Well worth a visit!

What is the most important aspect of live prompting? What are the most important points to look out for?

Dr. Daniel Stoller-Schai: Live prompting means working with AI systems in real time to achieve immediate results. Two aspects are particularly important:

• Precision and transparency: the prompts must be precisely expressed to achieve the desired results. The clearer the instructions, the more relevant the results.
• Flexibility and adaptation: Interaction with the AI requires adaptability - depending on the output, the prompt may need to be adapted directly to optimize the desired results. However, there are prompts that can take care of this.

In the workshop, we will perform practical exercises and use a prompting sheet that helps to create structured and effective prompts more quickly. The focus is on simplifying the input processes and enabling participants to interact confidently with the AI.

Are there specific areas that require special precautions?

Dr. Daniel Stoller-Schai: Yes, according to the new EU AI Act, AI applications are classified into different risk categories:

• Minimal risk: e.g. AI-supported chatbots without security-relevant functions.
• High risk: e.g. AI in medical diagnosis, credit scoring or personnel recruitment.
• Unacceptable risk: Applications that violate fundamental rights, such as certain surveillance practices.

Depending on the area of application, companies will need to take specific measures - from a simple labeling requirement to extensive testing and documentation obligations. However, there will be room for interpretation, which is why it remains important to continuously review the regulatory situation.

What will the EU's new AI regulations change in terms of practical use?

Dr. Daniel Stoller-Schai: The new regulations will focus increasingly on the ethical use of AI and define clear limitations, especially for sensitive applications. Businesses will have to fulfill transparency and verification obligations for certain applications in future.

However, I hope that the scope for innovation will be maintained, as I believe this is important for the European economic area - and Switzerland is part of this.

Europe can also position itself as a pioneer for trustworthy AI - an advantage for companies that want to comply with ethical standards.

However, the exclusion of military applications remains critical, which can be seen as a contradiction to the ethical guidelines. Overall, however, the regulations provide a clear framework that is intended to reconcile innovation and responsibility.

Does it make a difference in prompting which AI I communicate with?

Dr. Daniel Stoller-Schai: Yes, that is a determining factor. Different Large Language Models (LLMs) have different strengths based on their training data, architecture and objectives:

• GPT models (like ChatGPT) are versatile, focusing on language understanding, creativity and text generation.
• Claude (Anthropic) emphasizes security and ethical guidelines.
• LLaMA (Meta) is often used for research and open-source projects, with high customization potential.

Depending on the model, the results, interpretations and even the efficiency of the collaboration can differ significantly. Therefore, it is worth experimenting with different models in a targeted approach to find the right tool for the respective task. There are also platforms such as Straico that allow you to run the same prompt in parallel through different models and then compare the results.

Are there guidelines, instructions or even courses that can provide guidance for users, or does “practice does the trick”?

Dr. Daniel Stoller-Schai: Both play an important role. There are numerous guides, online courses and tutorials - from beginner to expert level. These teach the basics of prompting, provide best practices and help to avoid typical mistakes. The importance of Agentic AI and AI workflows will become apparent in 2025. There are already numerous training courses available.

However, the decisive factor is still practicing. You learn how AI acts, and which adjustments bring the best results through continuous practice and observation.

An additional tip: My preferred form of learning - collaborative learning or peer learning - plays a major role: those who regularly document their experiences and exchange ideas with others (e.g. in forums or networks) will make faster progress and develop their own personal style. LEARNTEC 2025 is a wonderful opportunity you should use.

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How important is collaborative learning among the generations in your organization?

Julia Hartinger: Knowledge management plays a significant role in the DB Group. The objective is to learn more and more with and from each other: e.g. boomers pass on their knowledge to younger generations at an early stage, as sustainably and continuously as possible, and not just before retirement. This guarantees that the valuable knowledge and practical skills of those with experience are not lost and are retained by the organization.

A learning-friendly working atmosphere and an appropriate environment can only be created if all those involved have an understanding for each other, an inner attitude and the resulting behaviour towards each other (TR-TN and TN-TN). To achieve these goals, the learning coaches from DB Training, Learning & Consulting have the responsibility of raising awareness among the different generations and preparing them for the challenge.

What is the role of individualized learning?

Julia Hartinger: Based on our experience, a mixture of individualized learning and the group experience is what makes learning successful.

Individualized learning enables self-determined learning (autonomy), which in turn is a decisive factor for the development of intrinsic motivation. Those who are intrinsically motivated can enter a state of flow while learning. This enables them to experience competence. During training, for example, our learners can often choose whether they work on a task by themselves or in a small group or decide which topic interests them more. And they take on moderating tasks in the training situation to test and expand their skills individually.

For us, “individualized learning” does not necessarily mean that learners work on content on their own. That only happens mostly in preceding self-learning phases. Instead, we see repeatedly that social involvement plays an important role in intrinsic motivation.

Learning theories also state that learning for the sake of learning is not very useful or sustainable. Again, this means that we promote individualized learning according to the “pull principle”, e.g. with the launch of the learning station this year, DB's new central learning experience platform. This platform ensures that learners learn exactly what they want and need - when and where is best for them. It is of course important that learners are also willing to do this: Although the theory may seem correct and reasonable, older learners are often overwhelmed by it in practice. The “learning mindset” remains strongly anchored in the “push principle” in this case. This needs to be gradually overcome by experiencing “new learning”.

Does collaborative learning between boomers and Gen Z also have effects in terms of knowledge transfer?

Inga Geisler: This is “pure” knowledge transfer in both directions. If the boomers are willing to develop themselves further and at least give new approaches a chance, they will benefit considerably from the ideas of Gen Z. Again, it is important to be open to learning from the experienced generation and to treat them with respect. Training situations can initiate further networking and a willingness to exchange ideas. After all, there is usually no time to do this in everyday practice due to the high workload.

In which fields/work areas do you use this form of learning?

Julia Hartinger: The fact that generations learn with and from each other is not what we call a special form of learning. It happens every day at DB and at DB Training in the context of work and training. For instance, in addition to the learners, our learning mentors also belong to different generations. In most cases, this automatically results in a cross-generational learning group.

Are you happy with the results?

Inga Geisler: Deutsche Bahn has been working intensively on cross-generational learning for some time now - and not just in the training environment. We have noticed that it is important to address the topic and raise awareness. We believe that it is not a given that people from different generations will benefit from each other - we actively promote this. We are on the right track.

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With our smart trade fair companion, we want to make your visit to the trade fair as easy and relaxed as possible. On the talque event platform, you will find an interactive hall plan that gives you an overview of the individual halls. You can also browse the exhibitor profiles and contact available exhibitors directly via video call or make an appointment with them on site. Another great feature of the app is that you can not only find out about the current convention, trade fair and supporting program, but also put together your own personal agenda from all the sessions. To do this, simply click on Participate in the sessions. You can also find relevant matches among the other participants, speakers and companies and suggest a short meeting via video call or on site directly via the app. And you can use the app to register for the Guided Tours, which will take place on all three days of the trade fair. New this year is the Self Guided Tours function: tours that you can simply use online, without a guide or fixed time.

You can find the app in both the AppStore and the Google Play Store.

Now it's just a matter of downloading the app, getting an overview and getting started from May 6 to 8. Have fun at the event and networking!

Being well-established as a service provider for technical documentation and eLearning, you can draw on a rich pool of knowledge. To what extent is NINEFEB's intelligent service chatbot based on this foundation?

Dr. Harald Stadlbauer: We believe that the future of documentation and eLearning depends on intelligently linking content with the objective of making better use of it in the context of generative AI solutions. Knowledge graphs and knowledge models that link content with product knowledge, domain knowledge, competence models and other relevant standards are a key element in enabling these links. Another important factor is the modularization of content for better reuse and preparation for more efficient use in the context of genAI. All this serves to achieve better and more transparent results. Graphwise Platform is part of the foundation on which these intelligent service chatbots can be built.

You use, among other things, microtraining content and knowledge graphs with competency models. Why do you use these elements in particular?

Dr. Harald Stadlbauer: It is becoming increasingly important to offer training in the context of actual activities. Larger education and training programs are of course still relevant, but in the context of the existing shortage of skilled workers, which will intensify in the coming years, it will be necessary for agile learning to be available in the context of the activity.

To enable an increase in productivity that counteracts the labor shortage, it will be necessary to provide employees with learning content quickly so that they can take on activities that may not be part of their current expertise. This requires the maximum possible granularity of the learning content = macrotraining content and a good link between this and the work context = knowledge graphs in the context of the competencies of the respective employee = competency models.

In which areas does your service chatbot make the most sense? Which experiences have you already gained with it?

Dr. Harald Stadlbauer: For me, the service chatbot is just one possible application that can be created based on intelligent content (microcontent + knowledge graphs and knowledge models + GenAI). It's just the most popular application right now and if you look around, everyone seems to be working on chatbots, and you don't see many other application examples. This is also obvious; I think such discursive elements can be used very universally.

In the context of eLearning, it could take a direction in which the training courses adapt to the knowledge requirements and interests of the trainees without losing sight of the objectives to be achieved. It would be a nice idea if the trainees could enter a dialog with the eLearning system and always get the next best steps suggested based on the current needs, their level of knowledge and the learning objectives to be achieved.

Your company is a member of the iiRDS consortium. What does this mean for your further development of the use of AI in the future? Has it influenced the direction of your development to date?

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The advantages of immersive learning environments have long been known. What are the advantages of combining them with skills tracking?

Dr. Leila Mekacher: Immersive learning environments have proven that they revolutionize the way knowledge is transferred. They allow content to be understood not only theoretically, but also to immerse learners in realistic applications through interactive scenarios. However, it is only in combination with skills tracking that they unfold their full potential, as a precise skills analysis reveals which skills are being developed, where potential can be found and how targeted support can be provided.

By actively interacting with their virtual environment, learners are no longer just passive consumers of knowledge. In addition to technical skills, they develop transferable skills such as problem-solving skills, stress resistance and the ability to work in a team, which are required in various professional environments. Skills tracking makes flexibility and adaptability visible, identifies individual strengths in real time and enables personalized learning paths.

An example which illustrates this connection is the EU-funded Metautism Green ASC VET project, which focuses on integrating autistic people into vocational training and the workforce through immersive technologies. My company, Innoversa Factory, is working with partners from Croatia, Portugal, Belgium, Denmark and Hungary on the development of 360° VR and XR simulators and is using a skills tracking platform with VR simulations for over 15 professions to prepare neurodivergent people specifically for professional challenges by training and developing their skills.

As immersive learning environments also require pedagogical and didactic concepts, we also develop training courses for interactive and practical XR-based learning methods that enable sustainable and application-oriented knowledge transfer.

Immersive learning environments not only allow job-relevant skills to be trained in a practical setting. Skills tracking also analyzes in real time how confidently participants deal with the virtual challenges. This facilitates the adaptation of learning paths to individual needs and specifically promotes the development of skills relevant to the job market, such as time management, accuracy or social interaction in professional contexts.

This benefits not only neurodivergent people, but also other target groups, including trainees, professionals in advanced training programs or career changers preparing for new professional challenges. The technology creates a flexible and individualized learning environment that can be adapted to different learning needs and therefore enables efficient and practical skills development for a wide range of user groups.

I am currently working with partners in the region on a STEM program that combines practical, creative learning methods with innovative technologies to get young people interested in technical professions. By using VR simulations in a targeted way, participants can learn complex physical or engineering concepts in a fun way by facing realistic challenges in interactive scenarios.

These include setting up a water supply system in a virtual laboratory or designing an autonomous robot to teach STEM skills in a practical context. In the skills assessment phases of this program, immersive simulators can be used specifically to identify the individual strengths and development potential of participants.

Skills tracking automatically analyzes which skills are developed in these scenarios - from logical thinking to the ability to make strategic decisions. This allows for targeted support that is tailored to the learner's individual potential and professional interests.

Which are the areas of implementation of immersive learning environments with skills tracking in education and training?

Dr. Leila Mekacher: A key area of usage is in academic and vocational training. Universities and vocational schools use immersive technologies to prepare students and trainees for real-life professional environments in a practical way. Skills tracking allows for the precise identification of strengths and weaknesses so that targeted support activities can be developed. In technical fields, for example, knowledge of machines or software can be trained realistically and optimized through real-time feedback.

Another important area of use is in career guidance. Young people and career changers can try out different professional fields in virtual scenarios before choosing an apprenticeship or university course. Skills Tracking analyzes their interactions and strengths to provide targeted recommendations for suitable career paths. This can be a valuable decision-making tool, particularly in manual, technical or medical professions.

This technology also offers significant advantages in teacher training and the development of new teaching methods. Teachers can use immersive scenarios to test digital teaching concepts in practice and get individual feedback through skills tracking. This contributes to the targeted improvement of teaching methods and their adaptation to the learners' needs.

To summarize, immersive learning environments with skills tracking are particularly useful in academic education, vocational training and career guidance. The data-based analysis of individual learning progress makes training more efficient, sustainable and practical.

In which areas are the high costs justified?

Dr. Leila Mekacher: The implementation of immersive learning environments initially requires a considerable investment. However, these costs are justified, especially in education, training and vocational qualification, as they enable more efficient, practical and customizable knowledge transfer in the long term.

Teachers and trainers benefit from testing innovative teaching methods, expanding digital skills and responding specifically to the learners' requirements. Especially in STEM courses and technical professions, immersive teaching units can be designed that are more interactive and motivating than traditional teaching methods.

Finally, investments in immersive learning technologies also pay off quickly for companies and training institutes. When it comes to areas with extensive training requirements, high employee turnover or a shortage of specialist staff, efficient onboarding and training programs help to reduce costs, cut onboarding times and ensure that employees are qualified in the long term.

What role does artificial intelligence play in immersive learning environments and skills tracking?

Dr. Leila Mekacher: The combination of virtual reality (VR) and artificial intelligence (AI) marks the next big revolution in knowledge transfer, skills development and the management of learning experiences. While VR creates immersive training environments, AI ensures that these scenarios are dynamic, intelligent and adaptive. Combined with skills tracking, a new quality of learning analysis and skills development can be achieved.

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How important do you think content curation is today?

Stefan Diepolder: I consider content curation, or rather a curating mindset among employees in organizations, to be more important and relevant today than ever before. Curation enables us to deal efficiently with the overwhelming flood of available information, ideas and opportunities and to filter out relevant content in a targeted manner.

At a time when we can create, analyze and publish any type of media in minutes with the help of AI, human expertise makes all the difference. When curating content, we use our unique perspectives by acting as human filters to evaluate information, methods and tools in a targeted approach, checking its relevance, authenticity and importance and extracting meaning for ourselves, which in turn triggers a valuable learning effect.

We evaluate and share the essence with our colleagues and provide them with the opportunity to obtain targeted information and further training within our area of expertise. They do not have to spend time searching for the right sources and materials, because they can rely on the curators, who guarantee the quality of the content based on our name and reputation.

This trust in colleagues is a significant advantage over AI systems and the mechanism is very effective in providing learning paths and learning plans as well as learning and learning experience platforms, among other things.

Should it be internal or external? And what are the advantages of each approach?

Stefan Diepolder: Both internal and external curation have their advantages. Internal curation is particularly valuable for filtering out current and relevant topics from the company's perspective and placing them in context and promotes internal communication and collaboration.

For example, take the dominating trend of AI: a landing page maintained and prominently presented by a team of in-house AI experts gets to the heart of the most important topics on a daily basis. Which tools can I use? Who can I contact? What training opportunities are there? How can I take my first steps? Are there any successful use cases that are already available?

In an ideal scenario, I would no longer have to go looking for information externally and would still be provided with all the important and relevant information and resources, even with the corporate context. This information no longer needs to be verified by me as a non-expert who has no content expertise, because the internal experts have already done this. That's a considerable relief!

External curating outside your own bubble brings fresh perspectives and new trends into the company and increases the ability to innovate. It strengthens cross-organizational exchange and generates new impulses and ideas. Personally, I experience this every day in my New Learning Lab community, where around 160 learning enthusiasts from a wide range of backgrounds and organizations are currently curating together and learning from and with each other in a self-organized way.

It is particularly impressive how quickly and effectively new ideas, solutions and synergies emerge when people start to share their own work and challenges in a protected space.

I consider a combination of both approaches to be ideal because it creates a balanced knowledge ecosystem. This allows companies to benefit from both internal know-how and external trends.

How much effort does that mean for a company?

Stefan Diepolder: Content curation requires a certain amount of effort to establish systems, mechanisms and processes, especially in the initial phase.

Employees interact with information and people daily, develop ideas and solutions and perform value-adding work. In many cases, they already unconsciously perform parts of the curation process. When employees focus on their expertise, contextualize, document and proactively share relevant parts of their work and valuable information, a vibrant knowledge ecosystem is established without overwhelming the individual.

There is no need to train all employees to become active and passionate curators. It makes sense to test the mechanisms of content curation with an “alliance of the willing” and put them actively into practice on your own topics. For example, this can be combined very well with the content maintenance of a learning experience platform. The mindset of sharing knowledge, mutual recommendation and exchange is therefore introduced to large parts of the organization.

Curation mechanisms work completely independently of tools and provide valuable services for the sensible use of AI models, e.g. when critically scrutinizing AI outputs. The effort involved can be more than compensated for in the long term by efficiency and productivity improvements.

Can the effects of content curation be measured or, above all, described for an organization?

Stefan Diepolder: The consistent use of content curation leads to improved personal and organizational knowledge management and can save considerable time and (thinking) resources for the organization.

Studies have shown that knowledge workers spend between 1.8 and 3.6 hours per day searching for the right information and people, depending on the study (sources include: McKinsey Study 2012 and Coveo Study 2022).

You can calculate the return on investment if each employee spends just 30 minutes less a day searching for the necessary information!

In terms of quality, an increase in employee satisfaction and innovative strength can be noted. Content curation goes hand in hand with a change in learning culture. The success is ultimately reflected in a more agile and knowledge-based corporate culture.

Which requirements do you believe must be met for a company to be able to call itself a “learning organization”?

Stefan Diepolder: I believe that all organizations are “learning organizations”. The human brain spends 24 hours a day, 7 days a week engaged in learning. We cannot “not learn”, to use Paul Watzlawick's famous quote. The key is what we learn, and how quickly new, relevant skills and abilities are identified, developed, tested and used to create value at work.

Innovative, successful and future-oriented organizations are characterized by a culture of continuous learning and openness to change. It is important that managers actively promote personal and organizational learning and act as role models. Equally important are technological infrastructures that facilitate the exchange of knowledge.

Learning organizations are well connected internally and externally and are characterized by a high level of trust. Employees are encouraged to actively share their experience and knowledge and try out new approaches. There is no distinction between learning and working; every meeting, every project and every conversation is seen as an opportunity to reflect and learn from and with each other.

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What is the role of AI assistance in your specific target or professional group?

Sonja Bruns: For many in our target group, AI is still a relatively new concept. But we are noticing that everyday working life is changing - AI assistants are being increasingly tested, and some are already being used, especially in areas such as digitalization and automation. This applies, for example, to the processing of information or the automatic mapping of data.

In the area of taxation in particular, AI offers exciting opportunities for further training: it is becoming more efficient, more customized and more practice oriented. Tax experts can use intelligent tools to optimize their training and adapt more quickly to new legal requirements.

The use of AI in training is a significant step forward. Until now, online training courses have been very flexible, but often not specifically tailored to the individual requirements of participants. AI can do much more in this regard: it analyzes learning behaviour, makes targeted recommendations and adapts the content to the time frame. Automated performance reviews and adaptive learning systems are also used to communicate complex knowledge even better.

The bottom line is that training not only becomes more effective, but also more attractive and market oriented.

Which requirements should AI assistance meet?

Sonja Bruns: To be effective, an AI assistant must cover a wide range of areas: intuitive integration of the AI assistant into the learning process is essential so that learners can interact with it easily. The AI should act as an interactive support - be it by answering questions immediately, providing suitable examples or through quizzes and exercises that promote learning progress.

Ideally, the AI assistant should create personalized templates that adapt to both the learner's prior knowledge and individual preferences. The level of difficulty can therefore be flexible. In addition, the AI's recommendations should not only be tailored to the level of performance but also provide comprehensible support for the personal learning process. The objective is to make learning more efficient and motivating - for example through playful elements and individual feedback.

However, it is crucial that learners always have the option of ignoring the AI's suggestions and making their own decisions. This contributes to the transparency of the AI and strengthens trust in its recommendations. Accessibility and language support are also essential to make access as simple and intuitive as possible for everyone.

In addition, a comparative evaluation of all participants should be provided to ensure scalability. Finally, the trustworthiness of the AI plays a central role - especially when dealing with personal learning data.

Is personalized learning achievable in your industry without the use of AI?

Sonja Bruns: Of course, individualized learning is also possible without the use of AI - after all, we were working exclusively without AI not long ago. However, traditional learning methods involve several challenges: methods such as the use of personal tutors, self-designed teaching materials or project-based learning processes, as well as teacher-based assessments, are often time-consuming and cost-intensive.

Furthermore, without AI, it is not an option to adapt the learning process to the individual needs of learners in real time. This can limit the effectiveness and efficiency of learning. AI, on the other hand, can help to address the specific requirements of everyone more quickly and in a more targeted approach. AI-based learning systems use data-driven insights to dynamically support and customize learning progress by addressing learners' strengths and weaknesses.

What impact does AI have for your company TeleTax in particular?

Sonja Bruns: Our AI-supported solution enables us to create an offering that our market competitors are currently unable to provide. This gives us the opportunity to position ourselves as an innovative provider in a rather conservative environment - an environment that is, however, facing significant change.

AI will increasingly take over routine tasks in the tax sector, which means that training will have to focus more on strategic, legal and technological aspects. Digital skills and the use of AI-supported tools will become essential for tax and tax-related professions.

This means, above all, analyzing and restructuring proven offerings. AI not only opens new learning methods, but also new fields of learning. Areas such as “digitalization”, “artificial intelligence” and “new fields of activity in the tax sector” are coming into focus. However, acceptance of these innovations remains a challenge - trust is the key, and trust must be earned.

We are also using AI specifically to automate our internal processes. This enables TeleTax to reach a larger target group even better, react flexibly to market needs and make training more entertaining at the same time.

Do you expect further developments in this respect in the near future?

Sonja Bruns: In the near future, we expect artificial intelligence to be increasingly used in the digitalization of learning content. However, we are currently facing the challenge that a lot of content is not fully accessible in digital form.

We also assume that the personalization of learning content will continue to improve. AI can recognize individual learning needs more precisely, take learning preferences into account and specifically identify knowledge gaps so that these can be addressed with suitable content, examples or exercises.

Interactive learning platforms will also be enhanced using virtual assistants and chatbots to provide immediate support and feedback. AI will play a central role in the standardization and certification of assessment content. It will also become increasingly important to differentiate between self-generated documents and AI-generated content.

Another central aspect is “trustworthy AI”. This involves the question of how learners' data is processed and used. The label “Made in EU” could become more important in this context.

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Sünne Eichler, LEARNTEC Congress Committee, on current developments in the industry:

Not a day goes by without learning about a new AI tool, being introduced to new possibilities in VR or discussing current developments in LMS and LXP. It's a very exciting time right now and it's fun to try out the many new possibilities and use them in learning settings.

AI has a disruptive effect and offers us opportunities to not only create content more efficiently, but it also has a deeper impact on learning strategies. At the LEARNTEC congress, we have been making it clear for some time that learning and working are growing together. We need to bring learning to the workplace so that our learners can access learning content on demand. Learning opportunities can be tailored to our learners in terms of content and preferred learning format.

Digital learning concepts include - of course - digital technologies. There are countless innovative and pragmatic solutions for this on the LEARNTEC exhibition floor. You can literally feel the enthusiasm for innovation that is being brought about by the new possibilities of AI & Co. At the congress, we will therefore naturally also be focusing on topics such as AI in Learning & Development, Live Prompting and Gamification and Large Language Models. AI is finding its way into all areas of modern learning. However, this does not change the fact that we are dealing with learning HUMANS.

Technology or not - we have to take people with us. Therefore, the change in learning culture and the importance of targeted change management is an important part of ensuring that digital learning gains more acceptance. This also applies to the use of new possibilities such as AI and VR. We need to look at the strategic direction of our learning opportunities. Only if we manage to consistently align our learning opportunities and learning settings with the strategic issues of the organization will we receive the support of all stakeholders - from management to learners. Our learning design must be geared towards the needs of the learners and create sustainable learning experiences - in other words, enthusiasm and motivation.

Learning design means developing learning paths in such a way that learners receive exactly the content they need for their work. And preferably not according to the “one fits all” method, but according to the “mass customization” principle. This includes providing the right content at the right time and in the right place. A sensible combination of forms of learning is needed that corresponds to current needs. New learning, smart learning and agile learning are important approaches in this context.

Learning design also closes the circle between people and technology. A learning guide can be a physical person who helps learners to develop their own learning goals. At the same time, however, I can also provide a digital learning companion in the form of a chatbot that answers individual questions and also enables individual learning paths with adaptive learning. The importance of content curation also comes into play here: we have to curate the content using the seek-sense-share method, for example, so that learners can find the content they need. This is definitely a challenge when using AI: the faster we can create content, the more we might “flood” our learning platforms with content.

That is the exciting thing about modern learning concepts - whether they are based on the new or smart learning approach, pragmatic and strategic - we need the personal and digital components! This is the only way we can create successful learning experiences and generate real benefits for learners and organizations. Because that must be our goal!

Titled "AI & Digital Learning: Past Experiments, Present Impact & Future Possibilities," Dr. Hardman’s presentation will offer a deep dive into the evolution of AI in education, tracing its 60-year journey from early experimental applications to the transformative rise of generative AI in recent years. This keynote promises to be a thought-provoking exploration of AI’s potential to address longstanding challenges in education while critically examining its current limitations.

At the heart of Dr. Hardman’s keynote is the concept of the "impact paradox." This phenomenon highlights a troubling trend: while AI adoption has surged across educational sectors, the quality of outcomes—particularly for complex tasks—has stagnated or even declined. Dr. Hardman attributes this paradox to a widespread "faster horses" mentality, where AI is primarily used to accelerate existing processes rather than fundamentally improving them. For example, tools like Synthesia and Smartcat are being leveraged to create videos and translate training materials faster and cheaper than ever before. While these applications undoubtedly increase efficiency, they often fail to address deeper issues such as learning transfer, engagement, and real-world impact.

Dr. Hardman will challenge educators and L&D professionals to rethink their approach to AI by considering its strategic potential beyond speed and cost benefits. Drawing on research that suggests 60-90% of training fails to deliver meaningful impact on the ground, she will argue that scaling ineffective systems faster only perpetuates inefficiencies. Instead, she advocates for using AI as a tool for transformation—one that can elevate the quality of learning experiences and address systemic challenges such as equity, accessibility, and personalization.

The keynote will also explore how forward-thinking organizations are beginning to push the boundaries of AI innovation in learning design. Dr. Hardman will provide analytical insights into emerging trends, such as using AI to scale evidence-based approaches to training design, personalize learning paths at scale, and extract actionable insights from workplace data. For instance, some organizations are leveraging semantic analyses of employee communication platforms like Slack to identify skill gaps and align training initiatives with business objectives. Others are using AI-powered simulations to enhance experiential learning or deploying adaptive algorithms to tailor content for individual learners based on their unique needs and contexts.

Dr. Hardman will emphasize that while these innovations represent significant progress, they also raise critical questions about the role of human agency in educational transformation. She will argue that successful deployment of AI depends not only on technological advancements but also on human creativity, ethical oversight, and strategic vision. This perspective aligns with her broader mission as the creator of DOMS™, an AI-powered learning design engine that bridges the gap between learning science and educational practice.

A key analytical focus of the keynote will be AI’s untapped potential to address fundamental challenges in education—particularly equity and quality. Dr. Hardman will examine how AI can democratize access to high-quality learning experiences by scaling expertise across global teams and personalizing content for diverse learners. She will also discuss how AI can help organizations move beyond generic solutions by recommending optimal strategies rather than average ones—a shift that could redefine standards for excellence in education.

Dr. Hardman’s keynote promises to be both intellectually stimulating and practically actionable for educators across HR, L&D, K-12, and higher education sectors. Attendees can expect a rich blend of historical context, real-world case studies, and forward-looking strategies that illuminate AI’s transformative potential while critically evaluating its current applications. By drawing connections between past experiments, present practices, and future possibilities, Dr. Hardman aims to inspire her audience to think more strategically about how they use AI—and why human-centered design must remain at the core of educational innovation.

Ultimately, Dr. Hardman’s message is clear: while AI has the power to revolutionize learning design and delivery, its success depends as much on people as it does on technology. As educators grapple with the opportunities and challenges presented by AI, her keynote will serve as a vital guide for navigating this rapidly evolving landscape with insight, intention, and integrity.

Join Dr. Philippa Hardman at LEARNTEC 2025 for an unparalleled exploration of how strategic deployment of AI can create meaningful change in education—and why the future of learning depends on striking the right balance between technological innovation and human ingenuity.