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Everything You Need to Know About AI in Learning and Development

AI in learning and development: AI handshakeThe 21st century has brought with it many incredible technologies. Some of these were previously only reserved for sci-fi movies. And some we would have never even imagined. Foremost amongst all this tech is artificial intelligence (AI). This is one of the most talked-about subjects at all levels in many industries. It is no surprise that by 2022, companies expect to have an average of 35 AI projects in place.

The goal of AI is to implement human intelligence in machines to create smarter systems. One of the major motivations for AI is to give computers human-like intelligence, such as the ability to solve problems, learn and reason.

In reality, AI is already at work around us. Be it Netflix, which recommends videos based on what you have already watched, or Amazon with books or reminders about items sitting in your basket. Siri, Alexa, and Google Assistant also use AI to provide an enhanced user experience.

Although still in its early stages, 83% of enterprises have increased their budgets for AI and Machine Learning from 2019 to 2020. This highlights that organisations believe AI-powered products to have a very promising future.

As with so many other industries, AI is set to completely transform the learning and development (L&D) landscape. But before we learn about AI in learning and development, let’s start with a brief history of AI.

Are you ready? Let’s go!

The Beginning of Artificial Intelligence

So, how did AI come about? AI didn’t just leap from a checkers-playing programme straight to driverless cars. Like many technological advances, AI has a long history rooted in military applications and patterns statistics, mixed with maths and cognitive science. The original goal for artificial intelligence was to make computers more capable of independent reasoning and, therefore, more useful.

The birth of artificial intelligence can be traced back to a Dartmouth research project in 1956. This project explored topics such as problem-solving and symbolic methods. In the 1960s, the US Department Of Defence took great interest in this type of research and increased its focus on programming computers to mimic human reasoning.

For instance, the Defence Advanced Research Projects Agency (DARPA) produced intelligent personal assistants as far back as 2003. That’s a long time before the likes of Apple, Amazon, or Google developed their own.

However, most of us learned about artificial intelligence through Stanley Kubrick’s “2001: A Space Odyssey”. This movie featured HAL 9000, a sentient Artificial General Intelligence computer.

Now that we’ve talked about virtual assistants and homicidal computers, let’s have a look at what artificial intelligence is.

AI in learning and development: What is AI?

What is Artificial Intelligence?

There are a number of definitions for AI. Professor John McCarthy describes it as “the science and engineering of making intelligent machines, especially intelligent computer programmes.”

In other words, artificial intelligence is a branch of computer science with the objective to simulate and ultimately replicate human intelligence in a machine.

There are two main classes of artificial intelligence:

Weak AI

Weak AI encompasses all of the artificial intelligence currently in existence. It consists of systems and machines designed or programmed to perform a specific task very well. This includes facial recognition tools and self-driving cars. While it recognises faces with greater speed and accuracy than a human, it cannot easily transpose this ability to other tasks (such as recognising houses or cars). Likewise, chess AI AlphaZero can’t suddenly pick another game to be good at.

Strong AI

Strong AI comprises two categories:

Artificial General Intelligence (AGI) or General AI

General AI is capable of mimicking human intelligence and behaviour. It is an ideal goal where intelligent machines are able to perform a broad range of tasks, on a par or better than what a human is capable of.

Artificial Super Intelligence (ASI) or Super AI

Artificial Super Intelligence (ASI) or Super AI surpasses human intelligence in all aspects — from creativity to general wisdom and problem-solving. This is the type of AI that people are worried about. The type that could lead to human extinction. Luckily, this type does not yet exist.

Machine Learning vs Artificial Intelligence

Machine learning (ML) is a subfield of AI and computer science. It allows software to become more accurate at performing a task without having been specifically programmed to do so.

The ML system learns via computer algorithms which internally develop the sought-after capability. As the ML system absorbs more and more data, this capability improves. This is why the importance of Big Data continues to grow.

AI-driven technologies have the ability to enhance our lives, both as learners and workers. Researchers and developers are continuously working on improving them so that they can better mimic human behaviour.

AI technologies can now learn, problem-solve and process language. But whilst they are becoming stronger at mimicking humans, they still lack human traits such as wisdom, insight, humour and empathy. These traits will ensure humans remain an essential part of the workforce for the foreseeable future.

AI in Learning and Development

AI in learning and developmentBusinesses are already using AI to interact with customers and prompt them into action. It’s also used to analyse transactions and detect fraud. Indeed, as organisations continue to invest in AI, the number of applications and use cases have expanded dramatically. It’s up to L&D teams and learning professionals to keep up with these changes and to embrace them as part of their learning programmes.

Some AI tools and applications have already crept into the learning technology space. But the truth is we’ve barely scratched the surface of what’s possible. Other industries have already pulled out ahead.

L&D experts need to explore and implement AI-related improvements to optimise training strategies and techniques. In localised instances, AI has provided opportunities for adaptive learning features. Furthermore, it has shown to improve learner experiences and provide more personalised learning content.

Machine learning will also impact the L&D space by providing it with the ability to spot success patterns, learn from past data, and generate predictions and recommendations.

Let’s dig into the details. Here are eight ways that artificial intelligence will help transform Learning & Development.


1. Personalised Learning Experiences

No two learners are alike, and the way each person acquires and processes information varies. This is why there are so many different learning theories and training approaches. Unfortunately, using human judgement to create personalised learning experiences yields simplified results. As such, this approach is limited in scope and is resource-intensive.

Enter AI and ML, which have the power to recognise many individual learning patterns. As a result, they can progressively find out which type of learning experience or object gives the best result for individual learners. Powerful stuff, right?

2. Workflow Integrated Training

Professionals dedicate 1% of their time to training because actual work takes precedence over learning. This is a disheartening reality for many learning professionals. Luckily, AI can help in two ways:

  • By automatically breaking up training into bite-size chunks.
  • And intelligently incorporating carefully selected training into your employee’s daily routines.

As you might expect, this will help to increase both the intake and the effectiveness of training. Bring on our heavily automated future!

3. Reinforced Learning and Development

Unfinished or forgotten learning interventions are a drain on employee and organisational resources. AI and ML can help in a number of ways. For instance:

  • They can find ways to engage your learners (for example, by using game mechanics).
  • They can automate the learning reinforcement process (through testing and scheduling learning interventions).
  • And they can encourage learners to put their learning into practice through social channels.

4. Searching and Finding

When searching for information on a topic, there is no guarantee that you’ll find something relevant. Thankfully, AI-aided content tagging can help learners find relevant content more easily.

Keywords determine search results. Setting content keywords generates a heavy administrative burden. Luckily, you can reduce this burden with auto-tagging. This helps administrators provide a searchable treasure trove of curated resources for their learners.

AI-based digital coach

5. AI-based Digital Coaches

AI-based digital coaches have already been used very effectively to replace teachers, lecturers, speakers and coaches. They can mimic the expert-novice relationship and learning interactions, support learners with queries and also anticipate and provide resources for learning.

Sounds a bit futuristic doesn’t it? Well, the future is here. Duolingo already uses chatbots to replicate the coaching experience. And Thinkster Math is a platform with integrated AI that automates the tutoring process.

6. Improved Completion Rates

Despite growing training budgets, employees still drag their feet when it comes to attending training programmes.

But if you’ve got an AI-powered learning programme that recommends the most relevant content and intelligently links it to virtual rewards (such as Badges and XP), you’ll soon see interest levels grow.

Before you know it, you’ll start to see your completion rates creep up. Furthermore, you’ll see an increase in behaviour change. As a result, you’ll be able to deliver better business outcomes.

Measuring training effectivness

7. Learning and Training Effectiveness Measurement

Up until now, evaluating the performance of a learning programme has been very difficult. It’s often conducted after the initiative is complete, rather than during (when it would be most effective). When using AI/ML, evaluation is an integral part of the recommendation and personalisation process, and works both with historical data and real-time data.

Programme performance, learning object performance, and learner performance can all be dissected and linked to business results, providing the ultimate learning decision tool. This enables learning professionals to refine their training programmes as they go, rather than just crossing their fingers and hoping that they got it right.

8. Universality

AI/ML adapts its recommendations to all employee backgrounds, levels of education and types of personalities. It effortlessly tailors content for many different groups based on the data it has to hand. It can even process content for use by learners with disabilities or for those who speak a different language.

Automating this process will save L&D professionals time and prevent damaging knowledge silos forming throughout your organisation.

What are the Ethical and Practical Issues We Should Consider?

Algorithmic Bias

Instead of saying “garbage in, garbage out”, AI/ML specialists should say “bias in, bias out”. AI/ML is only as smart as the data it’s fed allows it to be. If this data is skewed, it can damage the results or outcomes of your programme. For instance, if your data is skewed to favour a particular gender, race or educational status, your machine learning software may interpret this as a determining feature.

To counter this, you should utilise machine learning systems with anti-bias features. This might include options to weigh opinions differently according to hierarchical levels or creating recommendations by category group.

You should also ensure someone checks the data used for ML, identifies potential sources of bias, and then scans results for bias. Finally, you will need to educate your analysts about how your machine learning system works, how to interpret its output and when to take the results with a pinch of salt.

Privacy and Security

AI/ML solutions require a lot of data to work effectively. A lot of this data will have to come from your learners. Some of this data is private and should be treated as such. This means applying all the required privacy measures and adhering to any relevant regulations.

Furthermore, none of this data should fall into the hands of people who don’t need to have access to it, whether internally or externally. Therefore, you should seek to take all measures that make your data secure.

False Information

To get the most benefit out of the system, employees should be encouraged to contribute accurate, up-to-date and truthful information. As such, you’re reliant on your end users to feed the machine with the right data. After all, inaccurate data will impact your results. Therefore, you should handle, clean, sort, connect and share your data with care to retain its accuracy.


You should be fully transparent and disclose what data is collected for the AI and what decisions are made with it. If an employee is dealing with an AI (rather than a human), you should let them know. And you should tell them how the algorithm works.

In addition, most countries require you to disclose what employee data you keep. You should also inform them about their right to correct or delete their data.

Honesty is always the right policy. Furthermore, this transparency will help you to encourage your learners to provide accurate information.


For the system to work, employees need to be comfortable about it and feel that they can trust their organisation to use the information wisely. The system will collapse if your learners find out that it is being used aggressively to promote, sideline or even dismiss employees.

Final Words

AI in learning and development is still in the early stages of adoption. It will require a significant commitment and resources from organisations and L&D departments. On the bright side, the potential contribution to learning results is enormous.

In fact, if you don’t embrace AI/ML, you risk getting left behind. NewVantage Partners have found that 97.2% of organisations are investing in big data and AI/ML initiatives as they seek to become nimble, data-driven businesses. Don’t be part of the 2.8%!

So far, we have highlighted the ways in which AI/ML will transform learning and development. In a subsequent article, we will explore how AI/ML can be applied within LMSs and other learning systems.

Stay tuned!

Artificial intelligence and machine learning can perform valuable tasks which are nearly impossible for humans. If you want to benefit from these and reduce the time and effort to do so, contact us today to find out how the Growth Engineering Impact Suite can help you!

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