AI Tools for L&D – Examples & Uses

Artificial intelligence tools for L&D

Artificial Intelligence Tools for L&D – Examples & Uses

The advent of artificial intelligence brings about significant analytical power that corporate L&D can take advantage of. While the AI technologies are generally nothing new, the significant increase in computing power has made the rapid development of recent years possible. Whereas the strong all-powerful AI remains a dream, there are a lot of practical applications for the technology. Here are 3 examples and specific use cases for different AI tools for L&D.

Recommendation engines & algorithms

One of the most commonly implemented AI tool in L&D is a recommendation engine. Most often used for recommending content, the engine analyses the context of an individual learner, and aims to offer a personalised curation of learning resources based on the materials given. However, it’s worthwhile to note that these types of recommendation engines have existed for long, even without AI.

Whereas content recommendation works on a relatively micro level, it’s possible to use the same principles on a wider spectrum. Some of the more advanced recommendation algorithms and AI tools don’t just recommend content, but can also extend to recommend different interventions and courses of action for the L&D team. For instance, the algorithms can provide suggestions on learning paths for different groups.

Grouping algorithms

Another great example of AI tools suitable for L&D are grouping algorithms. While they constitute a very basic form of machine learning, these algorithms can be a powerful tool. Essentially, what the algorithms do is they analyse different individuals or groups (e.g. business units, departments, locations) and their attributes. For instance, the algorithms could detect groups with similar recommended learning paths. Consequently, the L&D could use these inter-organisation groups as basis for organising learning, rather than arbitrary division.

Furthermore, another use case is to use similar grouping algorithms to group people based on their ability. This type of use would detect individuals’ and their groups’ common existing capabilities, and propose reorganisations based on that. In practice, this would enable further personalisation of learning by dividing the organisation into groups, and offering each group the optimal difficulty and degree of content.

Predictive analytics and modelling

Another great use of AI tools for L&D is on the analytics front. While there are several uses for learning analytics, AI makes possible more than what we are used to. Instead of simply reporting descriptive analytics, AI enables us to get into diagnostic, predictive and prescriptive analytics. Diagnostics generally aim to answer why certain things happened (i.e. why did learning results drop). While that in itself is incredibly valuable information from an organisational development perspective, there’s still more to unlock.

Predictive analytics enable us to answer questions about potential impact (e.g. “what will happen if we get learning engagement to increase by 20%?”). This enables organisations to run “what if” analysis and supports them in identifying the areas of L&D where it’s possible to make the most impact. Prescriptive analytics, on the other hand, do a similar thing, centring around inputs (e.g. what do we need to do to raise learning engagement by 20%). While these kind of analytical powers require significant commitment in measurement and defining relevant parameters, they provide a tool for L&D to demonstrate its impact to the business that hasn’t been around before.

Final words

While AI is currently suffering a slight inflation thanks to its buzzword status, there are a lot of great AI tools for L&D out there and in the making. These tools not only enable learning professionals to offer better learning experiences, but also to understand the impact of learning. There’s also big potential in automatising a lot of the conventional information gathering. This, in turn, should enable L&D teams to focus on their core competence – delivering great learning. If you’re interested in the different possibilities AI can offer and how to use AI in organisational development, contact us here. We’d be happy to share some of our experiences, examples and research.

How to Use Chatbots in Corporate Learning? 3 Value-add Cases

Chatbots in corporate learning

How to Use Chatbots in Corporate Learning?

In today’s efficiency-driven business environment, organisations are looking to automate whatever functions they can. Consequently, corporate learning and development teams also face similar pressure to do more with less. Hence, we’ve seen a surge in both AI technologies and robotic process automation (RPA). One particular technology that has become highly popular is the chatbot. While chatbots don’t have to be artificially intelligent, most of them are. Powered by machine learning functions, these bots have the capability to learn from all interactions and refine their output accordingly.

But what are chatbots in corporate learning good for? Here are 3 ideas for delivering better learning experiences with the help of our virtual friends.

Using chatbots to reduce administrative workload

To enable effective learning, it’s important that an organization has a good learning support infrastructure in place. From answering learners’ queries about topics to technical assistance with digital learning platforms, there’s a lot to take care of. Often, these functions are neglected or are not capable of handling queries rapidly enough.

Bring in the bots. A corporate learning chatbot is a great way to handle most of this workload. A trained chatbot can easily answer most of the queries your learners come up with. Furthermore, it can also help on things like finding and locating learning content from within company systems and learning portals. It can also help to learners to enrol in relevant activities to them by presenting data-driven recommendations.

Using chatbots for onboarding and HR-related queries

Similar to the previously mentioned functions, organisations can also use corporate learning chatbots for onboarding and HR related purposes. Traditionally, onboarding is a process where organisations dump all the information they can assemble on their new employees. A lot of it might be totally irrelevant, and most of it will definitely be forgotten by the time the onboarding is over. So, how about delivering slightly smarter onboarding with a bot?

Instead of the usual information dumping, which results in a cognitive overload, a chatbot could deliver this information much more seamlessly – and at the point of need. Whenever a new employee encounters a problem, they could simply consult the chatbot. Whether it’s HR policies, compensation and benefits or even simpler questions like where the office water cooler is located, the bot can answer it all. Quite frankly, this type of virtual personal assistant could be of use to everyone, not just the new joiners!

Using chatbots in workflow learning

While the other two use cases concern primarily administrative functions, bots do have applications in the actual learning as well. Currently, a lot of the traditional type of corporate learning is becoming obsolete. Without the capability to demonstrate performance improvements, employers are less and less willing to lend their employees to sit through hours or even days of learning activities. Thus, learning is increasingly going into the workflow and that’s where chatbots are at their natural habitat.

Generally speaking, the most effective learning experiences are those where you can apply the newly learnt immediately. With just-in-time learning happening in the flow of work, that’s a natural occurrence. We query information rapidly, get information and execute. Hence, the memory effect generated is a much more significant one. Furthermore, this is a naturally occurring behaviour already. Without dedicated learning chatbots, we would do the same with our mobile phones on platforms like Google, Youtube or Quora.

However, the competitive advantage of the learning chatbot in workflow learning is the ability to deliver curated and highly contextual answers. When you do a google search, you’ll get millions of hits. But a company chatbot is able to tell you a specific way that the particular task should be executed. The answers may of course be included in your formal learning materials, but the problem is that employees can’t generally access them seamlessly enough. This type of chatbot-powered performance support resource is unmatched in accuracy, speed, scalability and user experience.

Final words

Overall, chatbots are a great tool to support many different functions in corporate learning. Firstly, the performance improvement possibilities and improved efficiency alone are compelling, but the bots are also a powerful source of data. For instance, analysing the interactions between your bot and employees could provide valuable input for a truly data-driven training needs analysis.

However, the best thing about chatbots is that they are flexible. Generally, chatbots can be implemented on any platform, as you’ll just need to feed them data. This makes them a low-entry-barrier addition even if you’re running expensive legacy learning systems. If you’d like to explore the possibilities chatbots or other digital learning solutions offer, we are happy to arrange a discussion. Just contact us here.

Learning Technology Trends for 2019 – What’s Ahead?

Learning Technology Trends for 2019

Learning Technology Trends for 2019 – What’s Ahead? 

During the past few years, we’ve witnessed an unprecedented speed of development in the learning technology space. Likewise, the year 2019 looks to be no different. At Learning Crafters we are lucky to have an inside view to much of the development happening in the learning technology space thanks to our work with some of the leading technology vendors. Therefore, we thought it would be worthwhile to share some of our thoughts, views and first-hand experiences on what’s ahead for the industry next year. Hence, here are four key learning technology trends for 2019. 

Learning Technology Trend #1: Big Data will deliver exponential impact in 2019

For the past few years, organisations have been adopting tools and technologies to capture, analyse and execute on business data. While the human resources function in general seems to be lagging slightly behind in that adoption, 2019 looks to a be a big year for big data. For learning and development, the holy grail of learning data – the Experience API (xAPI) – has already been available for several years. While adoption of the xAPI standard has been slower than expected, any organisation claiming to do “learning analytics” today cannot remain credible without involving with xAPI. The old, commonplace ways of capturing learning data (e.g. SCORM) are simply not powerful enough. As we move into data-driven decision making in the L&D space, big data capabilities are an absolute requirement – and that will be delivered with xAPI. 

Learning Technology Trend #2: Artificial Intelligence (AI) will undergo rapid developments

Naturally, in the era of machines, the xAPI learning data will not only be used for analytics. Rather, this type of behavioural data (comparable e.g. to Google Analytics) will be used to develop more advanced AI. Now, what is AI good for in the learning space? 

Currently, AI in learning is being used to build adaptive, as well as personalised learning. Furthermore, the currently available more advanced AI applications are able to curate learning content based on the individual roles, needs and preferences of the learner. In 2019, we’ll definitely see major developments in both fronts. Additionally, we predict another AI application in learning analysis. In other words, the use of artificial intelligence to form insights on the link of learning and performance. 

Learning Technology Trend #3: Virtual Reality (VR) will become more “commercial” 

If you’re a learning professional and didn’t hear about VR in 2018, it’s time to go out! While a lot of the hype surrounding VR is arguably just that, hype, 2019 looks interesting. In addition to developing an industry understanding of what VR is good for, we are likely to see some major enablers.

The first major problem with VR currently is the price tag. Arguably, building VR the way companies currently build it does not enable long term adoption. Since VR is currently mostly developed with game engines, there are few possibilities for the non-tech-savvy to build content. If you look at e.g. how videos have grown the their current dominance, that’s because every single individual can produce them. 

The second major problem with VR this year has been the lack of data capabilities. Without the ability to record big data from the VR experiences, organisations cannot possibly prove the investment worthwhile. While VR experiences are definitely a great gimmick, many organisations have vastly over-invested in it. However, there’s light at the end of the tunnel already in 2019. In fact, we are already seeing some of the first VR content editors emerge. These tools require no technical knowledge, game-engines or programming and come with big data capabilities. Hence, they overcome some of the two current major problems and are set for wider adoption. 

Learning Technology Trend #4: Augmented Reality (AR) will redefine workflow learning 

While VR has been on everyone’s news feed, augmented reality has gone largely unnoticed in 2018. However, several companies both in- and outside of the learning field are developing their AR tools. With the current pipeline of technological development, AR is likely to have a major impact on bringing learning into the workflow. A lot of the initial impact will focus on the technical fields, such as engineering. 

For the first time in history, people will actually be able to learn without interruption to work. This will happen with specialised AR headsets, which you can use to open learning content into your additional layer of reality. Best of the tools will have voice control and come with remote capabilities. This enables, e.g. trainers and experts to follow the learners and guide them through activities. Through a live connection, the trainers may influence the “reality” visible to the learner. Furthermore, the advanced headsets will likely incorporate cameras and tracking capabilities to capture great amounts of data. This data will be incredibly useful both for learning and the business as a whole, as it enables a totally new level of recording work, understanding workflows and the learning happening during them.

Now, the four technologies here represent only a part of the future of learning, but arguably they’re the most hyped. Later, we’ll look at some other technologies as well as emerging methodological trends in L&D. 

Is your organisation ready to take advantage of the upcoming technological developments in the learning space? If not, we’re happy to work with you in building that capability. Just contact us. 

AI in L&D – 3 Low-hanging Fruits for Implementation

AI in learning

Implementing AI in Learning – 3 Low-hanging Fruits to Start with

Artificial intelligence (AI) is one of the technologies that is going to fundamentally change learning. With applications from content curation to predictive analytics, AI applications provide powerful tools for making learning more efficient. However, with such a wide range of applications and use cases comes ambiguity. For many L&D and HR professionals who are not domain experts in AI, it may be hard to grasp all the potential. Furthermore, figuring out whether and how to get started with AI can be troublesome. Hence, we’ve compiled 3 different value-add cases for AI in learning.

1. Using AI in learning management to eliminate manual work

When delivering digital learning, one of the least productive and most menial of tasks is the learning management. Professionals use the digital learning environment, or an older LMS, to manage and assign courses, materials and produce reports. Naturally, this is work that needs to be done, but is very menial and repetitive in nature. In terms of productivity, the work is low value-add.

Luckily, machine learning and artificial intelligence can and will eventually take over practically all of this work. This will free the learning professionals from a time-consuming but unproductive load of work. Hence, they are able to focus on designing and delivering the learning, which is the true value-add part. Furthermore, AI is also very likely to outperform people in tasks like this – there’s less room for human error and the computer doesn’t forget. Such using of technology to automate repetitive tasks should be the first application of AI in learning for every organisation.

2. AI in Learning and performance support chatbots

Many organisations have embraced AI when it comes to chatbot applications. Smart chatbots based on AI can provide personalised and customised suggestions to user inquiries. These form an easy tool for on-demand learning and performance support. The users can get answers to their queries quickly, resulting in minimal downtime and better productivity. Think of it as an advanced interactive search engine. There’s no need to go through lengthy documents, manuals or guidebooks. The chatbot is able to pool from the organisation’s knowledge, the previous users and best practices to provide answers in a blink of an eye. Whatever the problem, the machine can likely provide good suggestions as long as it has been exposed to relevant data.

Read more about using chats and chatbots in learning here.

3. Using AI to personalise learning for every employee

Finally, a third use case of AI in learning is to provide personalised learning experiences, as well as designing adaptive learning. AI can collect data, analyse and learn from human behaviour far beyond the human ability. If our learners are having problems with the content, AI detects it, and offers them another set of material with different modalities or difficulty. Furthermore, the AI can suggest additional resources complementing the learner’s existing skill-set. Delivery of content will then be in formats that the AI has detected to be most efficient for the learner (videos, simulations etc.). This improves learning engagement and yields better learning results.

Naturally, AI will have many more applications as the technology develops. However, you can already take advantage of it to eliminate manual work and provide better learning experiences.

Do you want to understand the functionality of AI in learning better? Are you looking to implement AI in your organisation’s L&D? We can help you get started, just contact us here



Adaptive Learning Design – What Is It and How to Use It?

adaptive learning design

Adaptive Learning Design – What Is It and How to Use It?

One of the key trends of today is personalisation. Whether it’s shopping, marketing, watching movies or any other thing, we have come to expect personalised experiences. The same is true for learning also, even in the corporate setting. Employees expect the employers to provide learning that is relevant and empowers them both professionally and personally. For the best learning experiences and results, the employer’s and employee’s interests need to align. The modern learner expects learning content of the right difficulty, delivered with the right mediums, reflecting their professional and personal interests. Luckily, adaptive learning design helps us do all of that, and more.

What is adaptive learning design?

Adaptive learning design means that learning journeys transform from straight lines to something resembling a spider’s web. Each milestone of progress influences what the learner sees next and hence where the learner moves on that web. By taking into account the learner’s prior experience, roles and operational experience, we can assign content that is complementing their existing competencies and reinforces their skill-set. Endless amount of factors can influence the content assigned to the learner: prior learning results, prior job experience, age, position, geographical location or own interests.

Adaptive learning design aims to produce relevant learning taking into account as many of these factors as possible. More personalised learning improves engagement, motivation and retention. Employees gain knowledge on items that benefit them in many areas – professional careers as well as personal lives. Thus, they are more likely to stay engaged in their jobs, maintain continuous improvement and deliver better work.

How to make it work?

Adaptive learning can be designed in many ways, but one factor is crucial – data. For adaptive learning to hit the spot, the organisation needs to leverage big data and pool information from different sources. Once the required data infrastructure is in place, here are some methods on getting started with adaptive learning.

Initial Competency Mapping

Before the learners start a course, you should test their existing knowledge on the subject. Then, assign learning content accordingly. This ensures that secret subject matter experts don’t have to waste time on basis level things. Also, the beginners don’t get overwhelmed by too much content too soon.

Post-learning evaluation

You should also evaluate the learners after they have completed the course. People learn at different paces and in different ways, and good evaluation helps to support learners accordingly. While slower learners can keep on reinforcing what they learned, faster learners can start to tackle other topics. You can do this by testing, but perhaps a better approach would be to leverage learning data to analyse the learners’ progress. Thus, you can eliminate some of the dreadful formal assessment.

Curate multiple versions of the content

Naturally, personalised and adaptive learning requires curating the content into the spider’s web model. You can start by mapping out the same content for different positions and seniority levels in the organisation. This can also help in learning engagement, as enthusiastic and competent employees can take on learning above their current role, hence preparing them for more advanced duties.

Get Artificial Intelligence (AI) to automatise the cycle

Once you’re accumulating the data and have built content into the spider’s web, it’s time to dive into AI. AI tools can analyse this data and help to direct the learner in their own personal journey by recommending materials suitable for their personal style, seniority, experience, etc. With proper integration, AI can take into account changes such as position, job duties and operational issues in real-time. Furthermore, advanced AI capabilities can even help the learner to understand what learning style works best for them and assign materials accordingly. AI in learning is still new, so many learning systems don’t necessarily have the capabilities. But some do, and we’ll be happy to recommend them.

Finally, you should embrace continuous iteration and improvement. AI and advanced analytics provide us deep insights on what works and what not. You should use these to continuously improve the content and get the highest return on investment for your learning.

Would you like to implement adaptive and more personalised learning? Are you interested what AI can do in this aspect? Let us know, we’ll help you.

3 Great Tools for Digital Learning Support

Digital Learning Support

Digital Learning Support – 3 Great Tools for Real-time Problem Solving

The last years of constant connectivity and rapid technological development have solidified one specific behaviour. People expect and want things to happen now rather than later – they seek instant gratification. This has resulted in e.g. customer service functions in many industries improving their accessibility by introducing hotlines and online channels. This phenomenon is making its way to the corporate world as well. It’s no longer acceptable to leave emails or inquiries for weeks or even days without a reply. This also affects L&D professionals, who are tasked with supporting the learning infrastructure of the company as well as the learners. Hence, we introduce you three great tools for providing effective digital learning support.

1. Chat modules provide a basic level of digital learning support

A simple chat module is a great way of providing real-time responses to rudimentary learning inquiries. You can find many different systems quickly, some of them even free. You can often easily incorporate these to a company website, intranet or a digital learning environment. This creates a help desk for the employees to go to when they encounter problems with their learning. The problems will not get buried in email boxes and you can solve them faster. This results in less downtime for the learners, which translates to better efficiency. Also, you can easily configure and manage the chat systems to enable small teams cater to large user bases. All of the modern chat modules come with mobile interfaces as well as support ticket management. These help the L&D support staff to support queries on the go and keep track of all the activities.

2. Using Video Chats to provide quick, real-time learning interventions

Going a bit further, we can add picture and sounds the text based chats. Result: a video chat! Video chats provide a great way to provide quick interventions or guidance to the learners. If verbal explanations and support are not enough, staff can easily share screens to show how they do things. Furthermore, this can also help the L&D department to troubleshoot issues with the learning systems, as they are are able to access live footage remotely. Also, video chats can be used to provide virtual instructor-led training and virtual coaching.

In terms of usability, lighter systems which can connect people with just 1-2 clicks work the best. Effective real-time digital learning support requires effortless accessibility, which traditional video conferencing software sometimes fails to provide. Also, video chats, as well as normal chats, work best when integrated with your own learning systems. This way, you can easily pool the data from them with your overall learning data. This helps to provide better learning insights and single out situations where you need to intervene.

3. Using AI-powered chatbots to reduce manual labour

Thanks to the adoption of the previous tools, chatbots are also becoming increasingly available to reduce the amount of manual labour that goes into support functions. For L&D, chatbots can effectively be used the same way as traditional chats. People can communicate with chatbots, who with a bit of training will be able to answer basic queries related to learning. This AI powered technology can help to alleviate a lot of pressure from the L&D staff by handling the low-value-add inquiries. Hence, the learning professionals are able to put their time where it matters – in the high-value interventions.

Furthermore, you could easily incorporate chatbots into the learning content as well. Employees could engage the chatbots when faced with subject-matter specific enquiries. This type of use provides a great way for doing on-demand performance support. Also, you can easily configure chatbots to become interactive, engaging and even funny FAQ portals. Just type in your questions and let the bot do the rest!

How do you handle learning support in your organisation? Are you making sure that learning downtime stays at the minimal? If you’d like to find out more about these digital learning support tools, just drop us a note or chat with us!


Personalised Learning – 3 Things That Go a Long Way

Personalised Learning

Personalised Learning – 3 Things That Go a Long Way

One of the traditional caveats of corporate learning and training has been the lack of personalisation. Due to, among other things, company policies and regulation, organisations are sitting their people through several hours/days/weeks of trainings annually.  In many cases, employees fail to see the relevancy of these training programs, which demotivates them. “Why am I being trained on this? I’m not involved in anything like this in my daily job”. This kind of thinking is commonplace and the frustration for the lack of personalised learning evident.

While we move from compliance driven training to skills driven learning, we need to seriously reconsider our approach. Ironically, it’s information technology rather than persons which is the best in driving personalisation. When training for compliance, you can tick the box as long as the hours are fulfilled. But when you are developing for skills, knowledge and capability, the results define success. And good results require learning engagement, in which personalisation helps tremendously. To understand and drive personalised learning, here are three simple things you can do to personalise your learning.

1. Linking organisational roles and experiences to learning

Naturally, organisations employ people of various degrees of capabilities, knowledge and experience. However, in most cases, there’s quite a clear link between seniority or experience and the individual learning needs. Thanks to technology, we can take advantage of this kind of a link. We can feed our learning systems with information from e.g. the company’s Active Directory (AD) and HRM systems. We can retrieve all necessary information regarding e.g. seniority, tenure, experience, prior learning with this kind of data flows. Once we have this data, we can use it with the learning system to assign learning automatically, based on all these factors. This is the first step of improvement – providing personalised learning based on perceived knowledge.

2. Providing personalised learning based on skills and competencies

Moving to a more individual level, the next step is providing personalised learning based on skills and competencies. Naturally, skills and competencies are a bit harder to track than the roles and seniority. However, by employing seamless testing and data analytics, we can get a better picture of our employee’s actual capabilities. By analysing our employees’ learning history, results, experience and projects completed, we can predictively pinpoint where an individual employee needs learning.

Furthermore, we can complement the above by structuring our learning material in a new way. Firstly, relevant learning materials should include an initial capability assessment. Upon completing this, and based on the results, the learning system forwards the learner to a personalised path on the learning materials. If you scored poorly, you’ll get beginner level material. If the system perceives as you a subject matter expert, it will give you more advanced topics to deal with. Doing this, we give our employees learning content with the right difficulty level. Hence, we don’t overwhelm (too difficult) or bore (too easy) our learners. Essentially, in this model the learning architecture is more like a spider’s web rather than a straight line.

3. Give the learners the chance to personalise their own learning

Finally, a major source of learning motivation is a natural interest in the subject matter. Often, the scope of corporate learning doesn’t extend quite as far as our personal interest would take us. When finding things interesting, we would be happy to dig in extensively but the corporate eLearning only covers the basics. Of course, with limited resources, corporates can’t provide extensive material on all topics. However, we can reap the benefits of the connected ecosystem called internet.

When developing personalised learning materials, we should acknowledge our limitations. But, instead of stopping there, we should put in a little bit of extra effort to guide our learners. There are plenty of outside resources on any given topic which our learners could use to satisfy their personal interests. Off-the-shelf / open source content is seldom a good solution for corporate learning, but in this case, it can help. To help our learners, we should attempt to identify quality content which we can link to. Sure, it might be outside of the current scope of our corporate training, but it can provide a relevant learning opportunity for many. By allowing our learners to seek out subject matter they are interested in, we can positively influence their personal skills development. Even if the learning is not currently related to their scope of work, it might be soon.

Are you looking to provide more personalised learning to enable relevant learning paths across the organisation? We are happy to help and advise you on a data-driven personalisation approach. Just drop us a note here