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Project

KIPerWeB

AI-supported Personalization in vocational training

  • Duration:
  • Research Topics
    Other

The vocational training environment in Germany includes a tremendous multitude of courses. As such, it is challenging to find the best matching offer as an individual learner. Ideally, the selected training program should closely match my respective interests, abilities, learning goals, and current life context. An enhanced matching can not only increase learning motivation but also perceived self-efficacy and learning success.

The project KIPerWeb intends to apply AI technologies to match training offers to individual learners. The vision is to establish an automated dialogue in which learners can communicate their individual preferences and state of learning, whereupon they receive an automated recommendation for a matching training program. Additionally, the project intends to modularize trainings such that it becomes possible to recommend a finely matching combination of modules. To achieve these goals, the project intends to include both methods of statistical AI as well as symbolic AI.

The project lead is the Forschungsinstitut Betriebliche Bildung (f-bb). DFKI provides all of the AI modules for the project and is involved in the integration of AI modules in the systems of the project partners, namely the Bildungswerk der Niedersächsischen Wirtschaft (BNW), the oncampus GmbH, and the Provadis Partner für Bildung und Beratung GmbH. The Institut für betriebliche Bildung (IFBB) organizes a network of training providers in the form of a community of practice.

Partners

Forschungsinstitut Betriebliche Bildung (f-bb) Berufliche Fortbildungszentren der Bayerischen Wirtschaft (bfz) gGmbH Bildungswerk der Niedersächsischen Wirtschaft (BNW) oncampus GmbH Provadis Partner für Bildung und Beratung GmbH

Sponsors

BMBF - Federal Ministry of Education and Research

BMBF - Federal Ministry of Education and Research

Publications about the project

Alonso Palomino; Andreas Fischer; Jakub Kuzilek; Jarek Nitsch; Niels Pinkwart; Benjamin Paaßen

In: Kai-Wei Changi; Annie Lee; Nazneen Rajani (Hrsg.). Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations. Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-2024), System Demonstrations, located at System Demonstrations Track, Mexico City, Mexico City, Mexico, ACL, 6/2024.

To the publication

Benjamin Paaßen; Malwina Dywel; Melanie Fleckenstein; Niels Pinkwart

In: Alexandra I. Cristea; Chris Brown; Tanja Mitrovic; Nigel Bosch (Hrsg.). Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). International Conference on Educational Data Mining (EDM-2022), July 24-27, Durham, United Kingdom, International Educational Data Mining Society, 7/2022.

To the publication

Benjamin Paaßen; Malwina Dywel; Melanie Fleckenstein; Niels Pinkwart

In: Maria Mercedes Rodrigo; Noburu Matsuda; Alexandra I. Cristea; Vania Dimitrova (Hrsg.). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. International Conference on Artificial Intelligence in Education (AIED-2022), July 27-31, Durham, United Kingdom, Pages 132-137, LNCS, Vol. 13356, Springer, Cham, 7/2022.

To the publication