The concept of hyper-personalization originally comes from the realm of marketing and seeks to develop the closest possible long-term relationship with the customer. How should we comprehend "hyper-personalization" in the individual learning context?

Theresa Voss: Hyper-personalization in the context of individual learning opens the door to a world in which rigid teaching methods are a thing of the past. Each learner is unique - with individual needs, talents, and preferences. Instead of dictated, uniform learning paths and generic content, we view them as individual learners with personal paths.

Thanks to AI, we can now design novel learning experiences that flexibly adapt to each individual's needs, abilities, and learning styles.

This personalization not only strengthens the connection between learners and learning content, but also maximizes learning success by precisely addressing individual strengths and weaknesses. Intrinsic motivation in particular is stimulated here: the more precisely the content is tailored to the individual and the more influence we have on it, the greater the motivation.

If we look at social media, for example, we actively influence what is displayed through a wealth of data that reflects user behavior. This may seem scary at times, but on the other hand, it also sets the standard and expectations.

Personally, I am a big advocate of the proper and ethical use of technology. We are on the brink of a revolution in education, and we should use this opportunity to focus on the essentials: sustainable and personalized education for all.

Medicine is one of the largest fields of AI application. How can it meaningfully be used to link AI in training and application?

Theresa Voss: AI has already made impressive progress in medicine by improving diagnoses, personalizing treatments, and optimizing therapy recommendations. These achievements provide valuable insights that can be adapted for further training. By applying the methods and technologies from the field of medical AI to learning and professional development, personalized learning experiences can be created that are tailored to each individual's needs and goals.

For example, machine learning algorithms can be used to analyze learning behavior and suggest personalized learning paths that take each employee's individual learning style and preferences into account. In addition, Natural Language Processing can be used to analyze learning content and create customized learning content that helps learners continuously improve their knowledge and skills.

Ultimately, the integration of AI fosters the development of innovative approaches for corporate learning that help firms and their employees operate successfully in an increasingly digital and complex working world.

Which AI tools are used for hyper-personalization in corporate learning?

Theresa Voss: Hyper-personalization in corporate learning, involves the use of various AI tools to adapt and optimize the learning experience for each individual:

• Machine learning algorithms- These analyze the learning behavior of participants and provide personalized recommendations. For example, they can predict which learning content is most relevant for an employee or which learning method might be most effective based on previous interactions and learning success.
• Natural Language Processing (NLP) - NLP is used to understand learning content and provide personalized feedback. AI systems can answer questions, provide feedback, or even automatically generate learning materials tailored to individual needs.
• Data analytics tools - These monitor learning progress, capture performance metrics, and provide insights into learning behavior. By analyzing data, companies can identify patterns and trends to continuously improve their learning programs.
• Intelligent chat systems - These use AI to support the learning process. They can create personalized learning paths, set individual learning goals and provide adaptive learning materials that adjust to the progress and needs of each individual.

The integration of these AI tools enables companies to offer hyper-personalized learning experiences, which in turn enables employees to learn and develop more effectively, while increasing operational efficiency and productivity.

chunkx, the startup you co-founded, has made a name for itself with continuous and personalized microlearning. Will you be offering AI-based learning assistants for medical training?

Theresa Voss: Yes, chunkx offers an AI-based co-pilot for continuous learning. However, healthcare is just one of many application areas, and we go even further: We firmly believe that we can achieve more with technology than just offering personalized learning profiles or paths. Our focus is on the individual creation of learning content for each employee, i.e. hyper-personalized.

The way we think about learning is not only revolutionary; it also offers massive benefits for all parties involved. Employees are supported individually, and in the process, fascinating data on development or skill profiles can be collected, which conserves corporate resources.

By using an individual data structure and AI, we are able to create personalized and didactically well-prepared learning content that adapts to individual needs, learning styles, and progress. Employees can augment their learning content with the help of chatbots, ask questions about the content, or simply complete their microlearning units. This is not only relevant for the healthcare industry, but also for others such as the automotive, manufacturing or telecommunications sectors, in which we are also active.

Have you already gathered any experience?

Theresa Voss: Yes, our solutions have been used successfully in various scenarios and industries, such as employee onboarding, innovation topics, and product training. The data reports we have received have shown that our personalized approaches are very well received and have permitted continuity in the learning process that cannot be achieved with conventional LMS systems or generic and static content platforms.

It has become clear that the choice offered by these systems and providers is overwhelming, but, unfortunately, it has inadvertently also led to irrelevant and unspecific content. At chunkx, we are continuously working with stakeholders to improve our hyper-personalized learning-content creation, for example by promoting media-rich processing and the integration of company-specific content sources such as the intranet, presentations, or conventional documentation.