Why the need for AI literacy?

Artificial Intelligence (AI) literacy for staff and students will arguably be the next frontier in developing enhanced employability for many graduates. The widespread availability and use of Generative AI tools is shaping up to be a significant disruptor across many areas of human endeavour including study and work life.  

Is AI literacy just modern ICT literacy? 

AI literacy can be thought of as an extension of information and communications technology (ICT) literacy, information literacy and more broadly digital literacy, because it needs elements of all of these. However Generative AI tools have different and additional affordances compared to other ICT that make AI literacy an added area of expertise that needs to be considered.  

What should AI literacy include?

Here I outline a possible AI literacy framework, along with some starter ideas for how educators may approach each element. This framework came about in considering what a student or member of the public would need to be able to effectively utilise emerging Generative AI tools. The framework is focused on a user perspective rather than a developer perspective because the majority of students will sit in the former category. It is perhaps less desirable at this early stage to add complexity pertaining to the development of AI technology that would only be relevant to a minority. Those developing AI tools would be better served by programs in computer science, data science and artificial intelligence. It is likely that as Generative AI becomes more embedded that a user-as-developer perspective should be added – as we see in the UNESCO digital literacy framework.

An AI literacy framework:

a) Ethical use of AI tools (why and when, including issues such as data ownership, privacy, algorithm transparency, legality, types of embedded bias, undisclosed plagiarism, equity, and hidden labour). Online courses such as “Humane technology” provides another example of possible topic coverage. Many of the ethical and legal issues are yet to be resolved. Educators can explore with students ethical issues from multiple perspectives, perhaps by facilitating class discussion. This includes guidelines for the ethical use of AI in education. Jointly arriving at a position for the use of generative AI tools in the local context, including guiding the using AI for study and research purposes, will help to minimise uncertainly for students and put them in a better position to be able to make informed decisions about use of AI tools in the future.

b) Knowledge of AI affordances (Over 1000 AI tools are available and therefore awareness of the capabilities and limitations of various AI tools will help users choose appropriate tools for the job). Educators can collaborate with students to explore the capabilities and limitations of different tools relevant to the unit context, this could also include the key benefits and risks (e.g. hallucination, undisclosed plagiarism, embedded biases) associated with each tool. Students can be asked to undertake research on one or more tools. Building map of common features and limitations to AI tools and typical uses can then help students decide which tools may suit a given task. 

c) Working effectively with AI tools (e.g. “prompt engineering” and prompt refining practices). Educators can leverage free, open access online resources that are appearing such the “Learn Prompting” online mini course. The educator could then work with students to develop unit specific examples and lead discussion on the effective use of tools relevant to the discipline, unit or assessment task context. As indicated earlier a ‘user-as-developer’ perspective may fit here when the maturity of the tools and their users develop further in the future.

d) Evaluation of AI output. Generative AI is known to hallucinate to produce plausible, but false information in its output (such as fake references) and so being able to evaluate the output for its quality is a key capability in making use of AI tools. Thinking, critique and evaluative judgement are important skills regardless. Educators can use frameworks such as the MQ library CRAAP model to work with students to identify the characteristics of quality AI generated responses. 

e) Use and integration into practice. Generative AI can add value in study, personal and for professional work purposes (e.g. co-writing, ideation, creativity, code starters and code review). Building awareness of the changing patterns of work will also help students make informed career and development decisions. Educators can collaborate with students to explore how industry are adopting generative AI tools, how it is impacting workflows and productivity as well as how industry can navigate the emergent issues that these tools bring, such as by using an AI risk framework to evaluate technologies. This could also be done by leveraging existing industry links via work integrated learning activities and MQ’s PACE arrangements. Dr Ali Amrollahi from MQBS provides an example of how a unit can be designed to teach a non-technical audience about how advanced technologies (such as Blockchain) can be deployed in professional contexts.

Where to next?

We are in the early stages of a potential AI driven revolution to the way many of us work and live. The potential benefits of well managed implementations of new technology can be big, but so are the pitfalls when such technology is done poorly (see Robodebt Scheme for an example of the latter). The 4th industrial revolution is underway and education institutions need to get on board to develop thoughtful and integrated approaches to educating students for this future as well as rethinking the design of education programs and operation of the institutions themselves.

Further reading on a review of AI literacy frameworks:

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Join the conversation

We welcome your comments below about on this proposal for an AI literacy framework and how we can work with students around the use AI at tools. You can also contribute your ideas by emailing professional.learning@mq.edu.au.

Read other posts in the generative AI series. Recent posts include: comparing ChatGPT to a calculator and a search engine and the launch of Turnitin’s new AI writing detection feature.

Found an ‘AI generated’ academic integrity breach? See this advice on how to gather evidence and report it to MQ for investigation.

Acknowledgements: Banner image: Modified image based on Stable Diffusion output with the prompt “view through magnifying glass to look at tiny robots. plain background.” (24 March 2023). Further edited by M. Hillier. CC0 1.0 Universal Public Domain. Post last updated 11 May 2023.

Posted by Mathew Hillier

Mathew has been engaged by Macquarie University as an e-Assessment Academic in residence and is available to answer questions by MQ staff. Mathew specialises in Digital Assessment (e-Assessment) in Higher Education. Has held positions as an advisor and academic developer at University of New South Wales, University of Queensland, Monash University and University of Adelaide. He has also held academic teaching roles in areas such as business information systems, multimedia arts and engineering project management. Mathew recently led a half million dollar Federal government funded grant on e-Exams across ten university partners and is co-chair of the international 'Transforming Assessment' webinar series as the e-Assessment special interest group under the Australasian society for computers in learning in tertiary education. He is also an honorary academic University of Canberra.

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