By Melanie Robinson, Associate, Okana
As someone who studied innovation diffusion theory as part of their PhD, the sudden boom of Artificial Intelligence (AI) is fascinating to watch and be involved in. As with many digital innovations, it has highlighted some wonderfully human traits in society, differentiating between the go-getters and innovators, the laggards and luddites, and everyone else in between.
Terms like Large Language Models (LLM) and Generative AI have become regular parlance as we generate fun images of ourselves as action figures or in the style of Studio Ghibli, showing that the novelty of it all is still very much there.
So why am I bringing this up in an article about BIM and education?
Because Building Information Modelling (BIM) and AI both rely on the symbiotic relationship between technology and people, with education being the enabler for that relationship.
And yet, we still hear about people not wanting to adopt BIM, whilst in the same breath, exploring how to use AI to automate a particularly onerous set of tasks.
Now, I know that it is far more complex than that, but the academic within me can’t help but ask the question: why does one digital innovation spark excitement while another is perceived as cumbersome or unnecessary?
The most obvious reason, beyond the fun novelty of seeing yourself as a Simpsons character, is that there is a personal gain to be had through AI applications. You can achieve quick wins without needing wider organisational change or undergo extensive training, whereas innovations like BIM are inherently more complex and needs deep-rooted change through governance frameworks and change management models.
In innovation diffusion theory, this is known as perceived complexity versus tangible benefit, which relies on characteristics such as compatibility, trialability, relative advantage, observability and the extent of complexity. On one hand, BIM requires upfront investment in not just a suite of tools but also a great deal of process re-engineering, whilst on the other, AI can produce quick, tailored results with an immediate pay-off and limited investment. People find themselves more willing to experiment with a very basic understanding of prompt engineering, even if they don’t fully understand the underlying mechanics.
Education doesn’t end at graduation
This is where education is critical: alongside support and appropriate resources, fostering the knowledge, skills and mindset necessary removes many of the people-led barriers to complex innovation adoption.
Both AI and BIM need people who don’t just understand how to use them, but also why. This is not only the critical role of education and training, but also the role of a continuous upskilling culture. Industry needs to embrace and make time in the working week for this, ensuring we aren’t just teaching people how to use tools, but also providing them with a well-rounded understanding of collaboration, strategic thinking and ethical judgement, to support an innovative mindset.
After all, education doesn’t end at graduation. Whilst the term ‘education’ tends to conjure images of lecture halls and classrooms, the need for it never goes away; if anything, the pace of change in an increasingly digital world demands it. Initiatives such as industry-wide continuous professional development (CPD) programs, industry conferences, and professional membership routes certainly go partway to bridging the gaps between traditional academic timelines and the rapid pace of technological change, albeit the lack of standardisation and coherent knowledge framework underpinning this is questionable – however, that’s another article for another day!
The question at the forefront of my mind today is, if we as industry professionals are battling to keep up, how are academic institutions and educators bound by long lead-in times for curricula updates responding?
One potential answer lies in the growing push for agile education models. Instead of overhauling entire curricula, which can take years to plan, validate and implement, many institutions are embracing modular, bite-sized learning opportunities to keep pace with technological advancements. Short courses, online modules and micro-credentials allow academics to slot new developments into existing programs without waiting for the next major curricular review cycle. This approach not only speeds up knowledge transfer but also helps educational institutions nurture the very mindset that AI and BIM both need: one that is ready to adapt, test out new ideas, and continuously learn.
Collaboration between academia and industry
Collaboration between academia and industry is another key driver. Industry partnerships allow educators to tap into emerging best practices, giving students exposure to real-world challenges and tools, through co-created curricula.
When universities partner with software providers or industry organisations that are actively experimenting with AI and advanced BIM workflows, the classroom experience becomes more relevant and resilient. At the same time, industry gains from a pool of graduates who are better aligned with fast-moving innovations, while academic staff stay current through regular interaction with professional practitioners.
In essence, if we want to see holistic adoption of BIM and continued responsible experimentation with AI, it’s not just about introducing new tools or frameworks; it’s about recognising the broader cultural and educational shifts necessary to sustain progress.
The complexities of BIM and the excitement surrounding AI may differ in their adoption curves, but ultimately, both hinge on a mindset that values learning, exploration and openness to change. It is in that mindset where academic institutions, industry professionals, and learners of all ages can align, ensuring that as the technology around us evolves, we evolve too.
