Skills are more critical than ever in today’s AI world

An increasing and unchallenged trend is the use of Artificial Intelligence (AI) to undertake the work of engineering professionals. Application tools, such as ChatGPT, are retrieving data, preparing documents, writing code, providing advice, undertaking research and summarising it.

There is a pervasive belief that all existing knowledge is now on-tap, and as with a calculator, the results can be trusted implicitly with minimal knowledge or acquired skills. Many believe AI will merely need an engineer to proficiently prompt it. The results vary wildly however, from reasonably coherent to unadulterated nonsense.

The respected Professor Barbara Oakley and her colleagues have dissected what they call ‘The Memory Paradox’.¹ It explores another reason why engineering professionals should use AI prudently.

The Memory Paradox

Our increasing reliance on AI to offload cognitive tasks weakens our memorising systems and skills. Both declarative (fact-based: why) and procedural (skill-based: how) memory have been responsible for strengthening neural pathways and enhancing our ability to learn and apply knowledge. Their underutilisation, alongside our diminishing cognitive engagement, may be contributing to the Flynn Effect: the recent decline in IQ scores in developed countries.

Modern educational practices have compounded the issue by de-emphasising memorisation and foundational knowledge. The authors of The Memory Paradox, along with neuroscientific research, suggest that this approach undermines mental flexibility, which then hinders the development of robust internal schemata or frameworks. Without these we may struggle with understanding, complex reasoning, and adaptability.

What we should do

Are we to abandon AI? On the contrary, we should engage actively in its use, but not to the detriment of our mental acuity and skill development. The research recommends an active engagement with knowledge, including deliberate memorisation, to reinforce these systems.

In the fast-evolving fields of industrial automation and instrumentation, engineers and technicians must continuously adapt to new tools, platforms, and AI-driven systems. However, as The Memory Paradox highlights, over-reliance on digital aids can erode the very cognitive frameworks needed to innovate and troubleshoot effectively.

Training strategies should focus on building deep, retrievable knowledge — integrating cognitive effort with active learning — not merely procedural familiarity with software or equipment. Spaced retrieval practice, troubleshooting exercises, and scenario-based learning all help reinforce declarative and procedural knowledge. For example, manual loop tuning exercises rather than auto-tuning, or designing and simulating entire control architectures from scratch before deploying them into SCADA or DCS systems. Embedding knowledge will enable engineering practitioners to diagnose and solve problems even when systems behave unexpectedly or when automated aids fail.

Another approach is to regularly reflect and explain why a system behaves in a certain way. Walking through an operational process plant to understand and articulate how all the components work together can achieve this. Human expertise must complement AI-driven automation, rather than being a slave to it.

Engineers and technicians should aim to become adaptive problem-solvers, equipped with robust internal schemata that empower them to lead in an increasingly automated world. AI tools should support, not drive their endeavours.

^{1. Oakley B, Johnson M, et al 2025, The Memory Paradox: Why our Brains Need Knowledge in an Age of AI, <<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250447>>}

*Dr Steve Mackay has worked in engineering throughout Australia, Europe, Africa and North America for over 40 years in the mining, oil and gas, and power industries. A registered professional engineer in electrical, mechanical and chemical engineering, he believes university engineering programs need to be strongly focused on industry. He has been the author or editor of over 30 engineering textbooks sold throughout the world.

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