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AI and Education: Cheating? Risky? Or right for future resilience?
Minimizing the risks of AI in education means bridging the ‘relevance gap’ to develop the kind of thinking young people will need to thrive in their careers.
The widespread reliance of students on AI points to a deeper problem: students realize when they’re being asked to develop skills that seem irrelevant or outdated.
How do we ensure that upcoming generations and younger employees have skills that are future resilient? And is there a safe role for Artificial Intelligence in empowering them to build these skills?
For educators, it’s a quandary. On the one hand, AI offers appealing efficiencies and convenience in assignment development, grading, and feedback gathering.
At the same time, educators are increasingly concerned about its unrestrained use and the potentially harmful impact on students’ critical thinking, creativity, writing abilities, and overall academic integrity.
Getting it right – integrating AI into education responsibly and effectively – means first focusing on what it is that students need to learn. This is critical because the increasingly ubiquitous use of AI isn’t just about cutting corners, nor is it a function of laziness. The widespread reliance of students on AI points to a deeper problem: students realize when they’re being asked to develop skills that seem irrelevant or outdated.
In this two-part series, I’ll look at three things:
- The relevance gap and how it can drive inappropriate use of AI in learning
- The risks associated with the unrestrained use of AI
- The right educational approaches to balance traditional teaching strategies with targeted and thoughtful use of AI
The relevance gap: Teaching what matters
A recent report on the use of AI in learning makes for arresting reading. According to the UK’s Higher Education Policy Institute (HEPI), some 92% of students in 2025 are actively using AI – up from 66% in 2024. Of these, 88% have used Generative AI to complete assignments, explain concepts, summarize articles, and even generate text directly in their work.
“These skills involve ways of thinking and feeling and constitute a set of competencies that humans will use to continue creating unique value in the Age of AI.”
How to address this kind of overuse?
I believe the best approach is not to try to restrict access, but to ensure that students believe two things: first, that there is genuine value in the skills they are learning, and second, that they need to be strategic in their use of AI to develop those skills. This approach helps solve the ‘race to the bottom’ incentive problem and essentially eliminates futile efforts to control students’ use of AI outside the classroom. So, what constitutes “genuine” value?
The Future of Jobs Report 2025 from the World Economic Forum offers a clear view of the skills that will remain essential in 2030 and those that are becoming obsolete, providing a framework for establishing educational priorities.
According to the report, there are essential skills that are already important and will become even more vital by 2030. These skills involve ways of thinking and feeling and constitute a set of competencies that humans will use to continue creating unique value in the Age of AI. They include:
- Creative thinking
- Analytical thinking
- Resilience and adaptability
- Leadership and social influence
- Technological literacy
- Curiosity
These skills encourage lifelong learning, and together, form the core of what education should focus on.
The WEF report also identifies “out-of-focus” skills: competencies that are no longer essential and that are unlikely to grow in importance in the future. These include routine reading, writing, and mathematical operations, and are precisely the skills that AI handles increasingly well, making them poor investments of time and energy in education.
When educational experiences focus on developing capabilities that learners perceive as obsolete or easily automated, several predictable behaviors are likely to emerge.
Asked to write a five-paragraph essay following a rigid template or solve a structured math problem, learners will likely recognize that AI can execute the formulaic task efficiently. But when challenged to develop an original argument, synthesize complex information creatively, or demonstrate adaptive problem-solving, learners are more likely to understand that they’re developing capabilities where human beings bring distinctive value.
The widespread use of AI to complete assignments, therefore, often represents not laziness or a lack of integrity in my view, but rather intelligent resource allocation by students who recognize that they’re being asked to practice skills of diminishing value.
Here’s how I see the dynamics at play:
- Rational disengagement – students correctly identify that practicing out-of-focus skills provides minimal future value, leading them to seek efficient completion rather than meaningful learning.
- Strategic resource allocation – rather than investing cognitive effort in developing capabilities that AI handles well, students logically redirect their energy toward more valuable activities.
- Erosion of authenticity – when assignments prioritize formulaic tasks over creative thinking, students lose their connection to their voices and capabilities, making AI-generated content seem more acceptable.
- Learning motivation decline – curricula focused on soon-to-be-obsolete skills fail to inspire genuine curiosity or intellectual engagement, creating environments where shortcuts become attractive.
This creates a vicious cycle where irrelevant or obsolete educational tasks fuel AI dependency, which in turn reduces learner engagement with authentic learning processes.
Redesigning an MBA program around transversal skills
At IMD, we have redesigned our MBA program around a foundation of 10 transversal skills – competencies that cross all conventional business domains and align closely with the core skills in 2030 identified in workforce research.
These 10 transversal skills are:
- Pattern recognition – identifying meaningful connections and trends across complex information
- Systems thinking – understanding how interconnected elements influence broader outcomes
- Visioning and scenario planning – creating compelling futures and preparing for multiple possibilities
- Asking good questions – framing inquiries that unlock insight and drive productive exploration
- Decision making – synthesizing information and choosing optimal paths under uncertainty
- Divergent and convergent thinking – generating multiple possibilities, then synthesizing toward solutions
- Quantifying strategies – translating qualitative insights into measurable frameworks and metrics
- Structured problem solving – applying systematic approaches to complex, ambiguous challenges
- Storyboarding and storytelling – communicating ideas compellingly across diverse audiences
- Leadership presence – influencing and inspiring others through authentic personal impact
Because the IMD curriculum is organized around these capabilities rather than traditional functional silos, our MBA students immediately recognize the relevance and value of their educational experience.
This relevance creates natural motivation for deep engagement, reducing the appeal of AI shortcuts that bypass meaningful learning.
The risks and realities of AI use in education
Meanwhile, emerging research suggests that habitual (over)use of AI tools to complete assignments can cause significant harm to their intellectual capabilities. The most significant concern involves potential atrophy of precisely those skills that remain uniquely human and increasingly valuable.
Students who consistently rely on AI to perform cognitive tasks may lose the ability to perform these tasks independently, creating dependency that extends into professional environments where creative thinking, analytical reasoning, and adaptive leadership prove essential. Understanding these risks becomes crucial when education correctly emphasizes future-relevant capabilities.
Critical thinking deteriorates
When students bypass cognitive effort by relying on AI-generated content for tasks requiring original analysis, they reduce their capacity for independent reasoning and evidence evaluation. This risk becomes particularly acute when assignments properly focus on developing analytical thinking – one of the core 2030 skills signaled by the WEF. Students who consistently use AI to formulate arguments or analyze complex problems often miss necessary practice in logical reasoning, resulting in learners who struggle to distinguish credible information and lack the analytical skills required for academic and professional success.
Creativity and originality decline
Heavy AI dependence can diminish creativity and originality by removing the struggle and exploration that creative thinking requires. Since creative thinking represents perhaps the most essential core skill for 2030, this deterioration proves especially problematic. AI circumvents the vital process of wrestling with problems, revising, and iterating original ideas that support cognitive development. Students may become accustomed to AI-generated ideas and lose confidence in their creative capabilities, missing the iterative process of brainstorming, failing, and refining that builds creative resilience.
The illusion of knowledge increases
Dependence on AI-generated solutions creates an illusion of competence rather than genuine capability development. Students may appear proficient due to polished AI-generated responses, but they may lack authentic mastery of analytical thinking, creative problem-solving, or other core skills. This superficial competence can mask significant learning gaps, as students perform well on AI-assisted assignments but struggle when required to demonstrate capabilities independently.
The use of AI in our education and talent progression systems and processes is only likely to proliferate.
So, where does this leave us?
The use of AI in our education and talent progression systems and processes is only likely to proliferate. And while learners rightly distinguish between the rote and the valuable, the obsolete and the skills that matter most – while they are instinctively and automatically using AI to bridge the relevance gap – there are clear and inherent risks in overreliance on artificial intelligence. So what can educators do?
In my next article, I will explore the 10 things that AI can – and 10 that it must not – do to support learning and skill development in our tech-powered and fast-evolving world.
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