Today I had my first opportunity to share my research into generative AI and thinking through education futures with my new colleagues in the School of Education at La Trobe. It was a great experience and it got me thinking about the fact that there is so much need and desire in this field to talk about AI and its implications for teaching, learning and education futures. With my background as a sociologist of technology, it is almost like walking into a cornucopia of relevance rather than working away in my digital fringes.

I thought I’d take the time to share some of my formative thinking from engaging with the research on this topic here so that there is some kind of breadcrumb trail back to this point in time as I start moving into a familiar but new research space. Never fear however, I am still watching other frontiers such as Web3 (on which I am currently writing a book!).

First, I chose to grapple with the question of why we are talking about AI now.

The research, tech-focused and public discourse on the topic position Artificial Intelligence (AI) as an emerging and powerful technology with the potential to revolutionise various aspects of our lives. In the field of education, AI offers promising opportunities to enhance teaching and learning experiences, personalise instruction, and improve educational outcomes. However, it has already been flagged to raise considerations regarding ethics, equity, and the evolving role of educators.

In this blog post I’ve decided to delve into the world of AI in education, exploring its definition, historical trajectory, current applications, and the trends we need to consider. 

Let’s start with its historical trajectory.

AI in education has a rich history dating back to the 1980s. It has evolved alongside the growth of the commercial education technology (EdTech) industry and the influence of global technology corporations on education. This technology is intersecting and constituting with other technology trends including the Internet of Things (IoT), Big Data, machine learning, neural networks, platforms and applications. Some of the common threads of critique and conceptualisation across this complex of technologies are notions of ubiquity, platform capitalism and extraction.

From the early focus on simulating human intelligence and expert systems, AI in education has shifted toward data-driven approaches that leverage machine learning, neural networks, and deep learning. The convergence of AI research, edtech industry development, and data-driven policy has contributed to the current landscape of AI in education.

So what are its current applications and considerations?

AI in education, often referred to as Artificial Intelligence in Education (AIED), encompasses two main strands: the development of AI-based tools for classrooms and the use of AI to understand, measure, and improve learning. Examples of AI applications include intelligent tutoring systems, chatbots, and image/video generation tools. These technologies afford personalised feedback, adaptive learning paths, and real-time interaction. However, several considerations must be addressed, including concerns about truth, bias, equity, social justice, and ethical implications. It is crucial to fact-check AI-generated information, be aware of potential biases in training data, and ensure equitable access to AI tools and resources.

What do the tech trend types tell us to focus on?

There is an interesting comparison of trends across the 2021 and 2023 EDUCAUSE Horizon reports that highlight the rapid evolution of key technologies and practices shaping the future of education. The 2021 report highlights Artificial Intelligence, learning analytics amongst the top five trends, while the most recent 2023 report identifies AI-enabled applications for personalised learning and generative AI as having the potential to transform teaching and learning experiences. 

What’s in focus for me in all of this?

I am currently focusing on the opportunities and challenges of generative AI for education futures, which is technically referred to as Generative Pre-Trained Transformer (GPT) AI. Generative AI is conversational AI technology that is activated through user-supplied prompts and conversational turn-taking between the user and the AI. It uses supervised and reinforcement learning techniques to understand and model human and non-human languages. Examples in education include chatbots, intelligent tutoring systems, and image and video generation tools. I’ll have more to say on this in the future.

A quick recap on what I think the take home points are.

AI holds significant promise for education, offering innovative tools and approaches to enhance teaching and learning experiences. By leveraging AI technologies, educators can personalise instruction, provide real-time feedback, and create adaptive learning environments. However, careful consideration must be given to the ethical implications, potential biases, and the impact on equity and social justice. It is essential to navigate this evolving landscape thoughtfully, ensuring that AI in education aligns with the goals of fostering critical thinking, nurturing creativity, and empowering learners to thrive in an AI-driven world.