

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Introduction to Machine Learning by Google and London School of Economics and Political Science

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Introduction to Machine Learning by Google and London School of Economics and Political Science

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With help from the London School of Economics and Political Science, VRT News and Texty our Journalism AI courses offer an insight on how journalists are using machine learning in their newsrooms.

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Introduction to Machine Learning - Google News Initiative

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With help from the London School of Economics and Political Science, VRT News and Texty our Journalism AI courses offer an insight on how journalists are using machine learning in their newsrooms.

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Jiahong Su, Davy Tsz Kit Ng, Samuel Kai Wah Chu (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities 

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Jiahong Su, Davy Tsz Kit Ng, Samuel Kai Wah Chu (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities 

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This article explores the challenges and opportunities of AI Literacy in Early Childhood Education.

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Samia Kaz (2023) AI In Early Childhood – Six Things Teachers Need to Know

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Samia Kaz (2023) AI In Early Childhood – Six Things Teachers Need to Know

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This document approaches some considerations future teachers need to keep in mind in terms of the impact of AI in early childhood education.

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We are committed to operating in line with Māori Data Sovereignty principles.
01 Rangatiratanga | Authority
Currently practised by the fact we are migrating all Māori data and communications tools back to New Zealand. Any Māori Data we have is either licenced using a Kaitiakitanga licence or Creative Commons Licence.
02 Whakapapa | Identity
Currently practised by ensuring all Māori Data whakapapa is recorded and publicly noted.
03 Whanaungatanga | Obligations
Currently practised via our licensing schemes and by not using any Māori Data that could be used against Māori, whānau, hapū, marae, hāpori or Iwi.
04 Kotahitanga | Collective benefit
Currently practised by recognising the many collective beliefs and identities within the Māori world and by ensuring all Māori Data can be accessed and used by Māori.
05 Manaakitanga | Reciprocity
Currently practised by ensuring all Māori Data is obtained and used with Free, prior and informed consent (FPIC) and any Māori Data is created so that its use and interpretation of data shall uphold the dignity of Māori and empower Māori.
06 Kaitiakitanga | Guardianship
Currently practised by ensuring tikanga and mātauranga values are applied at all stages of the Māori Data cycle. Te Tiriti (including WAI 262 and WAI 2522), He Whakaputanga and The United Nations Declaration on the Rights of Indigenous Peoples underpin the protection, access and use of Māori Data on this site.

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Māori Data Sovereignty Statement - Taiuru & Associates Ltd

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Dr Karaitiana Taiuru is an Māori AI, Data and Merging Tech ethicist. A Kaupapa Māori Researcher and Data Sovereignty expert.

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Unesco Statement - Artificial intelligence in education

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Unesco Statement - Artificial intelligence in education

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This resource by Australian National University includes best practices in citing generative AI, privacy issues and the ANU approach to Turnitin

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Referencing generative AI (content/image)  

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Referencing generative AI (content/image)  

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Referencing style - APA 7th: Generative Artificial Intelligence (AI)

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Referencing style - APA 7th: Generative Artificial Intelligence (AI)

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You will need to confirm with your Unit Coordinator whether you are permitted to use Generative Artificial Intelligence (AI) such as ChatGPT in your assessments. Please check your unit outline to confirm if there are any specific guidelines for referencing AI for your unit. If it is used ALL the content that is generated must be referenced.

APA has provided guidelines for referencing generative AI content in APA:

...as generated by the generative AI program (OpenAI, 2023)

According to OpenAI (2023) ChatGPT states that the
relationship between language and psychology...

OpenAI. (2023). ChatGTP (Mar 14 version) [Large language model].
https:chat.openai.com/chat

Please note: The information in this guide is subject to change as guidance on referencing AI tools continues to evolve.

See the APA Style Blog for more information and examples.

See the APA 7 Publication manual for more information on citing Computer Software, Mobile Apps, Apparatuses, and Equipment.

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Library Guides: Referencing style - APA 7th: Generative Artificial Intelligence (AI)

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A guide to using the APA 7th edition referencing system for in-text citations and reference lists.

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Generative AI: Referencing and Acknowledging AI

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Generative AI: Referencing and Acknowledging AI

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Check with your tutor to ensure using Generative AI (GenAI) content in assignments is acceptable.
When referencing GenAI in AGLC style:

Use the Written correspondence format that can be found in section 7.12 of the AGLC4 guide (linked below)

Discursive text may also be used in the footnote to provide information about the prompts used to generate the output.

An appendix may be used in order to provide comprehensive information about the series of prompts and outputs used to generate the output (as in the second example below).

Footnote examples

¹Output from ChatGPT, OpenAI to John Smith, 23 February 2023.

²Output from ChatGPT, OpenAI to John Smith, 23 February 2023. The output was generated in response to the prompt, 'Provide an overview of the creation of the Australian Guide to Legal Citation': see below Appendix A.

Bibliography example

Place under the Other category in your bibliography.

OpenAI, ChatGPT to John Smith, Output, 23 February 2023

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Study Guides: Generative AI: Referencing and Acknowledging AI

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Study Guides: Generative AI: Referencing and Acknowledging AI

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Generative AI for teachers and school leaders

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Generative AI for teachers and school leaders

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This page by Te Tāhuhu o Te Mātauranga provides guidance to support teachers and school leaders with these new tools.

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Artificial Intelligence (AI) is likely to have an increasing impact on education systems over the coming years. Te Tāhuhu o Te Mātauranga is developing policies and advice about this to ensure the system is well prepared. In the interim, here are some initial points that schools and teachers should be aware of.

What is ChatGPT?

ChatGPT has been all over the media in recent times. So what exactly is it?

ChatGPT is a type of artificial intelligence called 'generative AI'.

Its creators, OpenAI, use very large mathematical models to process vast amounts of text that is available on the internet so it can provide responses to prompts in any form, such as answers to questions, essays, poems, or even lines of code. ChatGPT creates text that appears useful and accurate. The complexity of the model is why the same prompt can get slightly different results.

There are other free to use large language models like ChatGPT, such as Chatsonic, Youchat, BingAI, as well as paid versions, and more are being developed. Other AIs generate images, like Midjourney and Fotor. Some AI will be embedded in software that we use regularly, like search and word-processing tools.

Concerns have been raised both within Aotearoa and internationally about the implications of large language models and image generation AI for education, cyber security, and data-privacy. ChatGPT is being reviewed by European and Canadian privacy regulators.

An emerging concern is that the use of data from the internet to train generative AI systems may breach copyright laws.

Using Generative AI in school

Many educators are already using AI tools effectively to support teaching and learning, and we will be sharing examples of these as we learn more.

Some examples are in links on this page. PLD providers and tertiary institutions are offering short courses and learning opportunities for teachers about genAI tools, both general introductions and how to use them for specific learning areas or approaches.

Considering the issues below will mean that you will be keeping yourself and your ākonga safe as you develop expertise with these exciting new tools.

Be aware before you use

AI tools provide great opportunities for teaching and learning, but educators need to be aware of the limitations and risks before they jump in.

These tools are still experimental and the risks and potential harms of these are still not well understood. New Zealand's Office of the Privacy Commissioner has released expectations for agencies about using generative AI, which principals and teachers should be mindful of.
Office of the Privacy Commissioner - Generative Artificial Intelligence(external link)
It is worth being aware of the guidance to the New Zealand public service and considering it when making decisions about genAI in your school.
Understanding the benefits of GenAI to the public service - NZ Digital(external link)
As noted above, there are concerns about genAI and intellectual property. The onus is on you to ensure any generated content you may use does not infringe on other's intellectual property or copyright law.

Lack of knowledge of non-dominant languages and contexts

Generative AIs like ChatGPT have been trained on content that can be freely accessed on the internet, and most of this content reflects contemporary, dominant cultures and languages.

This means that the tools may not be able to provide results that reflect indigenous knowledge, and in the content of Aotearoa New Zealand, will likely be weak on Mātauranga and Te Reo Māori.

The tool may produce results with errors and biases which can reinforce existing inequities.
Speaking my indigenous language with new AI - Te Ao Māori News (external link) Māori culture and language observations with ChatGPT - Dr Karaitiana Taiuru PhD, JP, ACG, MInstD, RSNZ (external link)
Age restrictions on use

As with any digital tool, teachers must abide by the terms and conditions of the AI tools they want to use with their learners.

For instance, to use ChatGPT you must be at least 13 years old, and if you are under 18 you must have your parent or legal guardian's permission. The terms of use for ChatGPT and any services using its API are clear that you must not send it any personal information of anyone under the age of 13.

Requiring ākonga to use ChatGPT or another AI tool must be consistent with the terms and conditions of the tool.

Avoid using personal data in genAI tools

Our advice is that the safest option for now is not to input or use personal data in genAI tools.

There is a risk that personal information entered into genAI tools is retained or disclosed by the genAI provider. The responsibility of complying with the Privacy Act 2020 lies with schools.

Large language models like ChatGPT are unreliable

Large language models compile text convincingly, but they do not understand what they have created.

They can invent facts and details, because services like ChatGPT are designed to create answers that are grammatically correct compilations of text, based on predicting what word should come next in a sentence.

OpenAI, the developers of ChatGPT, caution that ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers and that fixing this issue is challenging.

ChatGPT 4 responses are generated based on a pre-existing dataset of information and patterns from a variety of sources available up until September 2021. ChatGPT doesn't have real-time internet access or the ability to browse the web. However, the new subscription-based ChatGPT Plus integrates with Microsoft's Bing search engine, which allows ChatGPT to provide real-time information to users.
Introducing ChatGPT - OpenAI (external link)
Don't use AI to make decisions about a learners' work

Using ChatGPT or similar tools for marking work can look promising, but teachers should be cautious of this.

Without understanding the basis for the judgements (i.e. seeing inside the algorithm), this can be unfair and discriminatory. It is the responsibility of teachers and the school to make final decisions on learners' work. Also, because of the way these tools 'invent' answers, sometimes they can be simply wrong.

AI systems trained off the internet have not seen enough work by children or young people to have a good understanding of what is appropriate for or expected of young people.

NCEA and ChatGPT

NZQA have provided advice specifically about reducing the risk of cheating in NCEA using ChatGPT or other AI tools.

Using AI checkers for assessments

As noted above, teachers should not rely on AI tools to make decisions about student work.

There are many AI detecting or checking tools available, some of which are marketed heavily to teachers. These may have some value, but anyone using them needs to be aware of the risks and caveats.

AI plagiarism checkers are not fool proof. AI detection tools will struggle to keep up with the ability of AI to draft text in the style of a student. AI detection tools can also generate false positives, which means they identify something as AI generated when it is not.

False positives and true positives cannot be told apart without using a different method, such as discussions with the student. This means you should not rely on AI based detection tools to judge whether a student has breached assessment conditions.

Teachers are best placed to make judgments about the authenticity of ākonga's work and should employ a range of strategies to support such judgements. School assessment policies should be clear about these strategies and the processes to follow if a teacher suspects unauthorised use of generative AI.
Understanding false positives within our AI writing detection capabilities - Turnitin(external link) A Hitch in Accurate Detection of AI-Written Content - Psychology Today NZ(external link) Turnitin says its AI cheating detector isn't always reliable - The Washington Post(external link)
So how might we use AIs like ChatGPT in schools?

As we have seen, there are opportunities and risks in harnessing AI like ChatGPT in the classroom.

However, through deliberate and discerning use by teachers there are opportunities to teach learners critical literacy, particularly supporting ākonga to question the accuracy of what they read as well as recognising bias. The curriculum is clear about the importance of critical literacy learning, including being literate in a digital space.

Although children and young people may not be able to access ChatGPT themselves, teachers can generate texts and use these with ākonga to support the development of their critical literacy skills. Teachers could also use a series of texts to support understanding of the effective use of prompts.

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ChatGPT and other generative AI tools: things to consider if you're thinking of using them at school.

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Free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI

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Artificial intelligence is already a part of our daily lives. When we post pictures to social media, search online or ask questions from chatbots, we're interacting with AI. Authorities, such as cities, rely on AI for providing public services. And governments are seeking solutions to global problems by using algorithmically produced knowledge.

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Chat GPT, What It’s Good for and What It’s NOT Good for.

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Post by Hannah Rozear, Librarian for Biological Sciences and Global Health, and Sarah Park, Librarian for Engineering and Computer Science
Unless you've been living under a rock, you've heard the buzz about ChatGPT. It can write papers! Debug code! Do your laundry! Create websites from thin air! While it is an exciting tech development with enormous possibilities for applications, understanding what's under the hood and what it does well/not-so-well is critically important.

is an Artificial Intelligence Chatbot developed by OpenAI and launched for public use in November 2022. While other AI chatbots are also in development by tech giants such as Google, Apple, and Microsoft, OpenAI's early rollout has eclipsed the others for now - with the site reaching more than 100 million users in 2 months. For some perspective, this is faster widespread adoption than TikTok, Instagram, and many other popular apps.

What you may know about ChatGPT is that it has significant limitations as a reliable research assistant. One such limitation is that it has been known to fabricate or "hallucinate" (in machine learning terms) citations. These citations may sound legitimate and scholarly, but they are not real. It is important to note that AI can confidently generate responses without backing data much like a person under the influence of hallucinations can speak confidently without proper reasoning. If you try to find these sources through Google or the library-you will turn up NOTHING.

Why does it do this? ChatGPT is built on a Large Language Model and has been trained on a huge dataset of internet sources. It can quickly and simply generate easy-to-understand responses to any question you throw at it. But the responses are only as good as the quality of input data it has been trained on. Its core strength lies in recognizing language patterns- in reading and analyzing lengthy scholarly texts. Given that, it may not be the most reliable source for in-depth research. The following is a shortlist of what we've observed ChatGPT is good for and not good for.

What It's Good For

Generating ideas for related concepts, terms, and words about a particular topic. I asked ChatGPT, what are some keywords for the topic of AI literacy? It replied with: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Neural Networks, Natural Language Processing (NLP), Robotics, Data Science, Big Data, Predictive Analytics, Ethics of AI, Bias in AI, Explainable AI, Human-AI Interaction, Cognitive Computing... These are all great leads for terms I might use to look for articles and books on this topic.

Suggestions for databases where I could find literature on the topic. I asked ChatGPT, What are some good library databases I could search to find more information about the topic of AI literacy? ChatGPT replied with: IEEE Xplore, ACM Digital Library, ScienceDirect, JSTOR, Proquest, arXiv, and Web of Science. It also suggested checking with my library to see what's available. A more direct route to this type of question would be consulting the Duke Libraries Research Guides and/or connecting with the Subject Specialist at Duke who is familiar with the resources we have available on any given topic.

Suggestions for improving writing. As ChatGPT has been trained on a large corpus of text, it has accumulated a range of dictions and writing variations within context. I have found it particularly useful for checking grammar and sentence structure in American English, as well as for suggesting alternative phrasing, synonyms, or quick translations of my writing into another language. Additionally, I have experimented with asking ChatGPT to rewrite my paragraph, but if it produced an unexpected response, it may indicate that my writing contains parts that do not make sense in that particular language. Nonetheless, it is important to thoroughly review the text and ensure that it meets your criteria before taking it.

What It's NOT Good For

DO NOT ask ChatGPT for a list of sources on a particular topic! ChatGPT is based on a Large Language Model and does not have the ability to match relevant sources to any given topic. It may do OK with some topics or sources, but it may also fabricate sources that don't exist.

Be wary of asking ChatGPT to summarize a particular source, or write your literature review. It may be tempting to ask ChatGPT to summarize the main points of the dense and technical 10-page article you have to read for class, or to write a literature review synthesizing a field of research. Depending on the topic and availability of data it has on that topic, it may summarize the wrong source or provide inaccurate summaries of specific articles-sometimes making up details and conclusions.

ChatGPT's "knowledge" is based on the dataset that was available before September 2021, and therefore, it may not be able to provide up-to-date information on current events or predict the future. For instance, when I asked about the latest book published by Haruki Murakami in the US, ChatGPT responded with Do not expect ChatGPT to know current events or predict the future. First Person Singular, which was published in April 2021. However, the correct answer is Novelist as a Vocation, which was released in November 2022. Additionally, ChatGPT did not seem aware of any recent developments beyond September 2021. It's worth noting that Murakami's new novel is expected to be released in April 2023.

AI chat technology is rapidly evolving and it's exciting to see where this will go. Much like Google and Wikipedia helped accelerate our access to information in their heyday, the existence of these new AI-based tools requires their users to think about how to carefully and ethically incorporate them into their own research and writing. If you have any doubts or questions, ask real human experts, such as the library's Ask a Librarian chat, or schedule a one-on-one consultation with a librarian for help.

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ChatGPT and Fake Citations - Duke University Libraries Blogs

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Post by Hannah Rozear, Librarian for Biological Sciences and Global Health, and Sarah Park, Librarian for Engineering and Computer Science Unless you've been living under a rock, you've heard the buzz about ChatGPT. It can write papers! Debug code! Do your laundry! Create websites from thin air!

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Massachusetts Library have curated resources for teachers, school librarians and administrators to get started with thinking, talking about, and incorporating AI technology.

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LibGuides: ChatGPT and Generative Artificial Intelligence: AI and Schools

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This guide accompanies MLS continuing education opportunities about generative artificial intelligence. Resources for primary and secondary education professionals

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Website containing demos of different types of AI. Playing around with these tools gives you a good sense of the kind of thing that might be possible.

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What Is Artificial Intelligence?

The intelligence demonstrated by machines and technology, in contrast to intelligence demonstrated by human behaviour. AI is "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation".

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The intelligence demonstrated by machines and technology, in contrast to intelligence demonstrated by human behaviour. AI is “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.

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Artificial intelligence: advice for students

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Artificial intelligence: advice for students

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Guide and list of resource by TEQSA to assist students in understanding how AI works and how to ethically use it while upholding their academic integrity.

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Generative Artificial Intelligence, commonly known as generative AI or AI, is progressing at a rapid rate. This form of technology includes AI chatbots, such as ChatGPT.

It's important to understand that, depending on your university or college's policies, using AI as part of your studies may be restricted or banned.

Alternatively, there might be subjects or tasks where the use of AI is encouraged or even required.

Where use of AI is permitted, you will need to understand how the use of these tools is referenced.

Use of AI in a way that is not consistent with your institution's rules can result in a finding of academic misconduct.

TEQSA encourages all higher education students to discuss expectations around AI with their institution and ensure they follow their university or college's instruction to avoid breaching academic integrity.

Because rules might be different in various disciplines, it's best to make sure you understand the expectations for each assessment task.

Assessments and AI

TEQSA does not mandate a particular form of assessment be used - the Higher Education Standards Framework (Threshold Standards) 2021 requires institutions to assess a student's knowledge and understanding of the subject.

The increasing sophistication of AI might mean that some assessment tasks are redesigned to ensure students cannot substitute student work with material produced by AI.

Redesigning assessments is an appropriate response to the risks posed by AI and is one that TEQSA supports.

You should discuss any concerns you have about how changes to assessment could impact you due to disability or other circumstances with your university or college.

All Australian higher education institutions are required to have policies and procedures to respond to student concerns - you can usually find them on your university or college's website or student portal.

Other AI resources for students

We're sharing these links to assist students in understanding how AI works and how to ethically use it while upholding their academic integrity.

Remember, before following any advice within these links, you should first check with your institution.

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Artificial intelligence: advice for students

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Generative Artificial Intelligence, commonly known as generative AI or AI, is progressing at a rapid rate. This form of technology includes AI chatbots, such as ChatGPT. It's important to understand that, depending on your university or college's policies, using AI as part of your studies may be restricted or banned.

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This research review will describe what is meant by ‘artificial intelligence’, what it does, how it works, and what the evidence says about the implications for education.

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An introduction to the role of Artificial Intelligence in classrooms and schools

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An introduction to the role of Artificial Intelligence in classrooms and schools

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This research review will describe what is meant by ‘artificial intelligence’, what it does, how it works, and what the evidence says about the implications for education.

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In late 2022 and early 2023, a series of articles and features broke across popular media. Artificial Intelligence (AI) had arrived, and everything apparently had changed. This research review will describe what is meant by 'artificial intelligence', what it does, how it works, and what the evidence says about the implications for education.
What is Artificial Intelligence?

Artificial intelligence or AI is a term used in computer science to refer to processing tasks that require considerably more advanced data synthesis and analysis than those used in ordinary computing applications. AI comes in two primary forms, algorithmic and neural networks. Until very recently, consumer facing applications of artificial intelligence have tended to be algorithmic, or rules-based. An algorithm (words in bold are in the glossary at the end of the review) is basically a series of 'if/then' rules, structured for a machine to follow. Simple algorithms in everyday life include things like sewing patterns or recipes. For computers, these rules are sometimes explicitly written by programmers and sometimes the rules are 'learned' by the computer from large data sets. Complex algorithmic systems usually mix these explicit (written) and inferred ('learned') rules.

However, neural network AI systems work differently. These are the kind that have resulted in this most recent surge of new products, such as Chat-GPT for text and DALL-E for images. These systems are modelled on how biological brains work. Rather than being given a set of explicit rules, these systems are 'taught' by looking at the patterns in relationships between things, with classification guidance provided by programmers. These systems require massive amounts of training data, on the scale of the whole internet. Posting on social media or using a Captcha contributes training data for an AI product of this type.

Familiar voice assistants, including Siri, Alexa, and Google Assistant, tend to be algorithmic systems. These systems are more rigid and their responses are usually predictable and repetitive. They often fail when they encounter a query they have not been trained to understand. While they are programmed to recognise and imitate natural language, their responses are scripted so people quickly discover the limitations of these systems when using them. These systems are unlikely to be mistaken for anything other than handy (and sometimes frustrating) call-and-response machines. Many of the pop-up chat-bots on the websites of banks and retail shops are of this type too.

Academics and scientists have been experimenting for a while now with a different AI that learns about the connections between 'things'. These are neural net AIs. These systems are not provided with rules for making decisions, but rather 'learn' rules for how to understand connections and basic context from within a set of data. What these machines then understand as the relationship between 'things' determines how they produce an output.

Data, data, data

Neural net AIs require enormous data sets. These data sets include significant proportions of the entire internet, humanity's written words, both conversational and formal, and all the images imaginable. In the latter half of 2022 the 'third generation' of many of these AI experiments were made available online for people to interact with.

The two primary kinds of these third generation AIs that are relevant to this article are both LLMs (Large Language Models), where the 'language' involved is text or images. The outputs of these, again either images or text, are called Generative AI. LLMs can generate conversational language and take in requests from people in natural language at a level never seen before. But more importantly, they can generate content at a quality level that has profoundly surprised many users.

Immediately this raised concerns amongst teachers and educators. First, AI-generated images from the likes of DALL-E and Stable Diffusion shocked people with the ability to create images at a level of quality and accuracy they had not before experienced from short descriptive prompts.

Created by DALL-E in response to the prompt, 'Can you show me a kiwi bird?' Generated on 2 March 2023.

But following along from these, attention quickly shifted to an AI product called Chat-GPT. Chat-GPT can generate text in a huge variety of styles, in different genres, fiction and nonfiction. It can cite, make arguments, connect facts, construct introductions and conclusions, using a variety of tones, from journalistic and conversational to formal and academic. However, first appearances were misleading, as it turned out that the nuance of what Chat-GPT produced lacked rigour. The devil, it turned out, was quite literally in the details. This will be covered in more detail below.

Example from Chat-GPT3, generated on 2 March 2023.

In real life, neutral net AIs also include algorithmic aspects. Developers and researchers have seen the limitations in the current generation of AI and are placing controls on theLLMs through algorithmic moderator bots intended to make these tools safer. This does not include preventing what they are used for, which will be covered more below. In current practice, this can be seen when an AI comes back with a response that is formulaic, simplistic, lacking a conversational nature or vagueness, and resembles something out of a smart-speaker. These new releases of AI are certainly impressive, but people familiar with them know that they most definitely have limits.

Does this herald the death of the essay?

Whenever a shift in technology occurs there is always considerable hype about the transformative nature of it, although most of this tends to be overblown. There is no evidence that the recent developments in AI will be any different to previous waves of hype. Also, it is important to realise that none of this is especially new. It works like auto-fill search fields and auto-correct. The scale is certainly different, and its abilities have expanded considerably beyond what has been experienced up to now, but importantly this is not a change in kind. Students are already using Grammarly and similar tools, and Chat-GPT is not far removed from this.

This is not to say that there will not be changes as a result. As mentioned above, many of these AI developments are in their third 'generation' or iteration. Some are now even into their fourth. More and more generations of these are expected to be developed, especially given that this technology is starting to move out of the research and academic arena into the private sector. The implication of these iterations is that the flaws that generative AI currently display will become less and less apparent. What we know now to look for in determining whether or not something is AI-generated will no longer be reliable indicators as the systems improve.

Still, what is generated by these AI systems is at a basic level convincing. The outputs sometimes lack depth and expertise, and it is often this lack that indicates that the content may not be quite right. In other words, they have reached being 'good enough'. But what they are 'good enough' for is the fundamental question that teachers and educators need to be asking.

Questions of quality

A primary concern is that AI tools can be wrong, sometimes considerably so. The tone and format of an AI may read like it is sure of the facts and arguments it is making, but the facts and arguments can be quite incorrect, and without any of the signals that humans who are uncertain or confabulating send. This occurs because it is trained on data, and the tool is dependent on that data being correct. If something incorrect is in the data with sufficient regularity, the connections that it picks up will be repeated. Further, many questions asked of these tools are filled with complexity: relationships between relationships. The material used will tend to be relevant, but can be lacking in robustness, or even just explicitly false. Note that this is not what is often called an AI hallucination. An AI hallucination happens when the AI diverges from the data or is unfaithful about it. This is another way that AIs can fail.

Further, what is typed into a prompt can be conflicting for the AI. There are many documented cases of a person asking an AI to generate an essay with references. The AI will do this, but it sometimes creates an entirely invented citation which is convincing, but fake. A student using generative AI to write an essay could easily be fooled, not realising that they may be compromising their academic integrity. Even if the reference actually exists, what the referenced author states may not be what the AI generates, or what the author has stated may not be able to be used to support the claims the AI has generated. These are crucial questions to remember, as they form part of how a response to this technology could be framed.

Moreover, and importantly to this point, the AI doesn't know what is true and what is false. All AI tools are limited to what is in the databases of information they have been trained on. The information they are providing means nothing to them. Meaning doesn't matter, and true and false are beyond them. Similarly, contextual particularities and less common nuances not only do not matter to them, but they are not aware of them. This includes the connections and patterns beyond the basic ones they develop, the ethical or moral concerns about what the information conveys, and questions of import regarding the information.

It has also been noted that the ability of these generative AIs to write essays convincingly does vary depending on the purpose of the essay. On those topics that require higher levels of critical thought, interpretation, and applications of theory for analysis, particularly where context matters, AI tools do not perform as well as topics where these are less central. Overall, though, the essays produced do tend to be 'good enough'. They might result in a C or a B, but lack that excellence that would result in an A. However, a C or B is still a good passing grade, and so, despite the above concerns regarding accuracy, this is something that teachers need to reckon with and develop good strategies and approaches.

What this means for teachers and schools

First, teachers do not need to worry about being replaced with an AI teacher. That scenario is a very long way off[1]. Some institutions have taken the approach of banning student use of these tools. These institutions consider it in the same arena as plagiarism and the passing off of another's work as a student's own. TurnItIn and similar companies have announced that they will be rolling out feature additions to their existing software to detect AI-generated content. How precisely this will be done remains unclear.

While banning the use of generative AI is an option, this could quickly become a moving target as AI keeps improving. Constant policing to detect AI use will be needed in order to keep pace with developments in AI over time, demanding staff resourcing and financial investment. Instead, an approach that recognises that the use of generative AI tools by students is happening[2], and plans for this use, offers a more practical option.

What do we know?

When considering how to move forward, age matters a lot. These tools are not the same as algorithmic driven chatbots and there is strong evidence that they can generate toxic outputs, even with 'mod bots'. Chat-GPT appears to be less likely to produce such outputs than the AI version of Microsoft's Bing, which is still only available to certain users, although Chat-GPT has also had toxicity in its outputs. These models are not controlled in the same way that algorithmic chat-bots are, and they present a different risk profile. While there is a small and growing evidence base around using AI tools in early childhood education[3], this evidence base pertains to algorithmic AI tools, not generative AI[4].

The evidence-base around generative AI tools in education is extremely limited. They are simply so new and are developing so quickly that there has not been enough time for the scholarship around them to develop. The few studies that exist are small, quite narrow, and tend to use specialised deployments that sidestep toxicity issues. They also, of necessity, cover short term projects rather than offering a longer term look at the value of these tools for students and teachers. That said, there is an evidence-base around the use of algorithmic chat-bots[5]. It is not yet known if what we have learned about those chat-bots applies to a product like Chat-GPT.

Supporting students to learn about artificial intelligence is going to require coordinated approaches across the curriculum and year levels. The concept of AI literacy has led to the development of a flurry of scoping work around what this means and how to deliver it within curricula[6]. Singapore, South Korea, Australia, and the EU have begun their work to incorporate teaching about these tools into curricula[7]. Canadian schools are also developing coordinated approaches. New Zealand will need to do the same.

Practical approaches

The first thing to do is to talk to students about these new AI products, their limitations, what they can produce and what they cannot. Students are going to find out, and an approach that relies on them not knowing about it will result in them using it badly. Of course, do so at an age-appropriate level, but let them know about it and what this could mean for them. Demonstrate how they work in class, and discuss limitations and potential uses. A 2021 study which examined the process of developing a curriculum with a learning trajectory for these sorts of AI might provide a sense of how to approach this conversation in a systematic way and help to identify learning objectives. It is also worth being careful around disparate impacts. One study showed that using these tools in a painting class left girls less engaged than boys[8].

If teachers choose to tell students they are allowed to use generative AI tools for their assignments, it is important to tell them how to use it and talk about their limitations. Some of them will be using it anyway, so best to be direct about expectations. There are a few studies that offer guidance on how these tools can be used in the classroom and their utility as a learning tool. For example, researchers explored the question of whether a special implementation of Chat-GPT could be used to encourage students to develop and ask more curious questions. The study involved 9- and 10-year-olds, and used a specialised version of Chat-GPT with strong protections to avoid toxic outputs (it is not currently available for public use). The study found that this version of Chat-GPT was useful helping the students to ask better questions. A recent position paper lays out a range of other uses of this technology for both students and teachers. Again, it is early days, but the range of possibilities is wide.

Rethinking assignments

Given that AI performance varies depending on the subject being taught, it might be good to explore this further. As mentioned, questions that encourage interpretation, critical analysis, contextualisation, and comparative analysis are less vulnerable to AI misuse. When showing students how generative AI tools work in class, try different ways of framing a prompt to show them how to investigate something. Get them thinking about a good prompt and what might go into it. Relate the different prompts to the different parts of the research process, or use the prompts to investigate the different parts of the research question the students need to answer. Particularly to that point, it is important to rethink how research questions are framed. For older students, emphasise research and analysis skills over 'find me the facts around x' essays. If essay framings and use-cases are having to shift, do so in such a way that encourages focusing more on critical thought and analysis.

The benefit of these approaches to incorporating AI into teaching is that they help future-proof for advances and improvements. While it is tempting to ban any use of this technology, not many institutions will have the ability to enforce these bans. As advances in the abilities of this technology almost inevitably occur, designing teaching around what it can provide rather than attempting to avoid it will not only allow teachers and schools to be more future-proofed and flexible, but it will ensure students can better manage their use more skilfully outside and after class.

Are there ethical concerns?

As with any new technology, academics and researchers have ethical concerns. One of the largest such concerns is around the legal status of the data that is used to train these AI. There is currently a lawsuit pending from Getty Images against Stable Diffusion, one of the biggest image-generating AI, and more lawsuits from other media companies are expected. However, while large media companies have the resources to bring copyright lawsuits, many small creators and artists do not. The crux of the position here is that their work is being used, without consent, knowledge, or recompense. Further, styles of art that may be specific to an artist can be (re)produced by this technology. While the focus here has been on image generating technology, it is no different for LLMs that focus on the written word. Undoubtedly this is something that will be worked out in the courts over time, but there is no guarantee that smaller creators will be respected. Any use of the technology has to be with this in mind.

Bias in the outputs of these tools is also a worry. There has been considerable research done on the ways in which bias is replicated and made worse by AI technologies. There are two common ways in which AIs can be biased: in design choices (either implicitly via ignorance or assumption, or explicitly via prejudice) or via bias in the sets of data the AI is trained on. The former is clearly evidenced in decisions made by early facial recognition technologies that were not given information on darker skinned faces. Neural net AI tools are particularly susceptible to training data bias. These tools are trained on enormous sets of data that are pulled from online sources and some of that data comes from the less savoury corners of the internet. If bigoted and discriminatory training data are common enough within an AI tool's data, the AI will replicate that in its outputs.

Further, there is the potential for misinformation to be spread by this technology. AI tools can be used by bad actors to generate large amounts of hateful or even just incorrect text. This text can then be shared widely, at a much lower cost than having human writers generate misinformation. Also, given the credence the content produced by AI has, it lends itself to having a degree of authority. This is problematic given their inability to determine meaning or whether something is correct.

The credibility the technology has as an information source is related to another ethical concern, that of anthropomorphism. Humans have a tendency to imbue animals and things in their environment with a degree of 'humanness'. This tendency has historically been the domain of culture and religion, and it has been a rich source of inspiration about the world and how we understand our moral relationships. Many AI tools have been designed to appear human in a way that encourages this tendency. While some AI tools, like those offered by Character AI, do so clearly within the realm of fiction, most do not. This means that it is easy for people to see the AI as self-aware or having agency. This is deceptive and can lead to serious harms, including radicalisation to violent extremism, that are still being explored in the scholarly literature. There is also a potential risk in humans transferring their poor treatment of AIs to human beings or animals. If we treat an AI poorly and we see that AI as being 'human' to an extent, then negative patterns of behaviour towards the AI can be replicated in our dealings with people[9].

Finally, we need to consider the impact on climate change mitigation efforts. AI requires processing power that is considerably larger (due to their higher and different levels of processing) than current data processing centres, which themselves already have an impact on our power grids. Widespread adoption and ubiquity of AI will result in larger amounts of resources required to run them, and it is important to be aware of how this will affect our electricity use.

Note: this technology is rapidly advancing, so this research review must be located in the context of its time and is valid as of early 2023.

Further reading and resources
ChatGPT is everywhere. Here's where it came from | MIT Technology Review
A solid history of the development of Chat-GPT and how we got to where we are.
https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web
A thoughtful and sceptical article about Chat-GPT and its potential.
[2301.04655] ChatGPT is not all you need. A State of the Art Review of large Generative AI models
This article offers a taxonomy of the primary models used currently for generative AI tools.
https://edarxiv.org/5er8f?trk=organization_guest_main-feed-card-text
A position paper on the potential uses of generative AI in education.
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education | Smart Learning Environments | Full Text
This article offers a summation of the current uses of chat-bots in education and offers a research agenda.

On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

A comprehensive analysis of the ethical concerns and risks of these tools.
Glossary and product directory
AI hallucination
https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)
AI Literacy
https://www.sciencedirect.com/science/article/pii/S2666920X21000357 https://www.bcu.ac.uk/education-and-social-work/research/news-and-events/cspace-conference-2021/blog/ai-literacy-the-role-primary-education
Anthropomorphism
https://en.wikipedia.org/wiki/Anthropomorphism
Artificial Intelligence
https://en.wikipedia.org/wiki/Artificial_intelligence https://www.britannica.com/technology/artificial-intelligence
Algorithm
https://en.wikipedia.org/wiki/Algorithm https://www.britannica.com/science/algorithm
Character AI
https://beta.character.ai
Chat-GPT
https://en.wikipedia.org/wiki/ChatGPT
DALL-E
https://en.wikipedia.org/wiki/DALL-E
Generative AI
https://www.techtarget.com/searchenterpriseai/definition/generative-AI https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
Getty Images
https://en.wikipedia.org/wiki/Getty_Images
Grammarly
https://en.wikipedia.org/wiki/Grammarly
Large Language Model
https://en.wikipedia.org/wiki/Wikipedia:Large_language_models https://www.forbes.com/sites/robtoews/2023/02/07/the-next-generation-of-large-language-models/
Microsoft's Bing
https://en.wikipedia.org/wiki/Microsoft_Bing
Neural Net
https://en.wikipedia.org/wiki/Artificial_neural_network https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Stable Diffusion
https://en.wikipedia.org/wiki/Stable_Diffusion
TurnItIn
https://en.wikipedia.org/wiki/Turnitin Endnotes
[1] Tack, A., & Piech, C. (2022). The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues.

[2] Hwang, G., & Chang, C. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, DOI: 10.1080/10494820.2021.1952615

[3] Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education, 3. DOI: https://www.sciencedirect.com/science/article/pii/S2666920X22000169

[4] Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education, 3. DOI: https://www.sciencedirect.com/science/article/pii/S2666920X22000042

[5] Su, J., & Ng, D. T. K. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education. DOI: https://www.sciencedirect.com/science/article/pii/S2666920X23000036

[7] Steinbauer, G., Kandlhofer, M., Chklovski, T., et al. (2021). A differentiated discussion about AI Education K-12. Künstl Intell, 35, 131-137. https://doi.org/10.1007/s13218-021-00724-8

[8] Sun, J., Gu, C., Chen, J., Wei, W., Yang, C., Jiang, Q. (2022). A study of the effects of interactive AI image processing functions on children's painting education. In: Gao, Q., Zhou, J. (eds), Human Aspects of IT for the Aged Population. Design, Interaction and Technology Acceptance. HCII 2022. Lecture Notes in Computer Science, vol 13330. Springer, Cham. https://doi.org/10.1007/978-3-031-05581-2_8

[9] Coghlan, S., Vetere, F., Waycott, J. et al. (2019). Could social robots make us kinder or crueller to humans and animals? International Journal of Social Robotics, 11, 741-751. https://doi.org/10.1007/s12369-019-00583-2

By Mandy Henk and Dr Sarah Bickerton, from Tohatoha Aotearoa Commons

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An introduction to the role of Artificial Intelligence in classrooms and schools - THE EDUCATION HUB

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A guide to the latest developments in artificial intelligence and what they mean for teaching and learning.