The authors’ application domain is accident prevention training made accessible through their prevention center in Berlin, Germany. The audience are more than 35,000 enterprises with more than 1,6 million insured individuals. Training takes place in the authors’ original virtual environments.

The authors’ working hypothesis is that the more human-centric learning and training is, the more effective and sustainable it is.

The research question under consideration is: How to maximize learner centricity? The goal is to offer highly personalized training experiences up to the deployment of customized pedagogy. The intention of the contribution is to offer the conference audience ideas, concepts, a methodology and illustrative implementation details–all from the authors’ training modules–that might be adopted and adapted.

There are the following 7 essentials toward learner/trainee-centric technology-enhanced accident prevention training. (i) The authors have chosen a game-based learning approach to allow for a low-threshold access by a very wide audience of practitioners. (ii) They have developed their own original game concept entitled time travel prevention games. On the one hand, virtual time travel is intended to make the training attractive, worth to remember, and worth telling. On the other hand, this allows for particular didactic concepts (see below) tailored toward individual needs. (iii) The time travel approach draws advantage from the insight that mental time travel serves as a highly individual form of human knowledge representation. (iv) Game play may include self-induced accidents and varying disasters, but training never ends in a state of disaster. Every trainee experiences an own story of success. (v) Virtual time travel is highly personalized in dependence on the trainee’s prior history of game play. (vi) The digital game system is equipped, to some extent, with artificial intelligence (AI) functionalities. The game AI controls time travel, in case this becomes repeatedly necessary to impact the fate. The AI’s time travel management guides the human trainee to a personalized success. (vii) To boost the effectiveness of time travel, the authors developed a methodology to change the past of game play upon arrival, so to speak. When a trainee arrives in a certain play state of the past several times, the game AI modifies the state in a way that is perceivable to the trainee and that offers new opportunities to interact in the virtual world. The AI responds to the human game playing behavior and, in this way, implements features of a personalized didactics.

The AI modification of the past that extends the space of human (inter-)actions is a novelty presented here for the first time. Designing the space of stories to be experienced potentially is a case of dynamic plan generation. At planning time, i.e. during didactic and game design, it is not yet determined which story a human trainee might experience. The stories unfold at play time in dependence on the human’s dynamic behavior. Every human trainee traverses a personalized space of stories that evolves over play time.

The authors’ current application domain is the training of industrial accident prevention. This is a task of societal relevance to preserve human health and lives, to avoid injuries and material losses, and to protect the environment. The authors’ results may be carried over to varying application areas ranging from children’s safety to crime prevention.

Keywords: Technology-enhanced learning, game-based learning, time travel prevention games, learner centricity, trainee centricity, game AI, stories of success, story space.