Successful in AI didactics as a social scientist

The career of alumnus Dr. Wolfgang König impressively demonstrates what the social sciences can achieve: they do not train specialists, but rather people who understand how knowledge is created, how learning works, and how to provide guidance when the broader context is constantly shifting. Today, Wolfgang König is a pioneer in AI didactics—offering educators a didactic framework for the use of artificial intelligence in teaching. He has developed two central models: the Gray Box Model of chatbot didactics and the AI learning roadmap. He says: “The goal is not to understand the technology, but to embed its use in learning processes.”

For several years now, Wolfgang König has been working with artificial intelligence (AI), learning, and training—and with the misconceptions that regularly accompany new technologies. He shares his knowledge on platforms like LinkedIn. “Around 10,000 followers show me just how great the need is here.” He works as an expert in digital vocational education at the Bildungswerk der Wirtschaft in Mecklenburg-Vorpommern, develops concepts for learning with AI, and reaches a large professional audience with his contributions. It was not planned that this would one day be his professional focus.

His journey began as a student in Landau—at RPTU. What particularly shaped him at his alma mater was less a single subject than an attitude: “As a humanities scholar, it has become second nature to me to recognize complex patterns and translate them into something applicable to everyday life,” he says. His doctoral advisor, Professor Ulrich Sarcinelli, played a central role in this: “He was—and is—a master at reducing complexity.” Not in the sense of simplification, but in the sense of structure. This ability became a tool for him that is useful far beyond the university: a tool for making complex topics understandable without losing precision. “It’s not about breaking something down,” explains König, “but about making the structure recognizable—so that others know how to proceed.”

Between Research, the Public, and Practice

Since the beginning of his professional career, Wolfgang König has been active at the intersection of academia and the public sphere: He worked on research projects regarding citizen participation and local and administrative reform in Rhineland-Palatinate, and explored political communication and the question of how people perceive and understand political processes. Social media was not just a trendy add-on here, but a field of research with explosive social potential. “I was interested in how the public sphere functions today,” he says. This included studies on media use, on the democratic literacy of trainees, but also research on the TV debate during the federal election campaign. At the same time, he wrote as a freelance journalist—an experience that further sharpened his focus on clarity: “If no one understands what you mean, you have a problem. No matter how clever the idea is.”

“For me, AI was a stroke of luck”—“Minimal resources, huge reach”

When artificial intelligence suddenly went mainstream with ChatGPT, it wasn’t a radical new beginning for König, but rather a logical continuation of his work. “For me, AI was a stroke of luck,” he says. “Minimal resources, huge reach.” Above all, however: a technology that raises new questions about learning, knowledge, and understanding. His approach is deliberately pragmatic: “AI can help—so it must be used.” He considers bans to be of little help. Instead, he advocates teaching people how to use the technology. Learning through application, experimentation, and reflection. Or, as he puts it himself: “You can’t fatten a pig just by weighing it.”

Two Models for Learning with AI

For König, it was clear from the start: AI must not be merely a technical issue. It must be structured didactically. To this end, he developed two central models: the gray-box model of chatbot didactics and the AI learning roadmap. Both models are now an integral part of the Q* network and are used in vocational education.

The Gray Box Model: Chatbots like ChatGPT often operate like a “gray box”: their internal processes are difficult to understand. The“Grey Box Model”developed by Wolfgang König provides educators with a framework for using this technology pedagogically without having to explain every technical detail. The model combines macro-didactics—that is, integration into the curriculum—with micro-didactics, or concrete lesson planning, and supplements this with a chatbot action cycle. Learners ask questions, interpret answers, and reflect on results. “The goal is not to understand the technology, but to embed its use in learning processes,” says König.

A real-world example: In a training program for warehouse logistics specialists, chatbots are used to simulate work instructions. Learners formulate questions, analyze the chatbot’s responses, and verify them against real-world procedures. AI thus serves not merely as a tool, but as a reflection partner that actively supports the learning process.

The AI Learning Roadmap: The AI Learning Roadmap starts even earlier. It structures the entire learning process from the very beginning and makes it transparent for both learners and instructors. The first step involves clarifying the framework conditions, data protection issues, understanding of roles, and the specific learning context. This is followed by the joint definition of goals: What is to be learned, which competencies are the focus, and for what purpose is AI being used? Only then does the actual work begin. Learners interact independently with the AI, formulate questions, verify answers, reflect on interim results, and develop their competencies step by step. Finally, the results are documented, evaluated, and discussed together. Reflection is not a separate add-on but an integral part of the learning process. “The learning roadmap makes AI learning transparent and planable,” explains König. “It’s not about abstracting technology, but about making the learning process visible and controllable.” In practice, this approach enables trainees to systematically develop their own questions without feeling overwhelmed by the complexity of AI.

Tools for navigating complex systems

König’s work is driven less by enthusiasm for technology and more by a social science-based approach: He is concerned with comprehensibility, with structures, and with tools that can be provided to people to help them navigate complex systems. The fact that this tool is AI today is not a break with the past for him, but rather a logical evolution.

* Network Q is a joint project of the German Economic Institute (IW), the Business Education Association, and other educational institutions. It is funded by the Federal Ministry of Education, Family Affairs, Senior Citizens, Women, and Youth (BMBFSFJ). It prepares vocational trainers for the digital transformation through continuing education, lectures, and workshops. It strengthens digital competencies for modern training processes and future-oriented dual training programs. Wolfgang König is part of “Network Q 4.0” (www.netzwerkq.de). In addition, since March 2026, he has headed the “AI Competence” continuing education network in Mecklenburg-Western Pomerania (co-funded by the European Union).