From the perspective of a descriptive analysis, and as a starting point to a new research line, this paper examines the potential impact Generative Artificial Intelligence (GAI) technologies may have on academic integrity, manifested in the learning and evaluation processes of law classes at the university level. The article takes as its premise the definition of academic integrity as a set of values and argues that a series of risks arise from the GAI that threaten those values, such as excessive dependence and trust in the GAI, the unreallizability of the pedagogical project and the loss of competitiveness of educational institutions, among others. To minimize or nullify such risks, and thus prevent them from affecting academic integrity, four mitigation measures are identified to be applied in university environments.

From the perspective of a descriptive analysis, and as a starting point to a new research line, this paper examines the potential impact Generative Artificial Intelligence (GAI) technologies may have on academic integrity, manifested in the learning and evaluation processes of law classes at the university level. The article takes as its premise the definition of academic integrity as a set of values and argues that a series of risks arise from the GAI that threaten those values, such as excessive dependence and trust in the GAI, the unreallizability of the pedagogical project and the loss of competitiveness of educational institutions, among others. To minimize or nullify such risks, and thus prevent them from affecting academic integrity, four mitigation measures are identified to be applied in university environments.

From the perspective of a descriptive analysis, and as a starting point to a new research line, this paper examines the potential impact Generative Artificial Intelligence (GAI) technologies may have on academic integrity, manifested in the learning and evaluation processes of law classes at the university level. The article takes as its premise the definition of academic integrity as a set of values and argues that a series of risks arise from the GAI that threaten those values, such as excessive dependence and trust in the GAI, the unreallizability of the pedagogical project and the loss of competitiveness of educational institutions, among others. To minimize or nullify such risks, and thus prevent them from affecting academic integrity, four mitigation measures are identified to be applied in university environments.

This paper examines the potential of artificial intelligence (AI) technologies to function as a criminogenic factor, namely as causes of criminal phenomena. The analysis distinguishes two perspectives from which this role may materialize. From a microcriminogenic perspective, it is argued that AI possesses the technical capacity to become directly or indirectly involved in criminal offenses or in conduct that, due to its dangerousness or harmfulness, should be criminalized. From a macrocriminogenic or structural standpoint, it is posited that AI-driven capitalism may increase social conflict or lead to an intensified reliance on the criminal justice system to protect the economic order built around AI.

This paper examines the potential of artificial intelligence (AI) technologies to function as a criminogenic factor, namely as causes of criminal phenomena. The analysis distinguishes two perspectives from which this role may materialize. From a microcriminogenic perspective, it is argued that AI possesses the technical capacity to become directly or indirectly involved in criminal offenses or in conduct that, due to its dangerousness or harmfulness, should be criminalized. From a macrocriminogenic or structural standpoint, it is posited that AI-driven capitalism may increase social conflict or lead to an intensified reliance on the criminal justice system to protect the economic order built around AI.