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AI and Education: 10 ways to support or erode future skills resilience

Published October 22, 2025 in Talent • 9 min read

AI can boost critical thinking or undermine it. Educators and talent leaders must learn to navigate this fine line.

The use of generative AI among learners has increased by 66% over the last 12 months. More than 90% of young people in higher education are now using ChatGPT and other tools to research, summarize, and complete their work, while educators are increasingly leveraging the same tools to create and assess assignments.

In my previous article I touched on some of the risks attached to the unchecked overuse of AI in learning, among them the potential to harm learners’ critical thinking, logical reasoning, creativity, originality, and genuine mastery of new knowledge and competence.

AI poses both genuine benefits and very real hazards to the education and development of our future talent. Key to its responsible use is designing AI integration that enhances – without substituting – the development of relevant core and emerging skills; human skills that will be critical to organizations in our AI-powered future.

So, how to do this?

Thoughtfully designed AI tools should possess certain characteristics that support rather than replace learning.

AI support for learning: 10 things AI should and should NOT do

Thoughtfully designed AI tools should possess certain characteristics that support rather than replace learning. Among them:

  • Active learning support for core skills – rather than providing direct answers to creative or analytical challenges, AI should prompt students with increasingly sophisticated questions that encourage deeper engagement with complex problems and develop critical thinking skills, creativity, and problem-solving.
  • Personalized feedback on core capabilities – AI should deliver immediate, individualized feedback on specific aspects of creative thinking, analytical reasoning, or adaptive problem-solving without removing the need for students to develop these capabilities independently.
  • Scaffolding for skills development – AI should provide frameworks and guidance for developing core 2030 skills, while helping students understand how to work effectively with AI systems, thereby maintaining human judgment and creativity.
  • Metacognitive development – AI tools can prompt students to reflect critically on their learning processes, fostering deep self-awareness about their development of transversal skills and future-relevant capabilities.
  • Creative stimulation rather than creation – AI should provide diverse initial prompts or challenges that help students overcome creative blocks while ensuring they develop original thinking and innovative problem-solving abilities.
  • Critical evaluation training – students can benefit from AI presenting flawed or biased responses that they must critically assess and improve, cultivating the analytical thinking skills essential for future success.

Focusing on what AI can and should do in terms of education and skills development is one part of the story. The other is understanding what AI tools should not be doing.

Educators must ensure that core critical thinking, creative, and problem-solving skills – those outlined by the World Economic Forum for 2030 – remain strictly prioritized. That means that AI should never complete creative thinking tasks, analytical reasoning challenges, adaptive leadership scenarios, or other core skills where students need to develop their distinctive human contributions. AI should not replace learning experiences designed to build capabilities where humans continue to add distinctive value across multiple domains.

What’s more, AI must not be permitted to eliminate cognitive struggle. It must not substitute the productive difficulty and iterative refinement necessary for developing resilience, creative problem-solving, and analytical thinking. Nor should AI in any way undermine future-relevant learning needs.

The limitations of AI tools used for education, training, and skills development should always be communicated transparently. And it falls to educators to help learners understand which capabilities they must develop to continue adding distinctive human value in an AI-integrated world. There are, I believe, 10 things that AI support tools should – and should not – do.

The 10 things AI should and shouldn’t do to support learning  

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Finding the right balance: the 75/25 principle

Equipping our future talent with core cognitive and creative skills means continuing to focus on traditional teaching methods.

I believe an ideal scenario integrates between 70 and 80% traditional teaching practices and 20% or so AI tools. Traditional methods should ideally represent 75% of what goes on in the classroom: best-in-class learning experiences that develop core skills, including creative thinking challenges, analytical reasoning tasks, adaptive leadership scenarios, complex problem-solving projects, and collaborative innovation exercises. 

Traditional teaching techniques include:

  • Discussions that build critical thinking
  • Hands-on activities that develop resilience
  • Peer collaboration that enhances social influence
  • Independent research that cultivates curiosity and lifelong learning

Meanwhile, AI integration should represent 25% of teaching and learning, strategically supporting future-relevant skills development through:

  • Immediate feedback on specific aspects of creative or analytical work
  • Scaffolding for developing human-AI collaboration capabilities
  • Metacognitive exercises that build self-awareness about skills development
  • Diagnostic tools that identify gaps in core competencies
  • Practice environments for technological literacy and AI fluency

This 75/25 distribution ensures that students develop capabilities where humans add distinctive value while gaining fluency in working effectively with AI systems – developing graduates who are prepared for an AI-integrated future, and who are not in danger of being replaced by technology.

Future resilience, prosperity, and longevity in our fast-evolving world will depend on this.

Rethinking executive development

The 75/25 principle applies to higher education. It also applies to executive development.

As AI reshapes business operations, organizations face the same fundamental challenge as educational institutions: ensuring that their leadership development efforts focus on capabilities where humans continue to add distinctive value in a technology-powered future

For decision makers, this will mean a comprehensive audit of traditional leadership programs: programs which often emphasize data analysis, routine strategic planning, and standardized decision-making frameworks – skills that AI is already arguably performing more efficiently than human beings and at scale.

I believe there is now a real onus on CHROs and learning and development leaders to redesign leadership curricula instead around the transversal skills identified in the WEF report: namely, creative thinking, adaptive problem-solving, and the ability to inspire and influence others through authentic leadership presence.

Future resilience, prosperity, and longevity in our fast-evolving world will depend on this.

Successfully implementing this approach requires deliberate curriculum design that prioritizes future-relevant capabilities while thoughtfully integrating AI as a learning enhancement tool.

Applying the 75:25 principle

Adapting the 75:25 rule to executive development follows broadly the same idea as higher education: 75% of leadership training should focus on uniquely human capabilities – navigating ambiguous situations, building trust across cultures, facilitating innovation, and making ethical decisions under uncertainty.

The remaining 25% should teach leaders how to work effectively with AI systems as strategic partners rather than threats: case studies, simulations, and the like should require participants both to collaborate with AI tools while also demonstrating uniquely human judgment. Assessment criteria must emphasize adaptive thinking and emotional intelligence, along with the ability to inspire teams through periods of technological disruption.

What does this look like in practice?

Successfully implementing this approach requires deliberate curriculum design that prioritizes future-relevant capabilities while thoughtfully integrating AI as a learning enhancement tool.

humans playing tug-of-war with a robot artificial intelligence
Companies that fail to adapt their leadership development programs risk cultivating executives who compete with AI rather than leveraging it

Practical implementation strategies

Auditing curriculum for future-ready skills relevance

Educational institutions should systematically evaluate their programs using the core 2030 skills framework, asking critical questions about each component:

  • Skills assessment: Does this learning experience develop core skills in 2030 or out-of-focus capabilities? Priority should strongly favor activities that build creative thinking, analytical reasoning, leadership, technological literacy, and other future-ready skills.
  • Value-added evaluation: Would completing this task well require capabilities where humans add distinctive value, or could AI handle it effectively? Educational time should focus on developing areas where humans maintain competitive advantages.

Incentivizing engagement with relevant learning

Rather than attempting to restrict AI usage through enforcement, which mostly proves futile, educators can create incentives and structures that make meaningful learning more attractive than AI shortcuts:

  • Structure assignments around demonstrating 2030 core skills rather than completing formulaic tasks makes AI-generated responses inadequate for success.
  • Require students to document their creative thinking processes, analytical reasoning steps, and iterative problem-solving approaches, emphasizing development over final products.
  • Connect learning experiences to real-world scenarios where students recognize the practical value of developing capabilities where humans add distinctive value.
  • Assign projects that require genuine human interaction, creative synthesis, and adaptive leadership, where humans continue to add distinctive value.

Aligning assessment to future-relevant skills

Assessment should explicitly evaluate student development of core 2030 skills rather than knowledge recall or formulaic task completion:

  • Track student growth in creative thinking, analytical reasoning, and other transversal skills over time, making authentic development visible and valuable.
  • Create scenarios requiring students to demonstrate adaptive problem-solving, innovative thinking, and collaborative leadership in real-time.
  • Include regular reflection components that allow students to analyze their development of future-relevant capabilities and plan for continued growth.

The shift that I have broken down here requires more than a curriculum adjustment. It demands a fundamental reimagining of what leadership means in an AI-integrated world. And it must be a priority for organizations today.

Organizations must recognize that future leaders need to excel at the intersection of human insight and artificial intelligence – combining technological fluency with irreplaceable human capabilities like empathy, creative vision, and authentic influence.

Companies that fail to adapt their leadership development programs risk cultivating executives who compete with AI rather than leveraging it.

Ultimately, educators hold both the opportunity and responsibility to ensure higher education and executive development remain genuinely valuable in an AI-integrated world.

Education that matters in an AI-powered world

The path forward requires fundamental commitment to teaching skills that genuinely matter for students’ and employees’ future outcomes – and for the development of robust and future-resilient talent pools, leadership pipelines, and succession plans

Whether in the higher education classroom or executive training program, education must emphasize creative thinking, analytical reasoning, adaptive leadership, technological literacy, and other capabilities where humans add distinctive value, to drive the engagement that follows when learners recognize the value of the material assignments and pedagogy used.

This relevance-focused approach transforms the AI challenge from a restriction problem into an opportunity for enhanced learning. Learners develop capabilities where humans add distinctive value and understand that AI can support, but never replace, their need to master these skills. Meanwhile, thoughtful AI integration can accelerate skills development and provide valuable technological fluency.

All of this will require courage to abandon traditional content that emphasizes out-of-focus capabilities in favor of learning experiences that develop the creative, analytical, and adaptive thinking students will need throughout their careers.

The 75:25 balance between traditional methods and AI integration works effectively only when the 75% focuses relentlessly on developing capabilities where humans add distinctive value. When students understand they’re developing irreplaceable skills rather than practicing obsolete tasks, they naturally choose meaningful engagement over AI shortcuts.

Ultimately, educators hold both the opportunity and responsibility to ensure higher education and executive development remain genuinely valuable in an AI-integrated world. By focusing on what students truly need to learn and using AI thoughtfully to enhance, rather than replace, that learning, we can prepare tomorrow’s talent to thrive alongside AI, rather than compete unsuccessfully against it.

Authors

Michael Watkins - IMD Professor

Michael D. Watkins

Professor of Leadership and Organizational Change at IMD

Michael D Watkins is Professor of Leadership and Organizational Change at IMD, and author of The First 90 Days, Master Your Next Move, Predictable Surprises, and 12 other books on leadership and negotiation. His book, The Six Disciplines of Strategic Thinking, explores how executives can learn to think strategically and lead their organizations into the future. A Thinkers 50-ranked management influencer and recognized expert in his field, his work features in HBR Guides and HBR’s 10 Must Reads on leadership, teams, strategic initiatives, and new managers. Over the past 20 years, he has used his First 90 Days® methodology to help leaders make successful transitions, both in his teaching at IMD, INSEAD, and Harvard Business School, where he gained his PhD in decision sciences, as well as through his private consultancy practice Genesis Advisers. At IMD, he directs the First 90 Days open program for leaders taking on challenging new roles and co-directs the Transition to Business Leadership (TBL) executive program for future enterprise leaders, as well as the Program for Executive Development.

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