Master of One's Own Acts: Thomas Aquinas and Delegated Decision in Generative Artificial Intelligence

This essay argues that generative artificial intelligence can displace students from the very act that, according to Thomas Aquinas, makes an action human: deciding with deliberation. For Aquinas, the human being differs from irrational creatures in being master of his own acts, and is so through reason and will (Summa theologiae I-II, q. 1, a. 1). The medieval controversy over the unity of the intellect, as Aquinas reconstructs it against the Averroists, also offers a structure for thinking about what happens when an intellectual operation is entrusted to a system that is external and shared by millions of people. The argument is a functional analogy: it attributes neither intellect nor will to the machine. Recent empirical literature shows that generative AI supports critical thinking when it is used in guided and reflective ways, and weakens it when it replaces the student's deliberation. The argument does not oppose delegation as such, since learning has always relied on external supports; it distinguishes supports that expand the student's capacity to understand and decide from uses that replace it. The essay proposes a pedagogical response that recovers the scholastic disputatio: the AI raises objections, and the student answers, verifies, and decides. The proposal is complemented by objectives, competencies, and an assessment at three points: before, during, and after the use of the tool. Preprint v1.0, not peer reviewed. Author: Antonio Carlos González-López, independent researcher (ORCID 0009-0004-4568-3842).

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-06
DOI
https://doi.org/10.5281/zenodo.23173651
Primary Topic
Artificial Intelligence in Education
Type
preprint
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Master of One's Own Acts: Thomas Aquinas and Delegated Decision in Generative Artificial Intelligence

González-López Antonio Carlos
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Education
preprint

Master of One's Own Acts: Thomas Aquinas and Delegated Decision in Generative Artificial Intelligence

González-López Antonio Carlos
preprint en

Abstract

This essay argues that generative artificial intelligence can displace students from the very act that, according to Thomas Aquinas, makes an action human: deciding with deliberation. For Aquinas, the human being differs from irrational creatures in being master of his own acts, and is so through reason and will (Summa theologiae I-II, q. 1, a. 1). The medieval controversy over the unity of the intellect, as Aquinas reconstructs it against the Averroists, also offers a structure for thinking about what happens when an intellectual operation is entrusted to a system that is external and shared by millions of people. The argument is a functional analogy: it attributes neither intellect nor will to the machine. Recent empirical literature shows that generative AI supports critical thinking when it is used in guided and reflective ways, and weakens it when it replaces the student's deliberation. The argument does not oppose delegation as such, since learning has always relied on external supports; it distinguishes supports that expand the student's capacity to understand and decide from uses that replace it. The essay proposes a pedagogical response that recovers the scholastic disputatio: the AI raises objections, and the student answers, verifies, and decides. The proposal is complemented by objectives, competencies, and an assessment at three points: before, during, and after the use of the tool. Preprint v1.0, not peer reviewed. Author: Antonio Carlos González-López, independent researcher (ORCID 0009-0004-4568-3842).

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Education
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Master of One's Own Acts: Thomas Aquinas and Delegated Decision in Generative Artificial Intelligence — González-López Antonio Carlos · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS