Vol. 72 No. 2 (2026): Ethics and regulation of artificial intelligence
Articles

SELF-CONTROL AND SUGGESTIBILITY IN THE USE OF ARTIFICIAL INTELLIGENCE

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  • Dulvis Mejía,
  • Juan Julio Fuente,
  • Julianny Castro
Dulvis Mejía
Investigador
Juan Julio Fuente
Universidad Nacional Pedro Henriquez Ureña
Bio
Julianny Castro
Universidad Nacional Pedro Henriquez Ureña
Bio

Published 2026-07-06 — Updated on 2026-07-06

Keywords

  • sugestionabilidad,
  • Inteligencia artificial,
  • autocontrol

Copyright (c) 2026 AULA Revista de Humanidades y Ciencias Sociales

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mejía, D., Fuente, J. J. ., & Castro, J. . (2026). SELF-CONTROL AND SUGGESTIBILITY IN THE USE OF ARTIFICIAL INTELLIGENCE. AULA Revista De Humanidades Y Ciencias Sociales, 72(2). https://doi.org/10.33413/aulahcs.2026.72i2.478

Abstract

The increasing presence of artificial intelligence (AI) in daily life raises concerns about its influence on human decision-making, particularly among people who interact frequently with digital systems. Grounded in self-control, this study aimed to explore the level of self-control required to protect individuals from AI-related suggestibility. A quantitative, non-experimental, correlational design was used with a sample of 134 participants aged 18 to 60. The Brief Self-Control Scale (BSCS-9), the Multidimensional Iowa Suggestibility Scale (MISS), and IA-specific interaction and suggestibility items were administered. Results revealed a negative association between self-control and both general and AI-related suggestibility, indicating that stronger regulatory abilities support resistance to algorithmic persuasion. Impulse control and self-discipline accounted for 36.1% of suggestibility variance, while longer AI usage time was associated with greater IA susceptibility. Sociodemographic variables such as age, education level, and gender did not significantly predict AI suggestibility. Findings suggest that self-control functions as a key psychological buffer against algorithmic influence and that vulnerability to AI depends more on internal regulatory mechanisms than on technological experience or demographic traits. These results highlight the need to strengthen self-control and critical thinking skills to promote safer and more autonomous interaction with intelligent systems.

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