Future of Work: Can AI Make Personalized Learning a Reality?

Key Takeaways

• AI’s Role in Personalized Learning: AI can deliver highly targeted learning experiences by providing relevant content exactly when it's needed, reducing time spent on already known material, and guiding learners through personalized pathways that enhance engagement and mastery.
• Rethinking Higher Education with AI: AI-driven pedagogical models, like those used by Minerva University, are reshaping higher education by enabling more effective, feedback-intensive learning. AI frees educators to focus on mentorship, enhancing both student learning and institutional scalability.
• AI in Upskilling and Performance Support: In the workplace, AI can improve upskilling and onboarding by providing real-time, on-demand support, fostering deliberate practice in virtual environments, and offering personalized coaching to boost employee performance.

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At Quench, our dream is "to inspire everyone we touch to be the best they can be".

We know how important personalised support can be to help individuals realise their full potential. When Husayn Kassai, Quench.ai's founder, arrived in the UK, a generous teacher spent every afternoon after school helping him learn English. Just eight years later, Husayn would go on to secure a place at the University of Oxford.

For many, a transformative teacher or mentor can be life-changing, yet this level of support remains a privilege, not a norm. At our recent AI & The Future of Work Summit, industry leaders grappled with a tantalizing question: Could generative AI democratize this level of personalized learning?

How do we effectively learn? 

First, we need to rethink what we mean by learning. Ben Nelson, Founder of the Minerva Project, made an important distinction: “Learning is not knowledge, rather it is knowing what to do with knowledge”. “There’s only one way to learn,” he continued, “spaced, deliberate practice, at desired levels of difficulties across context.” 

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“Learning is not knowledge, rather it is knowing what to do with knowledge”

Ben Nelson, CEO, Founder, and Chairman at the Minerva Project

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We should not equate learning with content and there is compelling empirical evidence which supports the importance of active learning approaches. Ben Nelson highlighted that students recall only 5-10% of lecture material after six months, whereas active learning approaches result in 70% retention even two years later.

Whilst the advent of GenAI has made it possible to create content faster than ever, the evidence shows it takes more than content to build mastery and we must explore the applications of AI beyond content creation. 

How might AI deliver Personalized Learning?

How might AI accelerate the shift from passive content consumption to interactive, tailored learning experiences? 

Egle Vinauskaite, Director at Nodes, highlighted two critical functions of AI-powered personalized learning:

1. Solving relevance issues: providing learners with precisely what they need, when they need it
2. Saving time: reducing the time spent reviewing things learners already know

There are three potential levels of personalisation that AI can deliver:

1. Basic Interaction: Users can interact with content through natural language queries and receive immediate responses
2. Advanced Curation: AI curates content based on user interest and provides additional information via search functions
3. Personalized Pathways: AI guides entire learning and career pathways leading to promotions or career transitions
   

However, we must remember that personalization is not merely about navigating content more efficiently. The fundamental consideration must be the individual's motivation for engaging with learning in the first place, whether that's saving time, increasing earning potential, or enhancing professional status. 

Reimagining University Education with AI

AI is reshaping higher education by enabling new pedagogical approaches designed for the post-AI era. For instance, Minerva University, founded by Ben Nelson, has integrated AI into its feedback-intensive learning model to address a key educational challenge known as 'far transfer' - the ability to apply knowledge in new contexts. Traditional teaching methods often struggle to address this, but Minerva students work towards over 80 learning objectives in the first year alone through a rigorous evaluation process.

This approach is already bearing fruit. Ben Nelson cited that Minerva graduates achieve higher graduate school placement rates higher than top Ivy league institutions, and 12% of Minerva graduates have gone on to become founders of funded startups, compared to less than 1% at Stanford.

This use of AI highlights its potential to personalize learning and support skill development in ways that were previously impractical. By reducing the reliance on human grading, AI frees up educators to focus on mentorship and innovation, paving the way for a more effective and scalable education system.

AI in Workplace Learning

Upskilling

There is great potential for GenAI to enhance upskilling through deliberate practice in a sandbox - a virtual environment where employees can experiment and develop their skills. The sandbox can facilitate conversation simulation, such as sales calls or managing difficult conversations, to provide the employee with real-time feedback on their performance. Egle Vinauskaite referenced one consulting firm whose aim was to improve the retention of their clients. They achieved double digit performance improvements after using AI to analyze the transcripts of their highest performing employees to create coaching playbooks for other employees.

Performance Support

Performance support faces unique challenges, particularly around data security and the accuracy of AI-generated responses to procedural queries. However, there is optimism that AI can significantly improve the employee onboarding process. Rather than requiring highly skilled professionals to sit through two to three week onboarding programmes, AI enables a more dynamic approach by integrating them right into work and supporting them with on-demand support precisely when needed.

How to develop a strategic approach to AI For L&D?

Despite the potential of GenAI to enhance upskilling and performance support, it may come as a surprise that most L&D departments have not yet fully integrated it. In a survey of 300 L&D professionals globally published in 2024 by Egle Vinauskaite and Donald Taylor, 45% of L&D practitioners were either taking no action or were only experimenting with AI.

There are legitimate concerns, having access to more powerful learning tools does not simply lead to better learning outcomes, and L&D leaders must audit AI tools to ensure that we do not get misled by solutions that do not solve actual problems.

However, in conversation with David James, Chief Learning Officer at 360Learning,  Egle suggested that using AI has become a competitive imperative and she recommended a three-pronged approach to incorporate AI into L&D:

1. Experimentation and Adaptation: Continually experiment with AI to discover your own use cases and skills gaps. You can also map out your processes to understand where AI’s benefits sit for you and your organization. 
2. Targeted use of AI: To ensure you know where the business value of leveraging AI may lie, you need to have a firm understanding of your department’s functions, key performance indicators (KPIs), and priorities.
3. Build the infrastructure: Prepare for AI’s broader use within your team and organization by creating a data capture and analysis infrastructure to leverage AI effectively today and in the future.

Get Expert Guidance on Harnessing AI

Our team is here to help you make the most of AI in your workplace and support you on your journey!

If you’d like to learn more about how to integrate AI more effectively in your organization's learning and development strategy, feel free to book a call with one of our team members.