Generative AI, academic skill structure and innovation

A theory-integrative model of transformation effects

Authors

DOI:

https://doi.org/10.66705/zakg0c54

Keywords:

Generative artificial intelligence, Academic competence structure, Scientific innovation, Distributed cognition, Recombinative search

Abstract

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. 

Author Biography

  • Enrico Moch, GrandEdu Research School

    Enrico Moch studied business administration at the University of Wismar and subsequently completed a degree in business law at Saarland University. Several years later, he completed a doctorate in economics on the topic of the influence of artificial intelligence on the world of work. His main job is as an administrative officer at the DLR Project Management Agency in the Society, Innovation and Technology division, where he is assigned to the AI Applications in Business department. In this role, he deals with the monitoring, evaluation and structuring of research and innovation projects in the context of digital technologies. Enrico Moch also works as a university lecturer at various institutions in Germany, including the Baden-Württemberg Cooperative State University Ravensburg. He also holds the position of Assistant Professor of Economics at the IICUT. He has been Academic Director of the Herford-based GrandEdu Research School for several years. His research focuses on AI governance, technical data protection and the institutional governance of digital platforms. In terms of content, his work focuses on regulatory frameworks, governance structures and the economic impact of artificial intelligence and data-driven systems. Enrico Moch regularly publishes scientific articles, is involved in interdisciplinary book projects and is active in academic peer review. He is also active in knowledge transfer between academia and practice and hosts the podcast "GrandEdu Research School - On the trail of the economy!".

References

Ahmed, A., Al-Khatib, A., Boum, Y., 2nd, Debat, H., Gurmendi Dunkelberg, A., Hinchliffe, L. J., Jarrad, F., Mastroianni, A., Mineault, P., Pennington, C. R., & Pruszynski, J. A. (2023). The future of academic publishing. Nature human behaviour, 7(7), 1021-1026. https://doi.org/10.1038/s41562-023-01637-2 DOI: https://doi.org/10.1038/s41562-023-01637-2

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333. https://doi.org/10.1162/003355303322552801 DOI: https://doi.org/10.1162/003355303322552801

Bentley, S. V. (2025). Knowing you know nothing in the age of generative AI. Humanities and Social Sciences Communications, 12, Article 409. https://doi.org/10.1057/s41599-025-04731-0 DOI: https://doi.org/10.1057/s41599-025-04731-0

Boiko, D. A., MacKnight, R., Kline, B., Gomes, G., et al. (2023). Autonomous chemical research with large language models. Nature, 624, 570-578. https://doi.org/10.1038/s41586-023-06792-0 DOI: https://doi.org/10.1038/s41586-023-06792-0

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942. https://doi.org/10.1093/qje/qjae044 DOI: https://doi.org/10.1093/qje/qjae044

Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7-19. http://www.jstor.org/stable/3328150 DOI: https://doi.org/10.1093/analys/58.1.7

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(1), eadn5290. https://doi.org/10.1126/sciadv.adn5290 DOI: https://doi.org/10.1126/sciadv.adn5290

Gilbert, S. J., Boldt, A., Sachdeva, C., Scarampi, C., & Tsai, P.-C. (2023). Outsourcing memory to external tools: A review of intention offloading. Psychonomic Bulletin & Review, 30, 60-76. https://doi.org/10.3758/s13423-022-02139-4 DOI: https://doi.org/10.3758/s13423-022-02139-4

Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174-196. https://doi.org/10.1145/353485.353487 DOI: https://doi.org/10.1145/353485.353487

Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A., Smit, B., & Laino, T. (2024). Leveraging large language models for predictive chemistry. Nature Machine Intelligence, 6, 161-169. https://doi.org/10.1038/s42256-023-00788-1 DOI: https://doi.org/10.1038/s42256-023-00788-1

Karpatne, A., Deshwal, A., Jia, X., Kumar, V., & Kumar, V. (2025). AI-enabled scientific revolution in the age of generative AI: Second NSF workshop report. npj Artificial Intelligence, 1, Article 18. https://doi.org/10.1038/s44387-025-00018-6 DOI: https://doi.org/10.1038/s44387-025-00018-6

Kobak, D., Dingemanse, M., Lakens, D., & Heathers, J. A. J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11, eadt3813. DOI: https://doi.org/10.1126/sciadv.adt3813

Kusumegi, K., Yamaguchi, T., Ito, S., & Murata, Y. (2025). Scientific production in the era of large language models. Science, 390(6661), 1240-1243. https://doi.org/10.1126/science.adw3000 DOI: https://doi.org/10.1126/science.adw3000

Lieberum, J. L., Toews, M., Metzendorf, M. I., Heilmeyer, F., Siemens, W., Haverkamp, C., Böhringer, D., Meerpohl, J. J., & Eisele-Metzger, A. (2025). Large language models for conducting systematic reviews: on the rise, but not yet ready for use-a scoping review. Journal of clinical epidemiology, 181, 111746. https://doi.org/10.1016/j.jclinepi.2025.111746 DOI: https://doi.org/10.1016/j.jclinepi.2025.111746

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71-87. https://www.jstor.org/stable/2634940 DOI: https://doi.org/10.1287/orsc.2.1.71

Mishra, T., Sutanto, E., Rossanti, R., Pant, N., Ashraf, A., Raut, A., Uwabareze, G., & Oluwatomiwa, A. (2024). Use of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers. Scientific Reports, 14, Article 31672. https://doi.org/10.1038/s41598-024-81370-6 DOI: https://doi.org/10.1038/s41598-024-81370-6

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586 DOI: https://doi.org/10.1126/science.adh2586

Tian, J., & Zhang, R. (2025). Learners’ AI dependence and critical thinking: The psychological mechanism of fatigue and the social buffering role of AI literacy. Acta Psychologica, 260, 105725. https://doi.org/10.1016/j.actpsy.2025.105725 DOI: https://doi.org/10.1016/j.actpsy.2025.105725

Zhang, Y., Khan, S. A., Mahmud, A., Alshahrani, M., & Alqahtani, S. (2025). Exploring the role of large language models in the scientific method: From hypothesis to discovery. npj Artificial Intelligence, 1, Article 14. https://doi.org/10.1038/s44387-025-00019-5 DOI: https://doi.org/10.1038/s44387-025-00019-5

Downloads

Published

15.04.2026

Issue

Vol. 1 No. 1 (2026)

Section

Articles

License

Copyright (c) 2026 Enrico Moch (Author)

(c) Original Author(s)

The original authors retain all the licensing and copyrights and any other rights to the published research. The original authors agree to publish their research under the Creative Commons CC BY ND 2.0 License (Terms of License).

How to Cite

Moch, E. (2026) “Generative AI, academic skill structure and innovation: A theory-integrative model of transformation effects”, Journal of Entrepreneurship and Global Business Strategy, 1(1), pp. 1–21. doi:10.66705/zakg0c54.