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Item type:Publication, Applying profit-driven metrics in predictive models: A case study of the optimization of public funds in Peru(Success Culture Press, 2022-01-01)Fund allocation is a crucial concern in public management, as it is an important factor for economic performance in public investment. Governments spend substantial resources to improve these investments’ efficiency and effectiveness. The use of Machine Learning techniques has proven to be a relevant tool in the decision-making process. In this study we test an approach based on a profit-driven perspective, in order to assess predictive models in public resource allocation, valuing the net profit obtained by the allocation of funds for Peruvian researchers to have a specific performance measure to the fund allocation. A series of experiments were developed using data from 24 Peruvian universities. The use of a profit-driven metric allows to make better choices regarding predictive models and reaching better performance in public investment for Peruvian government. Use of Machine Learning techniques supports the correct identification and selection of researchers to optimally allocate limited resources in an emerging country and shows a novel use of predictive models in public management. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Professional Obsolescence Anxiety and the Use of Generative AI in Engineering Students: A Sequential Mediation Model(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)Our study investigates the paradoxical relationship between professional obsolescence anxiety and the use of generative artificial intelligence (GAI) among engineering students, exploring the psychological mechanisms underlying why those who fear losing their jobs to technology are less likely to adopt AI tools. A sequential mediation model was tested using structural equation modeling (PLS-SEM) with 258 engineering students from Peru. The model examined the relationship between professional obsolescence anxiety, AI usage frequency, and the mediating factors of technological Self-Efficacy and performance expectations. Bootstrap confidence intervals were used to assess the indirect effects. Results revealed a significant sequential mediation pattern where professional obsolescence anxiety negatively affects technological Self-Efficacy (β = -0.116, p < .05), which then positively affects performance expectations (β = 0.525, p < .001), ultimately increasing AI use frequency (β = 0.609, p < .001). Notably, the study identified an inconsistent suppression phenomenon where direct effects (+0.077) and indirect effects (-0.068) work in opposite directions, resulting in no significant overall effect. The full mediation model showed a better fit compared to partial or no-mediation models. This research provides new insights into the technology adoption paradoxes by demonstrating that anxiety about professional obsolescence generates internal psychological conflicts that both encourage and hinder AI adoption. The findings have important implications for engineering education, indicating that interventions should focus on enhancing Technological Self-Efficacy rather than just providing access to AI tools. However, these findings should be interpreted cautiously due to the cross-sectional design and the single-institution sample, highlighting the need for replication in diverse educational contexts.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, More AI, Less Learning? The Role of Technological Self-Efficacy in STEM Education(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)The increasing adoption of generative AI tools, such as ChatGPT and Copilot, is transforming how STEM students approach their studies. Yet, the key question remains: Does the use of AI enhance academic performance? This research analyzed survey data from over 250 undergraduate STEM students at a private university in Lima, Peru, to investigate whether there is a correlation between AI usage and self-reported academic achievement. Using ordinal logistic regression, we explored three models: (1) AI use alone, (2) AI combined with self-perceived technological Self-Efficacy, and (3) a moderation model considering prior AI experience. The findings suggest that frequent use of AI does not lead to improved academic results; in some cases, it even correlates with lower performance. In contrast, students with higher technological Self-Efficacy generally performed better, regardless of how often they used AI. These results underscore the importance of higher education in going beyond mere access to AI, emphasizing the development of digital skills for effective tool use. The study discusses the implications for STEM education, highlighting the value of competence-based approaches to integrating digital technology.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Impact of Perceived Risks on Resistance to Generative AI in Engineering Education(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)The integration of generative artificial intelligence in higher education has created a paradoxical scenario: while these tools offer evident learning benefits, many students choose not to use them. This study investigates whether perceived risks associated with the use of generative AI - such as the loss of critical thinking skills, technological dependency, and distrust in AI outputs - act as significant barriers to adoption in engineering academic contexts. Through a survey of 258 engineering students from Pontificia Universidad Católica del Perú, an ordinal logistic regression model was implemented to analyze the relationship between perceived risks and self-reported levels of generative AI use. The theoretical framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Technology Threat Avoidance Theory (TTAT), providing a comprehensive perspective that explains both motivating and inhibiting factors of technology adoption. Results reveal that higher levels of perceived risk significantly predict lower usage of generative AI, with this effect being more pronounced among students with lower academic performance. Contrary to expectations, high-performing students tend to use these tools less, possibly due to greater ethical awareness or academic caution. These findings suggest that non-adoption of technology may represent a rational protective strategy against perceived threats, rather than simple resistance to change. The implications for engineering education are substantial: institutions must develop clear policies that address legitimate student concerns, implement digital literacy programs that promote the ethical and responsible use of AI, and design differentiated strategies that consider diverse student academic profiles. Furthermore, addressing the adoption gap between high and low-performing students is crucial to ensure equitable access to AI-enhanced learning opportunities.2