AI and the Future of Work – Reskilling Beyond the Classroom
- Brainz Magazine
- Jun 17, 2020
- 5 min read
Most people remember classrooms that prioritized grades over practical skills. I also experienced those classes, surrounded by classmates with varying ambitions. Some wanted to create apps or launch businesses, while others had no specific direction. Despite our different aspirations, everyone received the same assignments, took the same tests, and moved at the same pace.
As technology and labor markets change faster than curricula can adapt, professionals and organizations face an urgent need for more flexible approaches to skill development. Artificial intelligence (AI) offers a way forward by linking education directly to the workplace.
Instead of limiting learning to the classroom, AI and reskilling can go hand in hand, enabling people to build new skills while they work.
The myth of the classroom: Why traditional learning falls short
Traditional classrooms move everyone through the same material at the same speed. Growth rates and starting points, however, are never identical.
Four-year degrees, yearly certifications, and the occasional company workshop were designed for slower times. These cycles simply can’t keep up with waves of new tools, AI upgrades, and process changes.
The most important change AI brings to education is its ability to support “just-in-time” learning. This means that growth happens during work, rather than being confined to a physical or virtual classroom.
Research on AI in education describes a system that layers content, data, and intelligent algorithms. Think of it as a stack where adaptive tools respond to the context and needs of each learner.
Modern AI-driven platforms connect people, give timely feedback during real tasks, and adjust to the learner’s current situation. This is quickly becoming the new standard for continuous learning.
“Education will be profoundly transformed by AI. Teaching tools, ways of learning, access to knowledge, and teacher training will be revolutionized.” – Audrey Azoulay, UNESCO Director-General
Reskilling at scale: Matching skills, people, and opportunity
AI is changing job requirements so quickly that retraining has become an ongoing need rather than an occasional project. McKinsey projects that physical and manual skills will remain widespread in 2030 for many regions, but demand for technology, critical thinking, and creativity is expected to surge. The change in hours spent advancing technological skills, in particular, could grow as much as 55%.
Standard retraining programs typically send everyone through the duplicate content, overlooking what makes each person unique. They miss the mix of abilities and ambitions within teams, and can’t adjust as roles shift.
My experience with peer-driven learning showed the value of personalization. For my final year project at university, I worked on rating and categorizing online tech tutorials. I used feedback from computer science students to recommend the most valuable resources to each learner. The project demonstrated how targeted guidance can help students direct their own growth.
AI-enabled tools, when utilized well, allow organizations to support data-driven career growth. They can map the abilities of workers, then connect them to learning paths that fit their own strengths and ambitions. This process uncovers overlooked talent, recognizes potential beyond credentials, and creates new possibilities for people regardless of their starting point.
Lightweight, network-based models: Practical, scalable solutions
Corporate training is often expensive and slow to adapt. When learning is designed around people’s actual needs and daily work, it becomes much more relevant and practical. AI is making it possible to scale these ideas.
A study on artificial intelligence in education (AIEd) details practical tools now in use. These tools lower costs, speed up learning, and make it possible to personalize education:
Chatbots that provide quick answers or guidance. They work best when combined with other methods, since engagement can drop over time.
Expert systems that analyze learner data to plan and guide learning. They make management systems more responsive and effective.
Intelligent tutors and agents that provide feedback and support on real work, helping people tackle more complex problems.
Personalized learning environments that adjust pace and direction, increasing engagement and results.
Virtual learning environments that build realistic, hands-on scenarios for group practice.
AI competence itself can be built step by step. The TPACK framework highlights how elements like AI agents, unplugged activities, and hands-on learning can support growth across levels, from basic awareness to advanced understanding. Companies can use this integrated approach to build skills in context, without interrupting daily workflows.
I saw these ideas in action when I founded Learning Loop, an online community for startup founders. Our team combined software, people, content, and experiences in one package for a focused group. This helped us understand where the process slows down and why expanding this model needs new forms of support.
Innovations like these signal a future where education is adaptable, practical, and closely tied to the daily workflow. Lightweight learning models make it easier for people to build new skills and adapt to change.
Global lens: Opening access to talent everywhere
AI-driven learning is no longer limited to the world’s biggest tech hubs. Most of the growth in AI-powered upskilling now happens outside Silicon Valley. Students and professionals in Southeast Asia, the Middle East, and Africa are using flexible education tools to learn and build careers that once seemed out of reach.
AI-powered tools and flexible education models connect learners and mentors across locations, enabling people outside traditional centers to access practical skills and job opportunities. This benefits both individuals and companies, who can now find and support talent regardless of geography.
Human potential: AI as a partner, not a replacement
The “humans versus machines” frame is misleading. People and AI bring different strengths, and progress depends on organizations combining these effectively. Leaders should focus on developing collaboration skills and helping managers understand when to trust data and when to rely on human judgment.
Traditional systems often reduce potential to grades or job titles, overlooking much of what people could achieve. This can be reversed with thoughtful use of AI for highly personalized growth.
History also shows why balance matters. Looking back, each wave of new technology has disrupted work, but has also created new opportunities. Preparing people to adapt is far more helpful than predicting collapse.
Conclusion: Building a future of continuous learning
The future of work depends on combining human potential with AI’s ability to personalize and adapt learning for everyone. While traditional education provides a starting point, skills now need frequent updates as technology and job requirements change.
My vision is for a global reskilling ecosystem with AI at its core. This interconnected system enables professionals to receive continuous, personalized upskilling directly within the flow of their work. Companies can track real capability gains right where the work takes place. Policymakers, in turn, can support flexible credentials and open data standards that facilitate easier movement between roles and industries.
By applying these principles and moving beyond classroom limitations, we can expand the global talent pool and better prepare the workforce for the future.
About the Author:
Sina Meraji specializes in designing systems that connect continuous learning and AI-driven collaboration to real-world outcomes. He has managed teams building AI platforms for workforce training and quality improvement, particularly on practical methods for closing skills gaps across diverse groups. Sina has spoken about the future of education and workforce development at TED Talks and at the International Association of Universities Plenary Session.
References:
Chen, L., Chen, P., & Lin, Z. (2020, April 17). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278.
How can artificial intelligence enhance education? (2019, February 18). UNESCO.
Manyika, J., & Sneader, K. (2018, June 1). AI, automation, and the future of work: Ten things to solve for. McKinsey Global Institute.
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education Artificial Intelligence, 2, 100041.
Romeo, J. (2020, January 20). In search of (artificial) intelligence. HR Executive.
De Cremer, D., & Kasparov, G. (2021, March 18). AI should augment human intelligence, not replace it. Harvard Business Review.
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Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018, May 23). Skill shift: Automation and the future of the workforce. McKinsey Global Institute.
