Guevara Maldonado, César Byron
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Preferred name
Guevara Maldonado, César Byron
Main Affiliation
Quito
Email
cesarguevara@uti.edu.ec
ORCID
Scopus Author ID
56452706300

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Predictive Model to Evaluate University Students' Perception and Attitude Towards Artificial Intelligence

2024 , María Lorena Noboa Torres , Daniela Alejandra Ribadeneira Pazmiño , Daniela Paola Avalos Espinoza , Guevara Maldonado, César Byron

Artificial Intelligence is emerging as a transformative tool impacting various industries, including education As Artificial Intelligence continues to develop and gain prominence in classrooms, understanding how students perceive this integration and how it affects their educational experience becomes crucial. The aim of this research was to develop a model to predict the perception of students at Bolívar State University regarding the use and potentialities of Artificial Intelligence in the educational field. The methodology employed a factorial analysis, which represents the relationships among a set of variables. From this, a logistic regression was performed, generating an equation to identify predictors that allowed understanding student behavior based on specific characteristics such as attitude, perception, and satisfaction. As a technique for information gathering, a questionnaire composed of 25 items on a Likert scale was used, statistically validated with a Cronbach's alpha value of 0.925. The results of the model show that all covariates, except "Insecurity and fear of using artificial intelligence tools", are significant (p < 0.001). This suggests that the remaining variables are related to the dependent variable "Positive Perception of the Usefulness of Artificial Intelligence in Learning". It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations. Training can help students learn about AI and how to use it effectively and ethically.

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An adversarial risk analysis framework for software release decision support

2025 , Refik Soyer , Fabrizio Ruggeri , David Rios Insua , Cason Pierce , Guevara Maldonado, César Byron

Recent artificial intelligence (AI) risk management frameworks and regulations place stringent quality constraints on AI systems to be deployed in an increasingly competitive environment. Thus, from a software engineering point of view, a major issue is deciding when to release an AI system to the market. This problem is complex due to, among other features, the uncertainty surrounding the AI system's reliability and safety as reflected through its faults, the various cost items involved, and the presence of competitors. A novel general adversarial risk analysis framework with multiple agents of two types (producers and buyers) is proposed to support an AI system developer in deciding when to release a product. The implementation of the proposed framework is illustrated with an example and extensions to cases with multiple producers and multiple buyers are discussed

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