How to Use AI in Learning & Development

Incorporating AI into L&D isn’t just a tactical move—it’s a strategic step toward building a future-ready workforce that can adapt, innovate, and thrive in a rapidly evolving environment.  

But to get the best results L&D needs to understand both how to use AI in learning and development and where AI’s limitations lie.  

For example: The opportunities to scale content creation and personalisation are huge. AI’s ability to automate routine tasks can free up L&D professionals for more strategic work, and its capability in testing and evaluating learning is impressive.   

However, while many L&D professionals are embedding AI in learning strategies others are proceeding with caution. Concerns about data privacy, implementation and declining social learning are valid.   

This article explores both the advantages and disadvantages of using AI in learning and outlines how to enhance L&D while minimising risks. Ultimately the aim of AI technologies is to enhance, not replace human-led learning initiatives, but to do so L&D need some foundational skills in AI themselves. 

While AI brings many opportunities to organisational L&D, there are concerns and possible cons of AI in learning and development. Let’s explore. 

Security and Privacy Concerns 

A primary concern relates to data security and privacy. AI systems require substantial data to function effectively, and learner data is sensitive. So being clear on how L&D information is stored, processed, and protected is key when assessing AI enhanced systems. 

This is one scenario where using a specific AI-enabled LMS outperforms open access AI tools. ChatGPT, Claude.ai et al may process data on external servers, whereas purpose-built L&D solutions such as those powered by Access Evo use a closed system approach. AI-enabled LMSs are more likely to offer enhanced privacy protections as well as support regulatory compliance.  
 

Accuracy and Content Quality Issues 

AI systems are only as good as their training data: every general purpose large language model comes with a warning to factcheck for hallucinations. Inaccurate or misleading content is particularly problematic in learning contexts, as incorrect information could adversely impact learner development and therefore job performance. 

Additionally, AI cannot replicate the flair, empathy and unique qualities that people bring to learning content, and this can lead to bland, uninspiring content that garners low learning engagement. 

There are three mitigations here, all related to the importance of human input and oversight: 

• Training materials may be drafted at speed – by they need to be verified by L&D for accuracy and robustness; 
• Using subject matter experts to help create training is a win-win – they can use AI to create the materials faster and are best placed to catch any hallucinations as well as bringing their unique knowledge. 
• Ensure learning content outputs are enhanced to light up your learners’ engagement by the addition of video case studies of real people, and utilisation of story-telling techniques. 

Ethical Considerations and Bias 

AI systems can inadvertently perpetuate biases present in training data, potentially leading to unfair learning recommendations or assessments. For example, AI might consistently suggest certain training to specific workforce demographics based on encoded biases or historical patterns, rather than assessing the individual’s interests and potential.  

Addressing these risks requires ongoing monitoring of AI recommendations, combined with diverse training data and explicit fairness criteria. Using AI systems that have been specifically developed for HR/L&D use cases may help mitigate the risk. 
 

Diminished opportunities for social learning 

Many organisations and sectors rely on social learning to embed cultural values and transfer knowledge. The UK’s legal profession mandates that would-be solicitors complete a two-year training contract which is usually done on-site, so fledgeling lawyers can learn from established partners.  

Although most of the workforce is familiar with virtual learning – especially post-pandemic - some L&D practitioners believe excessive reliance on AI could diminish the opportunities for social learning. The ramifications extend across the whole of HR’s remit: social learning helps foster a culture of belonging at work and a culture of learning, aiding talent retention, attraction and employee engagement.  

Modern digital learning solutions can effectively address some of these concerns with creating opportunities for human connection in learning. This can be achieved through: 

1. Interactive online experiences: Utilise live virtual classrooms or team-based gamification to encourage collaborative learning and friendly competition. 
    
2. Social recognition: Implement organisational social networks to enable managers and leaders to easily recognise and celebrate learning achievements, fostering peer engagement and social encouragement. 

Implementation Complexity and the need for foundational AI Skills 

Introducing AI systems into established L&D processes can be complex. And the fears about AI replacing people’s jobs have sparked resistance to using it. Additionally, the learning curve associated with AI has proved to be steeper than many originally imagined, contributing to the sense of ‘hype’ around AI.  

Successfully implementing AI-enhanced learning strategies requires thoughtful change management processes, clear communication about the benefits and phased deployment approaches that allow people to adjust over time. 

Mastering prompt engineering across different open-access LLMs as well as learning platforms that have been enhanced with AI will be a critical foundational skill for L&D departments. That itself is a challenge. But it is also an opportunity – L&D can lead by example and help role model the change the wider workforce needs to make. 

Starting small and scaling gradually is often most effective for integrating AI into your L&D strategy. When you are ready to roll out across the whole organisation leverage change management techniques to bring your workforce along with you.  

Consider these steps to begin piloting AI in your learning strategy: 

• Identify specific pain points or inefficiencies in your current L&D processes that AI might address.
  • What are your biggest challenges?  
  • Content creation, curation, or personalisation?
  • Tying learning to people analytics and business outcomes? 
  • Evaluating knowledge acquisition and application?  
  • Identify some pain points then run a pilot to see how well AI solves them.  
• Experiment with general-purpose AI tools for simple tasks like drafting course completion reminder emails. Evaluate how well they create the content, does it read well, is it personalised, is it engaging?  
• Research specialised digital learning solutions with AI capabilities, and note where general-purpose AI can help and where an AI-enhanced L&D platform outperforms and can help you fulfil your strategic goals. 
• Develop clear guidelines for when and how AI tools should be used, and specify when they are not appropriate for learning – for example, not relying solely on AI to create eLearning materials without the input of subject matter experts and their industry-specific knowledge.  
• Invest in upskilling the entire L&D team to work effectively alongside AI technologies – this is foundational upskilling that will enable the upskilling of the entire workforce using AI.  
• Establish metrics to measure the impact of AI on learning outcomes, key people metrics, plus the efficiency and cost-effectiveness of the L&D department. 

AI’s promise for learning and development is great. From speeding up content creation, curation, personalisation and dissemination to drawing business insights from training data and aiding strategic workforce planning, the opportunities are many. And because using AI can reduce L&D’s administrative burden it frees learning and development professionals to pursue these strategic opportunities, adding more value to the business while creating impactful, engaging learning experiences. 

While challenges exist around data security, content quality, ethical implementation, and the impact of virtual learning on social learning these can be addressed through thoughtful planning, selecting the right tools, and providing clear direction on where AI is appropriate in learning and development and where it is not.  

There is still plenty of work to do that only people can do. Make sure your people are ready to tackle the future of work, whatever it looks like, with the right L&D strategies and tools for your organisation.