Navigation Design in Digital Learning – 3 Approaches

Navigation design in digital learning

Navigation Design in Digital Learning

From a design perspective, the digital learning field has been evolving quite a lot in recent years. Whereas we used to rely on highly linear e-learning experiences, we have since understood that we might need other types of delivery too. When designing learning, navigation is an integral part of the final experience: do we want learners to be able to explore freely? Or do we want them to stick to the “path” that we’ve designed? Naturally, there are various benefits and downfalls for any approach you choose, so let’s examine them in more detail. Here are three different navigation design approaches for digital learning, and their potential impact.

Locked navigation: structured, linear paths

First, locked navigation is still probably the prevalent and previously dominant approach in e-learning. What locked navigations means is that learners have to proceed through the learning experience in a pre-defined order. Proceeding to the next step may require playing all the content in the module, completing assessment or performing other tasks. The predominant logic of locked navigation design is that there’s a pre-defined path and each learner should go through it all.


  • If you’re using narrative in the learning experience, learners get the whole story.
  • The experience is highly consistent among all learners
  • The user experience and flow is smooth: learners don’t have to worry about where to go next


  • Forces learners to go through everything, which often results in a more one-size-fits-all experience than something personalised.
  • Doesn’t address learner needs and context very well, e.g. some might only need parts of the information, which is now locked down.

Unlocked navigation design: free-flow discovery

Opposite to locked navigation, unlocked design entails more free-flowing learning experiences. Whereas learners were previously on a pre-structured path, here they’re able to choose where to go, based on their immediate needs and preference. In general, there is some narrative or linear sequence to the learning experience, and navigation aids to guide the learner, but the final “journey” is highly dependent on the individual.


  • Individuals can pick and choose what to learn and when, which personalises the experience ever so slightly
  • They can direct their efforts as they see fit. E.g. skip topics they already know, while putting more time into the new things.
  • The experience is less likely to feel forced and “pushy”


  • Without adequate cues or nudges, the learners might miss or skip some important things.
  • Narrative structures don’t work with a “free-flow” design approach
  • Learners have to self-regulate their own learning; are they capable of doing that?

Adaptive learning navigation design

Finally, a third alternative, enabled by technology, is adaptive learning design. What it means is that the choice and curation responsibility of the learners is eliminated. Instead, through careful and meticulous design and content mapping, each learner is directed onto a journey based on their previous performance. For instance, a learner scoring low for a particular topic might be given reinforcement on it, whereas a more advanced learner might be allowed to skip the module altogether. The idea is to deliver highly personalised learning and eliminate the burden of choice.


  • The learning experience is personal and tailor-made to each individual
  • Continuous assessment of learning, skills and engagement to direct learners further
  • Each play-through can be different, and learners don’t have to worry about finding the right things


  • Designing adaptive learning content requires an extensive amount of work initially
  • AI algorithms powering up the “adaptive” require training, however the process is possible to do without AI

Final words

Overall, it’s good to see that learning and development is utilising more varied navigation design practices. Just like with any design, the goal should be to find the right fit for the given situation. Therefore, it’s really important to spend time on these approaches in the design phase. If you’d like to explore possibilities with different navigation design strategies for your digital learning, don’t hesitate to drop us a note. We’d be happy to help.

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.