Key points:
- There’s a growing belief that not every challenge should be streamlined away
- Building AI that teaches: Scaling learning systems
- Before students use AI, they should prove they don’t need it
- For more news on AI and skill development, visit eCN’s AI in Education hub
As AI adoption accelerates, a countertrend has emerged that reinforces the old saying: What goes around comes around. After spending years looking for ways to remove friction from everyday life, a growing number of people are embracing it by choosing to perform certain tasks without the assistance of AI and other forms of automation.
Known as “friction maxxing,” the trend reflects a growing belief that not every challenge should be streamlined away. For education leaders and instructors, the trend raises important questions about how to embrace AI-powered tools while ensuring students still develop the foundational skills needed to succeed.
Research underscores the importance
From generating code and summarizing documents to simplifying administrative tasks, AI is rapidly changing how people learn and work. At the same time, its growing role in knowledge work has sparked debate about whether overreliance on these tools could weaken some important foundational skills.
Recent research suggests the question deserves serious consideration. A new study conducted by scientists from Carnegie Mellon, Oxford, MIT, and UCLA found that even brief use of AI reduces persistence and impairment of unassisted performance. Across a variety of tasks, researchers found that while AI assistance improved short-term performance, participants performed significantly worse without AI support and were more likely to give up in their attempts.
Microsoft and Carnegie Mellon raised similar concerns in a joint study examining the impact of generative AI on workplace decision-making. Researchers found higher confidence in AI-generated outputs was associated with reduced critical thinking, while individuals with greater confidence in their own abilities were more likely to evaluate and challenge AI-generated recommendations.
These findings have significant implications for education. Success in fields such as cybersecurity, networking, systems administration, and software development depends on more than producing a correct answer. It requires understanding how systems work, identifying root causes, evaluating risk, and making informed decisions in situations where there may be no obvious solution.
Learning happens through problem-solving
In technical fields, confidence is often built by overcoming challenges and gaining experience. Troubleshooting a network outage or diagnosing a system failure can be frustrating, but those experiences play a critical role in developing the judgment and analytical thinking employers seek and value. The same principle applies in the classroom.
Consider a student learning networking concepts. An AI assistant can quickly explain subnetting, recommend troubleshooting steps, or generate configuration examples. But if students rely on those answers without working through the underlying concepts themselves, they may struggle to diagnose issues independently in real-world environments.
This is where friction maxxing, in an educational sense, can be beneficial. Deliberately working through technical challenges helps students build and retain the ability to actively engage with a problem rather than simply receiving an answer. The goal should not be to avoid AI, but to ensure students understand how to arrive at a solution before relying on technology to accelerate the process.
Finding the right balance in the classroom
It’s important to remember that AI tools deliver meaningful value and will almost certainly play an important role in future IT careers. Students should graduate with experience using AI responsibly and effectively.
The challenge for educators is finding ways to incorporate AI into the learning process without allowing it to replace foundational skill development.
A few practical strategies include:
- Requiring students to explain how they arrived at a solution rather than simply submitting an answer.
- Encouraging students to attempt troubleshooting exercises before consulting AI tools.
- Using AI as a coach that provides hints and feedback instead of a tool that immediately delivers the solution.
- Evaluating problem-solving processes in addition to final outcomes.
- Designing labs and simulations that require investigation, analysis and decision-making under real-world conditions.
Proactive approaches like these allow students to benefit from AI while still developing the technical judgment and confidence needed to succeed in the workplace.
The future still requires human expertise
Artificial intelligence appears poised to play an increasingly important role in both education and the workplace. The question is no longer whether students should learn to use AI, but how educators can integrate these tools without sacrificing the skills and judgment that make human expertise valuable in the first place.
The rise of friction maxxing serves as a reminder that not every challenge should be automated. AI can accelerate learning, improve access to information, and enhance productivity, but expertise is still earned through experience. As schools and training programs adapt to an AI-enabled future, they must strike a balance between leveraging these tools and preserving the productive friction that helps students learn.
James Atria is Senior Live Online Lead Instructor at MyComputerCareer, specializing in IT education and technical training. He leads and develops instructors while delivering hands-on training in networking and cybersecurity, fostering a fast-paced, results-driven environment focused on student success.
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