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Learning on the job: a future model

High qualifications are in demand in the production sector. But what is the best way to learn on the job? A white paper shows that many opportunities are currently still unused here.

Everything changes. The training with which one began one's professional life is often no longer sufficient years later. To alleviate the shortage of skilled workers that industry is complaining about today, smart education and training strategies are needed: learning and working belong together. A team from TU Vienna, together with researchers from TU Graz, TU Darmstadt and Fraunhofer Austria, as part of a working group of the International Association of Learning Factories, opens an external URL in a new window (IALF), has now published a white paper on work-based learning in the manufacturing industry.

It is a call for collaboration between industry and educational institutions: In a world with artificial intelligence and modern integrated learning methods, we should no longer settle for advanced training strategies from the last century. It is also a call for better integration of already existing knowledge about continuing education into the daily practice of industry. And it's a call for a people-centric industry, where you don't just think about production efficiency, but also about the needs and satisfaction of all the people involved - that's the only way to achieve long-term success.  

Learning in the learning factory

"There is a lot of research on how employees in a manufacturing company best learn important new skills," says Priv.-Doz. Dr.-Ing. Fazel Ansari of the Institute of Management Sciences at Vienna University of Technology. "For example, there are a whole range of learning factories where you can improve various skills in a practical way. Large companies often even have their own learning factories, or production facilities specifically for continuing education."

Still, there is much room for improvement, the research team believes. "Above all, we also found that there are very big differences between different industries here," says Steffen Nixdorf, who is currently working on his dissertation in Ansari's team. The pharmaceutical industry and the automotive industry are more innovative in this respect, while in the textile industry there is still rather little integrated learning on the job.

Artificial intelligence gives tips, humans decide

New technologies will also play an increasingly important role in professional development. "Artificial intelligence is a huge topic with us," says Fazel Ansari. "There are huge amounts of data accumulating in industrial operations that can be used intelligently."

For example, you can train an AI agent, such as a learning voice assistant, to look for similar problems in the past when a problem arises and then show the human in charge right away how the problem was solved back then. However, this requires a certain kind of reciprocal learning: artificial intelligence and machine both learn at the same time, adjust to each other and end up solving tasks that neither humans nor machines could solve on their own. At TU Wien, research is being conducted into how these learning processes can best be observed, measured, quantitatively assessed and thus also improved.

Social sustainability

The research team also sees a great need for other innovative ideas - for learning assistance systems, for demonstrator systems, for new, intelligent information provision - why do you need an info sheet when it can also be a podcast? "We are now striving to implement the topic of work-based learning in innovative solutions as part of application-oriented basic research," says Steffen Nixdorf.

The team sees the necessary rethinking of work-integrated learning as part of a larger transformation. "We are concerned with sustainability," says Fazel Ansari. "Thinking about sustainability in terms of the environment and climate, of course, but we're also about social sustainability at the same time." The production of the future is to be massively based on automation and artificial intelligence, but at the same time human-centered. Human tasks will be taken over by machines, but at the same time, humans will take on new tasks - ones that require more oversight, insight and feeling.

White Paper

Nixdorf, S., Ansari, F., Schlund, S., Wolf, M., Hulla, M., Papa, M., Bardy, S., Kress, A., & Rosemeyer, J. (2022). Work-Based Learning in Manufacturing Industry. A Sector-Based Meta-Analysis (1.0.0). TU Wien., opens an external URL in a new window


Dipl.-Ing. Steffen Nixdorf
Institute of Management Sciences
TU Wien
+43 1 58801 33047

Dr. Fazel Ansari
Institute of Management Sciences
TU Wien
+43 1 58801 33049

Dr. Florian Aigner
TU Wien
PR und Marketing
Resselgasse 3, 1040 Wien