AI Archives - imc Learning

To test the AI, the instructional designer used an old training project that had already been completed. “The tasks I gave ChatGPT were repeats of ones I’d completed myself when I designed the training.”

Summarising long texts:

ChatGPT does a very good job here. It can trim long, complex texts down to any word count and unpack jargon, producing short, easy-to-understand-prose.

Translating:

Here, ChatGPT is similar to DeepL, except that you can specify style. For instance, you can tell it to translate into simple or age-appropriate English.

Brainstorming:

Here, ChatGPT can come up with good initial ideas that you can build on in the later design process.

Arne Benoist did, of course, encounter a few limitations. “For summaries and translations, we can only use public domain content, as OpenAI is an open, online platform. You should never upload company data or confidential information. Currently that would fall foul of most companies’ data protection and privacy rules.”

Designing modern e-learning content is an intensely creative process. It seems strange to think a hunk of AI might make a good job of it. So, Arne Benoist gave it a try:

“For many of our designs we use stories, complete with scenarios, characters and plots. Hence, I asked ChatGPT to create a scenario for a web-based training course on cybersecurity. Well, the chatbot did pretty well, actually, but after a few repeat requests I kept getting similar scenarios. I’m sure I could have got better drafts if I’d fiddled around a bit more and given more specific instructions. But the first stories were too smooth and lacking in dramatic tension for my liking.”

Learning with chatbots and IT-powered applications is already a thing in many areas of corporate learning, including with imc Express. But integration of ChatGPT itself? Arne Benoist thinks that’s still some way off. “In most cases we’re working with highly detailed, specific content. So, to use AI in our training courses, we’d have to build the AI ourselves – so we could control it. I’m referring here mainly to data protection, but also to verifying sources.”

Nevertheless, Arne Benoist will be keeping a very close eye on ChatGPT and similar developments. “I’m very much aware ChatGPT is the start of something big, and it has quickly garnered a lot of fans. But I think we still have a way to go before it will truly revolutionize employee training and development. While I can imagine integrating ChatGPT or similar applications in the future, we’re not quite there yet for the kinds of projects we look after.”

Kristian Schalter: Artificial intelligence has enormous potential for making our lives better and our work easier. Yet, the discussion in Germany largely revolves around risks. That bothers me. I am not against some healthy skepticism in principle. However, many things are exaggerated – especially when it comes to the future of work. There is a lot of talk about the end of human work, broad automation and large-scale redundancies. This has almost become a tradition with the technological progress in Germany. The way I see it, the biggest risk is that we are falling behind with AI development on an international level, and that the jobs of the future are created elsewhere – not in Germany or even Europe. That would truly be a horrific scenario.

Kristian Schalter: When people read reports about algorithms with a discriminating bias or absolute surveillance in China, mistrust is a natural reaction. We tend to remember negative things better than positive ones. The term “artificial intelligence” certainly doesn’t help, either. Saying “machine learning” would be better. Although that is just one sub-form of AI, it describes what really happens. AI is not positive or negative in itself. It is a means to an end. What that end is, is still up to humans to decide. This makes it even more important to maintain a clear focus on the potential of AI and discuss the opportunities properly. We need to figure out where we really want to go with the development. If we look at fields like healthcare or mobility, it becomes obvious how great the potential is, and how much we can all benefit from it personally. There is no doubt about the things we don’t want. That is why we must be among the world leaders here. Setting international standards – and that includes ethical standards – is only possible from a leadership position.

Kristian Schalter: For the second part, simply using your smartphone will give you great insights. It contains many AI applications we use every day: in the navigation function, email inbox, web browser, newsfeed, language assistant, movie recommendations and so on. Professionally, I focus on the future of work. Artificial intelligence is already supporting people at work in a myriad of ways. I am particularly impressed with the industrial projects of the Fraunhofer Institute. They are always in tune with the latest trends, and reflect the reality companies experience particularly well.

Kristian Schalter: The most prominent example that is often used would be a radiologist. In many cases, artificial intelligence can analyse X-rays and patient data both faster and with more precision than a human. Rather than making the radiologist redundant, this means they can focus on other aspects of their work – like patient consultations. Pattern recognition is also useful in other fields. One example would be production: Identifying recurrent patterns is a huge issue in quality control.

Kristian Schalter: Education is the key issue in the digital transformation of the working world. The idea that completing an apprenticeship or graduating with a degree means having acquired the necessary competences for a certain career all the way to retirement is unrealistic. Flexibility and adaptability are the most crucial skills in the modern working world, as job requirements constantly change. While I believe the term “lifelong learning” is overused quite a bit, it perfectly describes what needs to happen: Employees must be willing to gain new knowledge – in every stage of their career. Many still resist that.

Kristian Schalter: Yes, absolutely. After all, the very advantage of intelligent algorithms is that they can give me tailored recommendations based on my personal qualifications. Among other things, this can help with the creation of customised professional development offers. Secondly, AI also provides support on the job. For example, smart glasses that project digital images in the wearer’s field of vision to create an augmented reality aid in the performance of various tasks. There are many opportunities.

Kristian Schalter: Humans have unique skills that no robot in the world can replace. A great teacher has more than technical knowledge. They stand out with their empathy and social competences. I trust we all had that one teacher who was a genius in their field, but never quite cut it in their interactions with the younger generation. Equally, we all had a teacher who was able to create enthusiasm for their subject, and maybe even made a real impact on our development. Praise from a computer will never have the same effect as praise from a human being. Meanwhile, computers are better at processing data, and this can be leveraged for determining a student’s optimal learning speed and the topics they need to catch up on, and for recommending tailored learning content. This opens up great potentials – also in terms of reducing the teachers’ workload. However, we still need to learn how to use those methods effectively as part of the lessons. I would say we are only just getting started with that.

Çiğdem Uzunoğlu: What is behind games? Groundbreaking content, design approaches and technologies for the digital age. Yet, these games components are barely developed for applications outside of the games industry. Our foundation wants to change that. We believe in a society that leverages games to shape digitisation, employs gamification to find new approaches to problem solving, and understands digital games as enrichment of its cultural identity. On that note, we consider ourselves a bridge between the games industry, society and other parts of the economy. We highlight opportunities for collaboration, and create new connections between players from different fields. That is why we also describe our work as a cross-over approach.

Çiğdem Uzunoğlu: Games are cultural goods. In principle, every game has a certain underlying value. It is always a cultural product created by certain persons under certain circumstances. Commercial productions are primarily concerned with the games being fun, fascinating and engaging – the same criteria that mark successful books or movies. Of course, there are games that create awareness of relevant issues, or aim to convey specific content. That also applies to serious games. Yet, even an abstract game without a clear message can have artistic value.

Çiğdem Uzunoğlu: In general, games teach us to deal with frustration and failure. In the long term, you can only be successful if you are able to learn from your mistakes. Most games are based on some sort of system. This makes them particular suitable for getting across relationships. You learn to grasp the consequences of your actions. You understand how individual changes affect a bigger process. According to a recent study by PwC, HR managers who already use serious games utilise these primarily for training soft and hard skills, as well as their employees’ cognitive skills. Digital games are also used more intensively in training and professional development. The interactive aspect helps to convey complex learning material and solidify knowledge.

Çiğdem Uzunoğlu: For many people, games open the door to the digital world. So, when it comes to digital topics, gamification and serious games allow you to pick up from where their daily life begins. According to PwC, more than half of the HR managers using serious games see a clear benefit from these games. The same applies to colleagues and superiors who hold that the fun factor helps to understand and manage work processes. Everyone can progress at their own pace, which also removes any fear of real consequences. At the same time, results are easy to analyse and compare. That is why HR professionals see the greatest potential for serious games in the training process.

Çiğdem Uzunoğlu: Games that question their own content have been around for a while. For instance, the German game “Spec Ops: The Line” deconstructs the image of the heroic soldier and the just war. Meanwhile, at the end of Metal Gear Solid 2, the protagonist faces the fact that their reality is only a simulation and that they should switch off their console. Apart from such content-related conflicts, there are also certain mechanisms that indicate that a game might have been played for too long: game characters getting tired or text overlays.

Çiğdem Uzunoğlu: Artificial intelligence has always played a crucial role in games. After all, we are competing against the computer unless we have a human opponent. Thus, the games industry has helped to shape the development of AI. Especially when extensive animated game worlds are produced, complex AI systems are working in the background which react to our interactions. Some games also allow us to build relationships with characters that are controlled by the computer. These relationships will change depending on our actions in the game. AI systems in the background make this possible.

Çiğdem Uzunoğlu: For most games, AI is a key component of a bigger creation, just like graphic and audio design. Naturally, the same holds true for serious games. Digital games respond to our actions or inputs based on algorithms. These are AI systems, albeit weak ones. The main drivers for innovation are found in the large entertainment games segment. A while ago, “Middle-earth: Shadow of Mordor” based on The Lord of the Rings caused a great sensation. In this game, AI opponents learn from every battle and adjust to the players’ behaviour. You could say they “remember” past encounters.

Çiğdem Uzunoğlu: First of all, we initiated a new event series on cultural aspects of games this year as the next instalment of “Quartett der Spielekultur,” which is supported by the Federal Commissioner for Culture and the Media. We also launched GamesTalente, a nationwide sponsorship programme for teenagers, together with Bildung & Begabung (“education and talent”), the national centre for the advancement of young talent. In line with our objective to build bridges, we will guide representatives of various foundations and a group of educators through this year’s Gamescom trade fair, and introduce them to the particularities of the industry. Several other projects – some with national reach – with renowned partners like the Konrad Adenauer Foundation and the Foundation “Remembrance, Responsibility and Future” (EVZ Foundation) are in the application stage. Naturally, I am greatly anticipating feedback and responses to these.

When it comes to the topic of artificial intelligence, there are a lot of misgivings. Losing sensitive data and constant employee monitoring are only two examples, and learning experts are also having to confront these questions as they develop.

However, imc software expert and AI enthusiast Andreas Pohl thinks it’s important to put things into perspective: “A lot of the fears related to the subject of AI are unfounded. Sure, there absolutely are justified concerns regarding ethical issues that need to be clarified, but when it comes to our Learning Management System (LMS), customers don’t have to worry about the possibility of an evil AI suddenly stealing their data or something like that because it’s not a thing that can actually happen.”

To provide a better understanding of what AI really can and cannot do and how it is used in our LMS, we’ll go over the most important concepts and provide concrete examples of their use.

The term is used to describe dynamic algorithms that are able to learn and improve by themselves in such a way that systems can recognize recurring patterns, develop solutions, or provide advice, for example. And the more data that is entered, the more accurate the corresponding predictions.

However, it’s absolutely imperative to define, in great detail, what data is relevant and which rules should be used to analyse data and recognize patterns. In other words, it requires for human beings to actively step in when it comes to the analysis of data and the actual decision-making process.

For more information, visit DeepAI.org.

A typical example consists of automatic recommendations similar to those used by Amazon: “You like A, so check out B.” This is the exact same principle used by the recommendation engines in learning management systems.

Basically put, a person chooses the course they want to take, and the system provides additional recommendations based on this. In fact, these recommendations can also be linked to the person’s learner profile, i.e., their position, their development goals, the courses they’ve already completed, etc.

And the more data that the underlying algorithm has, the better its recommendations will be. This is one of the classic applications of machine learning.

Deep learning is a subset of machine learning in which a machine is able to improve its abilities independently and without any human assistance. In contrast to machine learning, people do not influence deep learning results at all, and instead only make sure that the required information is available and that the relevant processes are documented.

In fact, the machine itself carries out the actual analysis and uses it to derive forecasts and/or decisions without assistance on the basis of neural networks, which are connected to each other in a manner that resembles the human brain. Ultimately, the machine is able to make decisions based on these connections.

Now, it’s important to point out that this requires an enormous amount of data, and that this type of data is not found in individual learning management systems.

For more information, visit DeepAI.org.

Generally speaking, using deep learning in learning management systems is still extremely difficult given the lack of sufficient data. In fact, in order to be able to analyse specific patterns and processes, learning platform vendors would have to analyse the data from various companies together, but this isn’t possible due to the fact that this data is subject to very strict data protection regulations and policies.

One concrete example of an area where deep learning algorithms could be used would be learning style recognition. With AI, this recognition could be much more efficient than it has been to date, as it would work uncoupled from manual input and tests.

In this scenario, a system would be able to automatically figure out a learner’s preferences and behavioural patterns and determine the correlation for the corresponding learning results. This means that, from a purely theoretical perspective, your LMS would be able to determine what your learning style is and recommend appropriate content for you based on that.

In order for the system to be able to use the existing knowledge on how to use learning style recognition to provide appropriate recommendations, all learning contents would have to be available in various versions, that is, as text, images, video, games, or audio.

Needless to say, however, creating these individual contents would entail an enormous amount of time and money, so this approach has seldom been used to date in real-life applications. However, it’s reasonable to expect that these topics will be pretty important in the future, particularly in relation to improving learning efficiency and learning in the moment of need.

In other words, a lot of the fears that people have in relation to the topic aren’t really relevant, or at least not today. However, when it comes to the subject of learning and AI, it will admittedly be necessary to answer ethical questions regarding applications in the future.

Or as Andreas Pohl puts it: “I think we should see AI as an active tool for supporting people. Humans must always be in the foreground of everything, and every single system must provide customers with real added value, regardless of whether it uses AI or not. And at the end of the day, we can always turn any system or tool on or off.”

More about AI and how it can also be implemented in the onboarding process, you can find out in another article of LMS Hot Topics.

28% of all new recruits quit within their trial period or even fail to turn up on their first day of work. Moreover, employees who receive poor induction are twice as likely to leave as employees who go through an effective onboarding process.

These figures by Haufe should alarm companies: Faced with the ongoing “War of Talents”, new hires are extremely time consuming and costly. Yet, a well-designed onboarding concept can be integrated directly into the existing learning management system (LMS).

To many LMS managers, some of the options available today sound like science fiction. That’s why we embarked on some onboarding time travel into the near future to experience what onboarding might look like.

The first few weeks are crucial for making new colleagues feel welcome in the organisation and helping them become part of the team, as this is the foundation for long-term company loyalty. This requires both empathy and clear onboarding processes. Even larger organisations with existing learning management systems that would provide an optimal infrastructure for efficient onboarding design are often unaware of the opportunities available.

This is why we are following our fictitious employee Hanna in this article to get a glimpse of her slightly different onboarding experience ...

Hanna is excited. She just signed the contract for her new job. She is leaving the big bank where she had been involved in investment advisory services for several years and completed many professional development courses. While most of these followed the traditional face-to-face format, some were delivered online – and often bored her to death.

She will handle similar tasks in her new job with her new employer and fears that she will have to chew through the same dull content all over again. After all, that’s what happened the last time she changed jobs. She lets out a sigh at the mere thought at yet another bog-standard first aid course. Hanna has been a certified first aider and fire protection assistant for years.

Exactly two weeks before her first day at work, a friendly email lands in Hanna’s inbox, asking her to join her new employer’s in-house learning management system. She is quite unsure what to make of that. Surely, they are not expecting her to work already?

Curiosity gets the better of her – and she is in for a surprise. Instead of the expected work assignments, a chatbot welcomes her who resembles a cross between Wall-E and R2D2. The assistant introduces himself as Robby and gives Hanna a virtual tour of her new office, introduces the other team members with a picture, and shows her where they sit. He also reveals her future email address and the (mobile) devices she can expect to find on her desk on her first day.

He asks how she plans to travel to the office, and whether she prefers public transport, car or changing it up. She chooses “both” and promptly receives an overview of all connections and parking options. Robby also shows her who will be sitting at reception on her first day and issue her access card.
The little chatbot then explains which communication tools are used in the company and which other tools Hanna will be working with. She is given the option to take a closer look at these tools before starting her job, but is informed that her manager will personally show her how to use them all in her first week.

Robby ends his tour with the words: “We are looking forward to welcoming you soon. If you have any further questions, simply launch a chat and I will be happy to help you!”

The day of truth has arrived: The first day gets underway and Hanna is stumped. Armed with a smartphone, she is asked to team up with another colleague to complete some type of digital scavenger hunt through the company. When she passes certain offices, she receives a notification along the lines of: “Say Hi to Peter! Peter is responsible for your business trips and finances.”

In her new office, she meets the colleagues Robby had already introduced her to virtually, and she is asked to log into the learning management system from her workstation and complete a test. Instead of a typical mind-numbing multiple choice test, Robby greets her, guides her through the different departments and processes, and invites her to an interactive quiz. Even the training sessions on data protection, cyber security and first aid flow nicely.

When she states her qualifications as a first aider and fire protection assistant, she is asked to upload her certificate and given leave to skip all subsequent questions in this field. Robby also enquires directly whether he may add her as a first aider in the system. Hanna agrees.

The system also adapts to her current knowledge with the subsequent questions, and tailors them accordingly. When the program notices that Hanna already possesses sound knowledge in a field, it either asks more difficult questions or skips certain sections. At the end of the training session, Robby leaves her with the words: “Thank you for your support! I see you are already a real pro. I will adjust your onboarding plan and training accordingly.”

Indeed, Hanna learns only new contents in subsequent training sessions. She is asked to self-assess and is provided with a tailored concept aligned with her personal development goals. She is free to schedule her learning time at her leisure. Her superiors only set a deadline for completing all training units.

Even learning method and approach are tailored to her personal requirements. All training contents is available in various formats, and Hanna can select her preferred system. This involves no additional tests: The algorithm identifies the methods most suitable for Hanna based on the training units she has already completed, and suggests training units using a similar approach. If she doesn’t like them, she is free to switch to other formats at any time.

A few weeks into her job, Hanna suddenly stumbles on a question. She enters it on the intranet and is immediately referred to a matching training session. In addition to the specific training offer, she is also shown which colleagues could assist her with her query. Meanwhile, Hanna is shown as an expert for questions in her specialty areas.

Intuitive tools also let her create her own training sessions in minimal time. Templates are provided in the system, and she can choose between voice-to-text, typed entries, preparing presentations or creating diagrams. Content design is automated and the output is provided in a range of formats. Training is delivered in “learning nuggets”, that is smaller learning units no longer than three minutes each.

By now, Hanna’s profile is after three months filled with a wealth of information. She had already discussed the direction she would like her development to take with her manager in the initial weeks, and they identified the required competences and skills and how she can acquire them. In the next half year, she would like to further improve her English. She is also considering assuming technical management tasks.

To this end, she submitted her self-assessment which must be confirmed by her line manager. A skill profile based on this assessment was set up in the system, which shows target and actual scores. Every time Hanna creates or completes another training unit, the target score increases, and she can see which courses would help her achieve further improvements.

Hanna’s onboarding is complete after six months. She feels like she has arrived and is in good hands. Her line manager now sets up a meeting together with the head of her department. Hanna is slightly uneasy when she arrives for the meeting and wonders whether she had misjudged her achievement after all.

Instead, she finds out that a colleague in another department had unexpectedly handed in his notice. The skills match for the position showed that Hanna already met 80% of the job requirements. While she still needed training in employee management, her superiors are confident that she will master this quickly. Hanna accepts and is promoted after only half a year with the company.

So far, so good. Which parts of this story are wishful thinking? Which aspects ring true in real life? No doubt, almost every scenario described is possible today - or will be very soon. Nevertheless, companies are only utilising a fraction of these options and opportunities. Creating a dedicated skills profile for each job, developing learning paths and linking these with competences or integrating learning on demand takes time. Yet, investing in these topics – also in combination with skills-based job profiles – and integrating them into the onboarding process pays off, especially when facing skills shortages.
We therefore hope that Hanna’s story leaves the realm of science fiction and becomes reality soon.

Our eBook on competency-based learning offers many helpful tips and, of course, we are happy to assist. Feel free to contact us directly on [email protected].

No, today we're not introducing our top 3 of science fiction Christmas movies, we‘re talking about artificial intelligence. Dr. Wolfram Jost is the board member responsible for product development and strategy at imc. His job is to anticipate what digital learning could look like in the future. Therefore, he is the ideal interview partner to talk about the future trend AI, which is probably the most praised and at the same time the most criticized in the age of industry 4.0. Completely without fiction we discuss what artificial intelligence can already do, what it can't (yet) and how it helps learners to make decisions.

Hello Wolfram, I'd like to talk to you today about Artificial Intelligence in the training and development of continuous development. But let's start from the beginning: What is artificial Intelligence explained quite simply?

Wolfram: Artificial Intelligence is a term which is currently very strongly hyped. And as it is always with hypes, not all people have the same understanding of it.

In general, the term artificial intelligence is to be understood as intelligence software technologies that have the goal to imitate human behavior. This can mean, for example, that a robot can walk, grab, or speak.

AI includes the term machine learning. That brings you a little closer to the subject of learning. Machine learning is about software technologies that are "learning" without being explicitly programmed in this way. Or in other words, they do not learn by it, that a programmer gives them rules, they learn these rules themselves. And that by examining data from the past for patterns, and then using those patterns extract it from the data.

AI systems are often described in films and books as human-like systems. But completely without fiction: Still in its infancy, a pubescent teenager or already a senior manager - How far is Artificial Intelligence in its development?

Wolfram: Not as far as it is portrayed in these films, but further than the critics says. A modern computer does not have human intelligence - and that is something that is not possible with today's technology. But I believe also that it's not about that at all. With AI it's always about supporting people, not to replace them. For example, if we look at the whole issue of speech recognition. We all know Alexa, and that's artificial intelligence, or face recognition, or image recognition, or translations... there are already very many examples where artificial intelligence is already being successfully used to improve the lives of the to simplify things for people.

Let us now come specifically to education and training: How does AI develop the way we learn?

Wolfram: In the area of education and training, learners have to make decisions. For example: Which content is right for me? Which courses should I take? Which career paths are interesting for me? AI helps to make these decisions. More precisely said, whenever it comes to making such decisions quickly, AI can play a role. This is also the case with autonomous driving, should the car accelerate or should it brake? Should the car turn left or should it turn right? AI helps to answer such questions on basis of historical data or the current context. AI is a decision support function. That's why it's important to answer the right questions about what you want to answer with AI.

Can you describe a concrete scenario in education and training, in which AI is already in use?

Wolfram: One example is the topic of recommendations. We’re all familiar with this when we talk about Amazon shopping. Users get a very direct prompt, people who have bought article X, also have bought article Y. So you can also tell the learners, all who have learned content X have been interested in learning content Y. Or, this skill profile has also participated in courses A and B.

In E-Learning Punk we deal with continuous development buzzwords that are on everyone's lips right now. But we don't just want to throw buzzwords around us, but also to critically question them, to build up an understanding and explain connections. How do catchwords such as Big Data and Learning Analytivcs go together with AI?

Wolfram: Big data used to be a hype - like AI actually - but then it flattened out again in public perception. Nevertheless, big data is still as relevant today as it was before for many years. Big data says nothing other than data management has drastically changed. The data volume has grown, the data must be processed faster - frequently in real time - and the data is no longer just structured data, as well as unstructured data. This means that the administration and evaluation of data is subject to different laws today than it was many years ago. The topic of learning analytics deals with how we can measure the success of learning. AI can help with both. For example when we look at "Content Curation". Here AI recognises the learning needs by the skill profile and what someone has learned in the past and can collect content from a wide variety of sources and provide learners very quickly. This means that the learner no longer has to search for learning content itself, but the system can offer automatically an assemble of new lessons from past activities. And this helps of course enormous with regard to learning speed and learning quality.

And what role can AI play in VR learning scenarios? Can these become even more lifelike through artificial intelligence?

Wolfram: I think it's not always the individual, but the interaction of VR, AI, Big Data, Learning Analytics... The real advantage of all these new developments for the learner can be found in the interaction.

Our last E-Learning Punk article was about Game-based Learning. If we now connect this to AI in a very concrete way: Are there any learning games that recognise my personality and my abilities, and adapt them to the remaining content of the game?

Wolfram: One of the biggest challenges we face in the area of training and continuous development is self-motivation. And game-based learning is a good approach, because it is about increasing the fun of learning, or rather, learning "playfully". We like to play games. And why? They are interactive, there are rules, there are goals, there are rewards ... The learner no longer sits passively in front of the computer but can intervene interactively. There is no predetermined learning path, instead the learning path develops dynamically during learning through interaction. And that's the nice thing, that it's no longer so predictable. The system can dynamically adapt to what the learner has done before. How my counterpart reacts to my game avatar is not explicitly programmed in the game. They don’t act according to pre-defined rules, but depending on what happens, he decides what to do next. And then we come back to the subject of machine learning - software systems that learn without being explicitly programmed in this way. Without a software programmer having predicted to the characters how and what they should learn, they learn automatically based on data during the game.

Finally, the question about the people behind the technology, what characteristics will we have to bring with us in the future to complement AI and robots? Or to put it another way, what can AI not do in comparison to humans?

Wolfram: Of course, that's the crucial question now. One area with which the AI has problems is decision making. When an AI system makes a decision today, it is very difficult to understand why it has made that decision. It's a bit like a black box. There is no explanatory component. Another topic is intuition. People make many decisions intuitively, that means without knowing why. This is not practicable for an AI system. Also, AI systems always control only a single domain, never several domains at the same time. As already mentioned at the beginning, human intelligence is exclusively entitled to humans. Things that are not based on mathematically describable patterns or behaviours, such as intuition and impulses, cannot be achieved by machines. But, it is not a matter of saying what AI can't do, it's about saying what AI can do. And it's about using AI sensibly in the systems we have today. And that's where we saw that such systems can already do a lot and can already be used sensibly.

Thank you for the exciting interview Wolfram!

The next issue of E-Learning Punk will be published in the New Year and will deal with the topic "Learning by Quizzing."

Content Authoring with AI: Maximising Productivity

AI in elearning content creation? A must-have these days.

Hype or future: Will ChatGPT revolutionise corporate learning?

Training Digitisation: Leveraging the knowledge of your people

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Hype or future: Will ChatGPT revolutionise corporate learning?

AI and chatbots have the potential to transform employee training and development – Our experts check out the possibilities

Diving right in: Making friends with ChatGPT

Arne Benoist, Instructional Designer at imc

But: Can ChatGPT creatively design training content?

Still some way off: ChatGPT integration into training courses

Big things are happening

Can Germany afford its current AI scepticism?

AI in Corporate Learning

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Can Germany afford to be this sceptical about AI?

“In Germany, the discussion of artificial intelligence (AI) is marked by skepticism. That bothers me.”

Hello Mr Schalter, I really appreciate you making time for this interview. In your blog, you are advocating a less skeptical approach to artificial intelligence. Why do think the German mistrust of AI is unjustified?

Where would you say that mistrust among the people stems from?

What tips would you give those who want to know more about AI and experience the latest AI technologies?

Could you give an example of how AI would change a specific job?

We all need to acquire new skills. How does the change in job profiles affect that?

Let’s look at it the other way around: Can AI help employees to gain new skills? If yes, how?

How would you imagine an optimal collaboration between a human lecturer and AI – a “robot teacher” so to speak – to convey learning content?

Most people are relieved to hear that AI cannot replace them entirely. Which competences do you think make humans unique and therefore irreplaceable?

A perfect closing remark, Mr Schalter! Thank you very much for this interesting interview.

AI in corporate learning

AI Learning: "Jingle Bots, Jingle Bots..."

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Computer Games in Professional Development

“Games have helped to shape the development of AI applications”

Hello Ms Uzunoğlu, thank you for making time for this interview on (serious) games. We are particularly excited to hear your answer to our first question. Do you have a favourite computer game?

That sounds entertaining but also demanding. The foundation you’re managing aims to highlight economic, cultural and social potentials of digital games as sophisticated as this one. A rather extensive and ambitious goal? What specifically is behind that mission?

What would you say makes a computer game valuable?

Do you think certain competences are best learned in a game rather than by other means?

Where do you see possibilities and opportunities for companies for imparting competences with a game-based approach?

Can computer games challenge themselves? How can computer games sensitise for moderating the use of digital games?

Let’s move on to the scientific topic of the year: artificial intelligence. AI has a major impact on digital formats and solutions. How do you see technology changing the gaming sector? What is your assessment of that technology in principle?

Which AI-based serious games do you know? Where are these successfully utilised?

What future trends do you see for AI-based computer games?

Do you think there are limits to the use of AI in computer games? What would those limits be?

We are curious about your foundation’s upcoming projects. Which one are you most excited about?

Thank you very much for the exciting interview, Ms Uzunoğlu! We will keep a keen eye on your planned projects, and wish you every success with all your ambitions!

Career development rethought: training soft skills through serious games

Gamification raises corporate learning to a new level

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Artificial Intelligence: Useful tool in Corporate Learning or a complete loss of control?

What kind of influence does artificial intelligence (AI) have on learning management systems? Where and how is it being used already? And are all employees about to be continuously monitored by machines?

What is machine learning?

What is machine learning used for?

Where in our LMS does machine learning take place?

What is deep learning?

What is deep learning used for?

Where in our LMS does deep learning take place?

What is the reality?

Onboarding slightly different

Convincing stakeholders for an LMS

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Science fiction or near future?

28% of all new recruits quit within their trial period or even fail to turn up on their first day of work. Moreover, employees who receive poor induction are twice as likely to leave as employees who go through an effective onboarding process.

...and in reality?

Courage to the LMS - even as SME!

Convincing stakeholders for an LMS

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AI Learning

Dr. Wolfram Jost on Artificial Intelligence and why it simplifies our (learner) life

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Artificial Intelligence:
Useful tool in Corporate Learning or a complete loss of control?