Generative AI, academic skill structure and innovation
A theory-integrative model of transformation effects
DOI:
https://doi.org/10.66705/zakg0c54Schlagwörter:
Generative artificial intelligence, Academic competence structure, Scientific innovation, Distributed cognition, Recombinative searchAbstract
Competence structures and their significance for innovation remain insufficiently explained. This paper develops a theory-integrative model to analyse these relationships. The starting point is the assumption that generative systems influence epistemic core processes. These include problem recognition, ideational variation and knowledge integration. Based on approaches of distributed cognition, skill-bias technological change and innovation search, a dual transformation mechanism is derived. Firstly, generative AI reorganises academic skills architectures. Routinisable cognitive activities lose operational relevance. Evaluative, integrative and problem-formulating meta-competences gain strategic weight. Secondly, this shift in competences is changing the structure of scientific innovation. Recombinative search spaces are expanding. Incremental innovations are accelerated. Under conditions of epistemic standardisation, the probability of radical knowledge formation can decrease. Central mechanisms of action are cognitive outsourcing, algorithmically supported variation and human judgement as an instance of epistemic quality assurance.
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Copyright (c) 2026 Enrico Moch (Author)(c) Original Author(s)
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