By Professor Ari Seligmann
Posted Tuesday, 15 April 2025

For many of us, AI still seems new and its rapid expansion along with other emerging technologies is changing the ways we operate, but we have a range of related experiences to help us to integrate new tools into our lives and evolve our practices to take advantage of them. For example, what kinds of utensils and cooking tools do you currently have in your kitchen? Most kitchens have a range of knives, forks, spoons, chopsticks, pots, pans, mixers, choppers, etc. Sometimes these tools also evolve, for example sporks. How do you decide which tool to use for which application? What range of safety precautions do you typically take?  How about your smartphone? What kinds of apps do you have? Which do you use regularly? How do you discover and install new apps? What kinds of data privacy and cybersecurity precautions do you take to safely and responsibly use apps on your smartphone? Arguably, evolving AI tools may be considered in a similar vein to our kitchenware, software or apps. They can all help us do things and they can presumably provide a range of conveniences, but they need careful consideration. They require appropriate precautions to ensure their safe and responsible use.

We have a range of related experiences to help us to integrate new tools into our lives and evolve our practices to take advantage of them

In a previous blog post accompanying the release of AI in Education Learning Circle guidance, we offered some suggestions for getting started with AI tools. This post is a follow-up, sharing some further resources and thoughts as the AI and related technology landscape continues to evolve. For many, the rates of change and the diversity of options can feel overwhelming. Some may still be wondering where to start and how to navigate, while others may be (im)patiently waiting for consensus on the basic set of AI staples to use in their regular work or personal lives. Others may be hopefully waiting for AI to pass like a storm; but like the internet and smartphones AI promises to continue insinuating itself into many aspects of our digitally enabled lives. 

Consider the range of digital tools you currently use to do your job at Monash. Your list might include word processors, presentation makers, PDF readers, spreadsheets, web browsers, bibliographic reference software, email clients, calendars, video conferencing, learning management systems, data management and data visualisation software, etc.  Some of these are provided as part of university enterprise systems to support our work and some are self-sourced to meet given needs. Our current digital toolkits will most likely evolve to include AI functionalities to augment their capacities and capabilities, a process that we are witnessing unfold in real time with the integration of Copilot, Gemini, Apple Intelligence, etc. into the staple software of the companies developing them. Concurrently, a wide and growing range of other emerging technologies are becoming available. Some are promising, some are yet to keep their promises, and some are poised to further revolutionise the ways we do things. Yet how do we get access to new tools to test and evaluate them, either personally or professionally, so that we can realise their potential benefits and consider possibilities for safe and ethical use?

How do we get access to new tools to test and evaluate them, either personally or professionally, so that we can realise their potential benefits and consider possibilities for safe and ethical use?

Our institutions are wrestling with rapid technological development and the uncertainty of which AI platforms to invest in as capacities quickly change and gradually improve, with sometimes little initial differentiation or indication as to how well products will align with our needs and values. New models and new applications are regularly introduced and competition is hot across the major corporations and open source innovators. As we (im)patiently wait for our institutions to articulate their technological positions, coordinate secured enterprise access to relevant technology, and slowly identify and stock up on key AI tools to support our work, we have two primary choices: wait until the dust settles or actively engage in exploring new and transforming tools. If we just wait and see, we risk things floating down stream at a pace we may never catch up with. If we wade into the flow of developments then we should embrace an experimental approach with lots of trial-and-error, learning and sharing together.

Monash has followed fellow global institutions entering the stream with the Copilot web browser search engine that has been provided to everyone. Uptake is progressing for this multipurpose AI assistant, which is based on Large Language Models (LLMs), including Open AI’s GPT-4. It has internet search abilities and image generation, and its commercial data protection enables the use of university data, including teaching materials. Copilot may be a useful entry level “spork” to start with. There are also a few other tools that are available at cost including Microsoft 365 with Copilot (including Word, Excel, Powerpoint, etc.), which provides a broader set of familiar “silverware” with increasing AI capacities. In addition, there are other enterprise AI tool options with different price points. Yet there is also a growing wide range of other AI tool “utensils” available for our careful consideration. If we can’t just wait for a full suite of tools to be handed to us, as a “picnic set,” and we don’t have a secure test kitchen (or quarantined sandbox) to safely try things in then we need other avenues to stimulate thoughtful consideration and engagement with AI tools.

Finding the time and space to experiment with evolving technologies is also a struggle amongst other competing demands. For example, in parallel with trying to keep up with the latest research in our fields and the latest pedagogical approaches to inform our teaching, it is challenging to also keep abreast of emerging technologies to further support these. We all have our go-to sources and shortcuts for gathering key information. Personally, I rely on regular AI updates, reviews, insights and tool options consolidated in Futuretools and the Neuron newsletters-my north stars to help navigate the evolving AI landscape. These resources have led me to pursuing a combination of overviews, reviews and previews as the primary strategy at Monash to build broad awareness and critical evaluation of AI tool options and to inspire hands-on experimentation and further knowledge sharing across the university. The following reflects on the collection, curation and development of a diverse range of resources and approaches that seek to draw together communal knowledge and experiences that are applicable to our context. These efforts are not intended to impart expertise but to foster and share collective intelligence and enable us to work together to navigate the rapid streams of technological change impacting how we live, work and study.

There are a range of university resources addressing AI tools and engagement including the AI at Monash website, Generative AI Shared Responsibility Model and TeachHQ supporting responsible use and AI literacy, and the AI in Education Learning Circle website introducing evolving tools and use cases and provoking careful considerations of key issues. The following briefly introduces a few further resources.

Test driving AI for teaching and learning MEA module

The Test driving AI for teaching and learning internal Monash Moodle site supports critical evaluation of the growing capacities of AI tools. The forum section provides a valuable platform for people to share their reviews of AI tools and experiences. The growing repository of examples offers insights into evolving tools, their potentials, pitfalls and educational uses. The site has been augmented with two new sets of resources. 

The first is a repository of the recordings from the ongoing AI Tool Time lunch-time online sessions. These sessions provided opportunities for staff to learn about some potentially useful AI tools, and see them demonstrated first-hand. Each session examined a different set of AI tools, introducing key features and considering benefits, limitations and cautions. The presenters demonstrated how the AI tools could be used for various tasks and projects in dialogue with the audience and the group tested out possibilities together. If you are wondering what Copilot in Microsoft 365 can do, are curious about how Google Gemini AI integrations and Notebook LM experiments are progressing, are interested in automating your tasks to reallocate your time and energy, or are curious to hear how AI tool pilots turned out, then check out the recorded sessions. Join an upcoming session or check out their recordings in the repository.

The second set is a collection of videos that review a range of available tools for different use cases. Each video shows how several related tools work, reveals some of their pros and cons, and raises some related key considerations prior to their use. If you are interested in seeing demos of AI tools for making presentations, videos, teaching materials or websites, or for interacting with text, video, and audio then watch this series of quick guided tours. However, these videos reveal that, just like kitchen utensils, cars, word processors or web browsers, there are a range of different AI tools that can accomplish similar kinds of tasks. They have related functionality but different interfaces and personalities so in making choices it is useful to try them out yourself and determine which ones suit your particular needs or working styles. The last video in the series also reinforces that AI tools are not only used in isolation and again, just like combining forks, knives and spoons to eat more efficiently, we can combine various AI tools into productive workflows.

AI tools for education dynamic register

This third effort approaches things from a different angle. How do you keep track of all of the tools and equipment in your kitchen, or all of the apps that are on your phone? Now imagine a University-sized kitchen or smartphone and you may start to appreciate the scale of the challenge of tracking AI tools at Monash. To do this, we are creating a university-wide AI tool register with an associated interactive visualisation to track what AI tools are being explored for education within our ecosystem. As AI tools rapidly continue to evolve and proliferate, the register and its associated dynamic visualisation help raise awareness of and provide an avenue for users to explore the AI tools being used across our diverse faculties. 

Tools in the register are divided into broad colour-coded categories (from general purpose LLM, to simulations, feedback, writing, image generation, idea generation etc.) and linked to faculties where staff have used the tools. We hope that this will be a productive resource to see the richness of current engagement with AI tools and to further support those who are looking for various AI tools to address given use cases within their own contexts and educational practices.

We invite everyone to participate and help us identify and share what is being used and potentially useful to others. In filling your toolbox with key staple AI tools you may need a text summarizer, a text editor, image generator, language translator (foreign/level/tone), a data visualizer, a code checker, a (re)search assistant, etc. Seeing what other people across the university are using could provide a useful place to start. The AI tools in education website hopes to bring together information to support broader understanding of what kinds of tools are operating currently in the Monash ecosystem complementing the range of reviews and the range of overviews available in the Test driving AI for teaching & learning site.

Further considerations

If we choose to engage (tentatively or actively) with approved enterprise tools, such as Copilot, and discover their possibilities or to cautiously explore the potentials of a diverse range of other available AI tools there are three key considerations. 

Firstly, we need to engage with AI tools with the same care that we do with unfamiliar kitchenware or software and use them carefully and responsibly to expand our repertoires. This includes paying careful attention to the data privacy and terms of services for all AI tools we use, i.e. using sensitive and or restricted  data only in tools with appropriate data protections in place as well as avoiding sharing personal information; and being careful about  what content we add into tools i.e. what rights do you have to the information you are providing to the AI tool, such as author, owner, with permission granted or creative commons access. 

Secondly, we have to check the accuracy, veracity and quality of outputs and stand accountable for the resulting products, as well as where and how we use them. Responsible use of AI requires accountability and explainability. Moreover, preparing future generations to operate in a world where AI is ubiquitous demands that we lead by example.

Thirdly, we need to gain comfort openly sharing our AI engagements. We readily share other productivity boosting tools as we find and use them, such as our newest kitchen gadgets, our go-to apps, our web browser preferences, or different web browser extensions. Sharing what and how we use AI is just another aspect of living and evolving with technologies. Moreover, robust critical discussions about different ideas, opinions, methods, tools and explorations are the backbone of academic environments, so we should be able to translate our academic modes of engagement to emerging AI technologies. Working in parallel with the building of enterprise AI toolkits, I encourage everyone to safely and responsibly experiment with AI and to graciously and enthusiastically share your findings (failures, successes and otherwise!).

Whether they are used regularly or sporadically, we often like to have our kitchens stocked with useful tools so we have ready access to them and know how to use them when needed. We may want to similarly stock up on a set of appropriate AI tools. The various efforts described above provide a network of support and platforms for engagement with AI tools to inform our educational practices. I encourage you to explore the evolving resources, experiment, build your tailored tool kit to facilitate your type of work, and contribute to the collective intelligence through the Test driving AI for teaching & learning forum, AI tools for education register and by providing suggestions for future AI Tool Time sessions. Share discoveries with your colleagues and expand conversations in the comments below. Together, let’s try to find some useful tools to help us cook up educational offerings and experiences for our evolving disciplines in the 21^st century.