Erosion in industry 5.0: 86 58

The hidden risks of generative AI

Authors

  • Dra. Adriana Zavala Martínez Tecnológico de Estudios Superiores de Cuautitlán Izcalli image/svg+xml Author
  • Dra. Martha Yaneth Segura Ruiz Universitaria Agustiniana, Bogotá, Colombia Author
  • Dra. María Erika Pecina Rivas Tecnológico de Estudios Superiores de Cuautitlán Izcalli image/svg+xml Author

DOI:

https://doi.org/10.51896/s0x8gr74

Keywords:

Education, cognitive offloading, artificial intelligence, Industry 5.0, critical thinking

Abstract

Technical, social, avant-garde, global, and cognitive pressures are driving the adoption of new technologies characterized by constant updates and automation most notably generative artificial intelligence (GenAI) and autonomous agents. While the stated intention is to augment and enhance human capabilities regarding complex challenges, skills, and critical thinking rather than replacing them recent empirical evidence suggests the actual trend is moving in the opposite direction, to the detriment of consumers. This study analyzes the flaws and risks stemming from the use and misuse of these cutting-edge technologies, aiming to propose intervention frameworks before they become irreversibly entrenched in a fragile structure within the technology adoption cycle. It explores phenomena such as the atrophy of technical skills, the productivity paradox, cognitive offloading, over-reliance on automated systems, and the irony of automation, drawing on evidence from randomized controlled trials and large-scale surveys conducted between 2025 and 2026. This analysis examines the critical failures and systemic risks of these cutting-edge technologies, aiming to propose regulatory and operational intervention frameworks before their negative effects become irreversibly embedded in the labor market and social fabric. Identifying these risk vectors early allows for the design of guidelines that restore human control, ensuring technology acts as a genuine complement rather than an agent of cognitive and professional erosion. Furthermore, there is high demand for courses, diploma programs, and workshops focused on critical thinking, reflecting a growing awareness of the need to pursue them.

References

Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779.

BBC Newsround. (2026, 16 de febrero). Books are back in Swedish classrooms. BBC. https://www.bbc.co.uk/newsround/74640167 (Nota: adaptado de la URL interna del portal formativo de la BBC).

Becker, J., Rush, N., Barnes, B., & Rein, D. (2025, 10 de julio). Measuring the impact of early-2025 AI on experienced open-source developer productivity. METR. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

Becker, J., Rush, N., Cunningham, T., Rein, D., & Mahamud, K. (2026, 24 de febrero). We are changing our developer productivity experiment design. METR. https://metr.org/blog/2026-02-24-uplift-update/

Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. David McKay.

Comisión Europea. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union.democracy. Greenpeace International.

Down, A. (2026, 20 de febrero). Amazon’s cloud ‘hit by two outages caused by AI tools last

Functional safety and AI systems. ISO.

Here’s the story you haven’t heard. MIT Technology Review. https://www.technologyreview.com/2025/05/20/1092682/ai-electricity-emissions footprint-data-centers/ https://www.greenpeace.org/international/story/66127/energy-environmental-impact-ai undermines-democracy/

https://www.theguardian.com/technology/2026/feb/20/amazons-cloud-hit-by-two outages-caused-by-ai-tools-last-year

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall. Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of Generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers.

Leman, M. (2026, 2 de abril). The energy and environmental impact of AI and how it undermines Naciones Unidas. (2015). Transformar nuestro mundo: la Agenda 2030 para el Desarrollo Sostenible.Resolución A/RES/70/1. O’Donnell, J., & Crownhart, C. (2025, 20 de mayo). We did the math on AI’s energy footprint. Organización Internacional de Normalización. (2023). ISO/TR 5469:2023 — Artificial intelligence —

Ozcay, D. (2026, 10 de febrero). I let AI write code for 30 days. It made me 19% slower (Study confirmed it). Stackademic. https://blog.stackademic.com/i-let-ai-write-code-for 30-days-it-made-me-19-slower-study-confirmed-it-b68b74654b5a

Shen, J. H., & Tamkin, A. (2026). How AI impacts skill formation (arXiv:2601.20245v2 [cs.CY]). arXiv. https://doi.org/10.48550/arXiv.2601.20245

Published

2026-10-22

How to Cite

Zavala Martínez, A., Segura Ruiz, M. Y., & Pecina Rivas, M. E. (2026). Erosion in industry 5.0:: The hidden risks of generative AI. INTERSOSTENIA: Science, Development and Innovation through the Convergence of Knowledge, 1(2), 55-70. https://doi.org/10.51896/s0x8gr74