Associate Professor Linda Corrin, Deakin University, ASCILITE Community Fellow

It’s happening again! The recent emergence of generative Artificial Intelligence (AI) technologies has resulted in a new labelling of today’s young people who are growing up in a world of AI. The “Digital Natives” are being usurped by the “AI Natives”.

In discovering this new label I was reminded of the famous quote by philosopher George Santayana that “Those who fail to learn from history are condemned to repeat it”.

For me, this overwhelming sense of déjà vu was prompted a few weeks ago when I saw a post on X (Twitter) by Dave Cormier (University of Windsor, Canada):

A particular reply to this post, by Carlos Santos (University of Aveiro, Portugal), piqued my interest and led me to read his earlier Medium story:

In response to Santos’ prompt, ChatGPT posited that AI Natives “possess an intuitive understanding of how AI systems operate and are adept at utilizing them to enhance productivity, creativity, and decision-making”.

This unsurprisingly echoes the Digital Natives notion originally proposed by Marc Prensky in 2001 that continual exposure to technology since birth (from 1980 onwards) meant that “today’s students think and process information fundamentally differently from their predecessors” (p.1). By contrast, the label “Digital Immigrants” was applied to older generations who came to technology use later in life and therefore retained the “accent” of the past.

What does the research say?

When I submitted my PhD thesis on why the concept of Digital Natives was problematic for higher education exactly 10 years ago, I had hoped the idea would slowly fade into obscurity. After all, the assumptions underlying the concept had been critically questioned by many high-profile researchers from around the world.

One of the most cited papers in this space is “The ‘digital natives’ debate: A critical review of the evidence” (2008). In this paper, Bennett, Maton, and Kervin suggested that these generational labels were creating a dangerous ‘moral panic’ that was getting in the way of our legitimate need to understand young people’s interactions with technology to inform its effective use in learning and teaching contexts. Interestingly, the most highly cited paper in ASCILITE’s Australasian Journal of Educational Technology (AJET) is “First year students’ experiences with technology: Are they really digital natives?” (2008) in which Kennedy et al. found that experience with and use of emerging technologies by those considered digital natives was “far from universal” (p. 8).

In line with these findings, the outcomes of my own research (with Professors Sue Bennett and Lori Lockyer) concluded that “generational generalisations are not useful in informing the design of learning and teaching in higher education” (p. 387).

Surely this wealth of research advising caution would convince people to move away from the practice of applying such generational labels. Sadly, the idea of Digital Natives persisted.

Now it has evolved into “AI Natives”.[1]

Since learning of the supposed existence of AI Natives, I discovered that this label isn’t as new as I had first thought. The earliest record of “AI Natives” on Google Scholar appears to date from 2019. A quick general Google search found that many industry commentators and technology vendors have already jumped on the AI Natives bandwagon when discussing the impact of AI on work, learning, and life.

One such commentator has even created a continuum of nativeness from Analog-natives, through Digital-natives, to AI-natives. Repeating the problematic patterns of the past, the design of this continuum is based on assumptions about the heterogeneity of people based on age, rather than a more nuanced understanding of the diversity of people’s experiences with technology… and learning.

Unsurprisingly, Marc Prensky weighed into the discussion in late-2023. In a LinkedIn post entitled “Our coming AI Natives” Prensky suggests that “young people will grow up understanding how to control Generative AI just as they learn to direct their own bodies and minds, with much of the guidance they need in their pockets or “chipped” into their bodies”.

It really is happening again!

So how can we avoid the mistakes of the past?

While the AI Natives horse has somewhat bolted, as evidence-informed educators it is important that we take the good advice from Digital Natives research to look beyond the hype to what we can do to ensure that learners can make the best and most appropriate use of AI in their learning and practice.

Rather than assuming that learners of certain ages are all the same, and that younger ones won’t need support in using AI due to an ‘intuitive ability’, we can work from the understanding that there will be a variety of starting points for our diverse learner populations.

Focusing on how we can develop levels of digital fluency in ways that include core AI knowledge and skills can equip learners to learn within and adapt to this ever-changing technological environment.

Also key to this approach is the provision of professional learning opportunities for educators to develop their own digital fluency and AI literacy. However, I would suggest this will be far more successful if we don’t first label them as “AI Immigrants”.

We can learn from the past by moving beyond unhelpful age-based native/immigrant dichotomies and focus instead on learning and innovating together in this continually evolving AI landscape.

Acknowledgements

An attempt to use Google’s Gemini to wordsmith the title of this blog post was made. Despite multiple prompts, a suitable title was not generated. Consequently, the title and all other text in this post was human generated. If the AI Native myth is to be believed, that is because I’m an AI Immigrant. Although I did work out how to use Midjourney well enough to generate the main image for the blog. Perhaps there’s hope for me yet!

[1] This is not to be confused with the technology concept of “AI Native” networking which “refers to computer networking systems that are conceived and developed with artificial intelligence (AI) integration as a core component to enable simpler operations, increased productivity, and reliable performance at scale”.