I delivered a half-day AI session with Bernie Goldbach to a group of supply chain students in Kemmy Business School at UL during the summer. I stressed to them the fact that if they use AI to do the work for them, they learn nothing. Their time studying, if only to obtain a piece of paper, as a means to an end, will be pointless in all practicality. I said, “if you use ChatGPT to write your assignments, you'll remain just as stupid as your were before you started”.
Controversial language, I'll admit, but it was designed to be, and it's not untrue. More accurately, if you and I rely on the AI to make the decision for us, to be the arbiter of truth and accuracy, we are in effect, dumbing down our own intellect and risking our jobs and/or our businesses. Eventually, our minds will atrophy, just as our limbs would if we stopped using them, and we become mindless automatons. God knows, we don't need any more of those!
Some voices in education, learning and development are fighting hard against AI, and I think that's a mistake, even naive. The technology is here, the cat is out of the bag, the worms are out of the can, so instead of insisting things stay the same, we've got to up our game. Knowing students are already using AI, how can we assist them use it in a way that enhances their personal knowledge and critical thinking? If we can't do that, we're driving the behaviour under ground and pretending change hasn't happened.
In 1819, the famous economist David Ricardo wrote that the amount of employment in an economy was of no consequence as long as rent and profits, out of which flowed its new investment, were undiminished. “Indeed? replied Simonde de Sismondi, a well known Swiss critic of the times. “Wealth is everything, men are absolutely nothing? What?… In truth then, there is nothing more to wish for, the king, remaining alone on the island, by constantly turning a crank, might produce, through automata, all the output of England.”
Robert L. Heilbroner in The End of Work by Jeremy Rifkin
The Value In Challenge & Difficulty
Technology has always served to simplify and augment human effort. From the invention of counting systems like the abacus 4000 to 5000 years ago, to the plough, the printing press, electric motors, digital currency and many other ideas that have changed how we do our work. We find ways to work better, more efficiently, and in doing so, we outsource mental and physical effort to mechanisms and software.
This outsourcing or offloading of human cognition has accelerated in recent times with the advent of the internet. Never before have we witnessed the speed with which this is happening as Generative AI enters everywhere humans work. Soon, we are promised, the machines will handle all our daily mental tasks and simplify all the challenges of life and work. Finally, the life of ease we've always been promised will be ours.
Utopia or Dystopia, I can't decide which.
Surely the challenge and the effort in learning something or figuring out an answer to a complex problem is where the real meaning and purpose of life lies. Achievement without effort seems like no achievement at all. Aristotle seemed to think so. In Nicomachean Ethics, he railed against the hedonistic life that sought ease and instant gratification of every desire referring to it as a slavish pursuit. He said that true happiness is to be found in the expression of excellence and virtue. That is, in the doing well of what is worth doing. According to Aristotle, Eudaimonia (human flourishing) is found in tasks that are inherently challenging yet worth doing in and of themselves–no ulterior motive required. It is the foundation of contemporary ideas of wellbeing such as Self Determination Theory (Deci & Ryan, 2017).
So if generative AI does the thinking for you, allowing you get things done simply for the sake of getting them done, life and work become devoid of meaning and purpose. You might as well not exist. I understand that this may well suit the subclinical psychopathic capitalist who finds constant irritation with human beings that go sick, have babies, and work less than 80 hours per week, but the rest of us need something other than objective ends.
What Is Cognitive Offloading?
Cognitive offloading refers to the delegation of human cognition to external artefacts and systems. In the workplace, it contributes to how we learn, make decisions and judgements, and maintain or develop new skills. Used selectively, it can save time and help people get things done efficiently and productively. Used indiscriminately, it displaces effortful thinking and weakens learning and retention and, therefore, negatively influences our capacity to make informed decisions.
The extended mind thesis (Clark & Chalmers, 1998) treats external resources as integrated parts of cognition. For example, long before Generative Artificial Intelligence, lists, diagrams, and calculators helped augment our memory and problem‑solving capacities. The brain’s metabolic cost favours these strategies, freeing our finite attention faculty for higher‑order reasoning. It serves us to know that the human brain likes to take the shortest route to a conclusion, but we've got to be careful because this fast system 1 mode of thinking (Kahneman, 2011), can get us in trouble. The slower, more deliberate System 2 is required to think things through, to weigh up the options, to consider our ethical position and so on. With Generative AI tools, however, we're farming both of these thinking systems to machines.
Benefits of cognitive offloading are conditional. It can reduce cognitive load when encoding new information, which in turn can weaken long‑term retention and recall (Grinschgl et al., 2021). The practical issue in the workplace is when to offload, and when to continue the demanding task and develop thinking skills. When the pressure is on from those upstairs to improve efficiency and reduce headcount, we may find we've no choice. A student on my organisational behaviour Masters program admitted last week that bosses in her software development company have insisted on no new hires, and that all teams adopt an AI to improve project efficiency. No pressure then… I suppose we'll have to break things and lose clients before we realise these tools won't replace people just yet.
Michael Gerlich on Cognitive Offloading
Prof. Dr. Michael Gerlich released a paper earlier this year investigating the relationship between AI tool usage and critical thinking (Gerlich, 2025). The phenomenon was explored through surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. The study found a significant negative correlation between frequent AI tool usage and critical thinking abilities with younger people exhibiting higher dependence on AI tools and lower critical thinking scores compared to older people. Higher educational attainment was associated with better critical thinking skills regardless of whether AI tools were used or not.
Gerlich recognises while cognitive offloading can improve efficiency, extensive reliance on AI may reduce the need for deep cognitive involvement, affecting critical thinking. Sparrow et al. (2011) demonstrated this through what they called the “Google effect.” This “Transactive Memory” reflects people's tendency to remember where to find information rather than the information itself. When AI tools provide quick solutions and ready-made information, they discourage workers from engaging in the cognitive processes essential for critical thinking (Gerlich, 2025). This represents a shallow and wide mode of thinking and is poised to have profound implications for organisational outcomes, not to mention individual learning and development.
Trust and Dependence
We had a saying on the building sites back in the day; “sorry, I only work from the neck down.” We had learned that the figuring out, planning and organising was done by people on the big bucks. The blokes on the tools were the grunts, paid to show up and execute. This wasn't a flippant remark that served to make our lives easier–although it may have done–instead, it reflected the truth which was that bosses didn't really valued or trust us beyond the doing of the work–they didn't pay us to think.
And today with the proliferation of AI in the workplace, I see a similar scenario play out. Some organisations are shy of the technology, occupying the fringes, waiting for things to become clearer perhaps. Others are driving forward into the unknown, forcing their staff to take on these tools without proper consideration or planning, affording too much trust in the hype machine.
Gerlich's paper reports that as users of AI tools develop greater trust in those tools, and they farm out to them more cognitive demanding tasks. This creates dependence, which might be great for tech companies selling the tools, but not so much for people because it chips away at our ability to critique and challenge. Long-term reliance could erode essential cognitive skills, Gerlich says, such as memory retention, analytical thinking, and problem-solving. Workers may become less capable of independent thought and more vulnerable when technology is unavailable. In other words, if we train our people not to think, that's what they'll do.
Recommendations Based on Gerlich 2025
So, how can you protect your organisation against the negative consequences of AI tool dependance? Here are some suggestions based on Gerlich's paper.
Assess Reasoning Rather Then Results.
The study shows cognitive offloading produces correct outputs while eroding the underlying thinking skills. Evaluation should capture how learners arrived at conclusions, not whether AI helped them get there.
Put Effort First.
Workers who engage cognitively before accessing an AI are likely to retain more and develop a foundation for critical analysis. Design learning experiences where participants attempt to solve problems independently before employing AI.
Use AI for Practice & Idea Generation.
AI can create scenarios, case studies, and provide feedback on learner responses. But the explanatory and sense-making work should remain with the learner to build critical thinking capacity.
Develop Metacognitive Skills.
Gerlich emphasises that learners need to recognise when AI use undermines their own abilities and development. Training should explicitly address metacognition (thinking about thinking) and when offloading is or is not appropriate.
Target Younger Cohorts.
The study found younger participants and those with lower educational attainment showed higher AI dependence and lower critical thinking scores. These groups may need more tailored AI learning programs at work.
Promote Active Group Learning.
Gerlich's paper cites evidence that active engagement (group discussions, problem-based learning) correlates with critical thinking development (Tusi, 2002; Freeman, 2014).
Balance Efficiency Needs with Human Thinking Skills.
AI tools offer genuine productivity benefits to people and organisations–I've seen this first hand. But the goal should not be elimination, instead collaboration and augmentation. The AI tools you choose must complement rather than replace the cognitive work that your organisation needs to successfully navigate this time of rapid change.
If you want help achieving that, get in touch with me about our AI Skills programme. Bernard Goldbach and I have delivered an ever developing version of this programme since 2023 with close to one thousand participants in total over the past two years. AI won't be replacing you or your staff any time soon, the aim of smart organisations must be on education and development of AI skills.
Thanks for reading…see you next week
P.S. I'm running a free AI Workshop Friday 5th at 11:00am on Zoom. 👈 Get on it..Places are limited though
Learn AI for Work
Take your learning a level deeper – join the GenAI Skills Academy where I share tutorials on how to become more productive and creative with Generative AI tools without sacrificing your intellect. It's perfect for people who don't code, and it's free too (for the moment).
References
- Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual review of organizational psychology and organizational behavior, 4, 19-43.
- Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the national academy of sciences, 111(23), 8410-8415.
- Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
- Rifkin, J. (1998). The end of work: The decline of the global labor force and the dawn of the post-market era. Journal of Leisure Research, 30(1), 172.
- Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.
- Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778.
- Tsui, L. (2002). Fostering critical thinking through effective pedagogy: Evidence from four institutional case studies. The journal of higher education, 73(6), 740-763.
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