New feature: personalized summaries in chunkx

Sustainable learning requires time and reflection. That’s why with chunkx we make it easy to subscribe to the content of existing trainings & courses to support learning transfer in small units and on-the-job. A great new feature for this is the personalized summaries that are now sent out as part of our weekly learning review. They show learners the key messages of their individually worked learning units from the respective week and make it easy to reflect and anchor learned material again.

Limitations of LMS and LXP

Traditional learning management systems (LMS) or learning experience platforms (LXP) do not directly access the content that learners are working on. Courses are linked via SCORM or other standards and may also be opened directly by the system, but exactly what content is in them is not known to the system. It usually only knows the metadata, such as the title and a short description. In some, and by no means all, cases, the system also knows what the learner’s progress is, such as being on page 5 of 20. However, the system does not know what was explained in terms of content on these first 5 pages and whether any gaps in the person’s knowledge were identified.

Learning content analysis in chunkx

With chunkx, things are different: In chunkx, microlearning units can be created, managed and played out directly. Innovative AI functions also make it possible to transform the learning content of existing trainings and courses into microlearning units and continuously play them out to learners. In other words: Subscribe to the learning content. chunkx thus has direct access to the content and can evaluate which content has been worked on by a learner in a week. For each learning unit, the key message is identified and presented in the user’s language. If more than five core messages are generated in a week, data on the current state of knowledge in chunkx can be used to make the selection even more appropriate and, if possible, support where the most learning is needed.

Seamless learning

Reflecting on what you have already learned is one thing, learning something new is another. Each communication event in chunkx, such as the weekly learning review or new recommendations, is also used to calculate which microlearning unit should be selected next for the user. In this way, communication and information occasions are optimally used to provide a low-threshold opportunity for seamless further learning. The microlearning units themselves are presented as completely as possible in terms of content. So users are not only made aware, but learning can happen immediately. The channels used are email or MS Teams, also for the personalized summaries.

chunkx personalized summaries

More AI features in chunkx

In chunkx, artificial intelligence and smart algorithms are used in a variety of ways:

  1. In content creation, to relieve authors and to efficiently transform existing learning content into microlearning units including learning tasks.
  2. In keywording, to be able to efficiently analyze and relate content.
  3. This is especially important for our adaptive learning algorithms. Here, we keep on using rule-based calculations, but we also experiment a lot in the areas of machine learning and AI.
  4. In skill analysis and referencing skill ontologies such as ESCO to relate content to standardized or company-defined skills.
  5. For recommendations for further learning. Again, the content is analyzed mathematically and related to other content, such as exciting articles, courses, or other learning units. Particularly exciting for companies and organizations with large product databases and knowledge bases: These can also potentially be used by us to provide learners with optimal recommendations.
  6. In personalizing learning content, such as the custom-created summaries shown here, which we will gradually incorporate at other places in our Learning Experience.

With chunkx, we’ve created the solution to turn your one-off learning activities into continuous learning experiences, such as personalized summaries. Talk to us about your learning culture and the changes in your organization and how chunkx can best support you.

Corporate Learning redefined: With ChatGPT and other smart technologies for sustainable and individual employee development

ChatGPT and other smart tools not only bring opportunities for more efficient content creation, but also open up whole new opportunities for sustainable employee development and professional development.

Digital Learning: The challenge of the growing flood of content

In a rapidly changing world of work, it is of great importance for companies to qualify their employees for the requirements of the future. In doing so, they face two major challenges:

  1. There is no endless stream of new employees to recruit who already bring new skills and experience to the company. On the contrary, the choice is very limited and companies are struggling to find the best offers and concepts to attract talent and appear attractive as an employer brand.
    #employerbranding #skilledlaborshortage
  2. “The world is changing faster and faster,” is a statement that may evoke a weary yawn from most people. Fair enough! Nevertheless, in light of the first challenge, continuing education is a constant and ongoing need for companies, particularly when it comes to preparing employees for unknown future challenges. Especially in terms of empowering employees to best deal with new challenges that we don’t know about yet.

Accelerated by the Covid crisis, digital training has become a top priority for companies. The proliferation of new platforms, customized trainings, and eLearning offerings has resulted in an ever-growing flood of content.

Content vs. impact

However, it is unclear how effective these measures are. How much knowledge is actually retained? How much does a company benefit from courses that end abruptly and rely on learners either internalizing everything in detail immediately or looking at slides on their own later?

As the amount of learning content increases, attention to the individual topic inevitably decreases, and in the rush of daily work, content tends to fly by rather than take root. But how to solve this? How can individual follow-ups be generated on the topics employees are working on? How can we technologically create learning companions through which learning processes are thought of continuously instead of once and which can also react to changes on an ongoing basis? Who is going to be able to handle all this work?
I’m sure you already have an idea of the direction the answer will take. 😊

New possibilities with ChatGPT and other smart technologies

Thanks to extreme scalability, ChatGPT and other smart technologies offer a new form of sustainable and individual employee development. At chunkx, we use technology like this in many places to provide companies with a subscription option to any learning activity in their organization. In doing so, we take on the task of ensuring the impact and retention of the learning for each individual employee. There is more on our main page.

But how do we use ChatGPT and other smart technologies now? Here are a few examples:

1. Efficient and scaled content creation

Just a few months ago, it would have seemed inconceivable for 99% of Learning & Development leaders to create personalized microlearning units for potentially all learning activities in their organization and to individually select follow-ups based on knowledge levels. But now that most of them have had their first touches with OpenAI’s ChatGPT, it’s starting to become more tangible how high the quality of automatically created content has become.

What is often still a difficulty is a lack of context and a not inconsiderable proportion of false statements that nevertheless sound right. At chunkx, we solve both by creating the necessary context with content that exists inside or outside the company in the form of PDFs, WBTs, PowerPoints, or learning videos. Herewith ChatGPT and other language models have the necessary orientation to create suitable microlearning units.

2. Smart content selection

Imagine receiving follow-ups to every learning activity you participated in to enable learning transfer. You’d drown in emails and learning content and end up ignoring it because it’s just too much and overwhelming.
At chunkx, we have developed our own algorithms to choose the right one for you from a number of thousands of microlearning units or, if necessary, to repeat one you have already worked on. Always with the aim of promoting learning transfer and ensuring sustainable learning success. Only through such a technology, which can guarantee the necessary reduction for the individual user for a wide variety of topics, can the acceptance and thus the overall success of the learning measures be ensured.

Language models and Natural Language Processing help us with smart or adaptively called content selection, e.g. via content tagging. We have written more about this here.

3. Personalization of the learning content

We have already created microlearning units to our existing trainings and courses in step 1. In step 2, we make sure that the content is selected appropriately per user. Now we’re going one step further and enabling language models like ChatGPT to be used to further personalize content as well. Thus, content can be made easier or harder as needed, alternative variants of the content can be created for different roles, or examples can be formulated more appropriately for the profile in question. All these measures, still consumed a lot of working time without AI solutions and were therefore suitable for high-priced learning measures. Our Machine Learning team is already experimenting with this and looks forward to discussing your company’s content personalization needs as well!

4. Recommendations for further learning

Continuous learning for us includes using technology to also find new content for the learner that fits his or her courses and training. If someone has taken courses on cyber security, or “subscribed” as we call it, then a newly published article on current phishing methods would be a fitting recommendation, enabling and encouraging continued learning.
Perhaps not only external open source content, but also courses from linked platforms, such as LinkedIn Learning should be considered when generating recommendations.
Or even the internal, non-public database with all knowledge articles should be related to the processed learning content and the right recommendations should be generated.
All this is part of our concept of how continuous learning can work with the help of ChatGPT and modern technologies.

Use ChatGPT and chunkx for more personalized and continuous education

It’s obvious that digital training is becoming increasingly important to your business. However, it’s also clear that without sustained support, the ever-increasing amount of learning content will overwhelm you and not sustainably embed your knowledge.
That’s why it’s time to leverage technologies like ChatGPT and other smart tools to make employee development more continuous and personalized.

Want to learn more? Contact us for a personalized consultation and let’s redefine your organization’s employee development together!

Also take the opportunity to subscribe to our newsletter and never miss a technological update again. By the way, we’re also on LinkedIn!

Tagging at the touch of a button

With chunkx, we want to sort large amounts of learning content in a user-specific way. To do this, it is essential to automatically analyze the learning content and relate it to each other. We use Natural Language Processing techniques to make this possible. Find out exactly how this works and what it means for our users, our learning app, and corporate training in this article. Have fun reading!

User-specific learning, without programming effort for the authors

chunkx supplements or replaces existing learning with continuous micro-learning. But what does continuous learning mean in an operational context? Well, typical trainings, e-learnings, learning videos, webinars, etc. end at a certain point – sometimes with and sometimes without a knowledge test. This limitation is a high risk in terms of time for the individual and economically for the paying company: because after just a few days, we forget a large part of what we have learned. Ebbinghaus figures that after 6 days we only know 23% of what we learned. Regardless of the significance of this specific figure, with chunkx we are taking on the challenge of flattening the forgetting curve.

Repetition and linkage

A first step towards this is the repetition of learned content. For this purpose, we use learning tasks followed by explanatory feedback in chunkx. he content we learn from a wide range of different areas – be it subject-specific content, regulations, new skills, or perennial favorites such as security, safety, and compliance – must be brought together in user-specific feeds. However, every user has limited time, which is why their respective feed should ideally prioritize content that they want to or should learn, but don’t yet know as well as others. With 10 topics with learning content of one hour each and a learner who can invest just a few minutes per week in continuous repetition, the question is how to succeed in this challenge? And how can additional recommendations for new content be made to learners based on this?

chunkx creator: tagging the content

In order to compile learning content as appropriately as possible and individually, it is necessary to relate it to each other. On the first level, this is done via channels: All learning content, e.g. on the topic of tax law, is assigned to the corresponding channel. But what does it look like within the channel, which content belongs together there and how? And maybe there is suitable content outside this channel?

For this, we already enable our authors to tag learning content. Different learning tasks are linked via individual keywords. This enables specific evaluations and allows our algorithm to better understand which content is didactically relevant for users.

While manual tagging is useful, it can also be time-consuming. That’s why we developed taggingatthe push of a button: Our authors can automatically generate suitable keywords after entering the task texts, remove them again if necessary and continue to add manual keywords. For this function, we use Natural Language Processing techniques. But what does that mean exactly?

How does auto-tagging through Natural Language Processing work?

The field of Natural Language Processing deals with the analysis of our language. Depending on the requirement area, sometimes simple statistics are enough for this. However, to produce intelligent results as needed in our case, more complicated methods must be used.

Word embeddings through high dimensional vectors

At the heart of our auto-tagging are so-called “word embeddings”. These translate words into high-dimensional vectors so that texts can also be understood by computers. Such vectors then allow us to make calculations and determine the proximity of words and texts to each other. For example, one can imagine that the terms “tax advisor” and “tax law” are relatively close. Exactly this proximity can be described by vectors. Of course, not all comparisons are so clear-cut. For example, which word should be closer to “tax advisor”: “lawyer” or “finance”? Both words have a different reference to “tax consultant”, which is why a clear, unambiguous answer is difficult.

Therefore, to capture all possible properties of a word, our word vectors consist of several hundred dimensions. In order for these dimensions to be meaningful, the model that translates the words into vectors must be trained beforehand – in our case with several hundred million words.

From vectors to tags

For generating the tags in chunkx, we first combine all the explanatory fields of a channel (i.e. title, description, questions, the correct answer, feedback) as a single vector. For thematically meaningful tags and lower computation time, all unnecessary filler words are filtered out of the texts. Now, to generate tags for a single task, the words are compared to each other as well as to the associated channel. The different sections are weighted differently and continuously readjusted.

The words whose distance from the channel vector is the lowest and which do not exceed a certain threshold are ultimately proposed to the author as tags .

Why are tags so important now?

The tag generation technology is not only a time saver for our authors, but also enables intelligent linking of content. For example, consider our learning content on the topic of tax law: A learner has knowledge gaps on the topic of sales tax. We can now repeat this selected content accordingly. However, we can also have our algorithm capture what learning content from other channels available to the learner has content proximity to the topic of sales tax. We can then suggest and prioritize these to him as he learns about one of these channels. The selection of content thus adapts to the learner and his learning time is used as efficiently as possible: Namely, with the content for which there is a need to learn.

While classic web-based training or learning videos offer the same content in the same order for all learners, with chunkx the most appropriate content can be selected – and not just within one channel, but across an infinite number of channels.

The more channels and topics are made available to employees of a company in chunkx, the more content overlaps arise and the more continuous micro-learning with chunkx shows its full strength.

Contact us

Have we aroused your curiosity or do you feel bored by our example around tax law? Well, let’s also rather talk about your topics and how we can best support learning about them with chunkx. Write to us and we’ll get back to you shortly.