This study analyzes the transition toward anticipatory public administration through the use of artificial intelligence (AI). Despite the growing deployment of predictive models, a clear research gap remains regarding the socio-technical prerequisites—such as institutional trust, data infrastructure, and ethical–legal frameworks—that condition their success. To address this, a Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines, retrieving 68 open-access articles published between 2020 and 2025 from the Scopus and Web of Science databases. Data synthesis was performed using descriptive bibliometric mapping and qualitative thematic analysis. The results show that scientific interest has experienced accelerated growth since 2024, highlighting a preference for machine learning systems that automate service delivery. While AI enables significant benefits, including a 40% reduction in budgetary errors and early fraud detection, progress is hindered by algorithmic opacity (“black box” models), the risk of structural bias, and civil servants’ confirmation bias. The study concludes that technological complexity must be subordinated to democratic safeguards. To this end, a research agenda is proposed alongside practical implications, recommending that public institutions implement mandatory independent algorithmic audits, enforce strict interpretability standards in public procurement, and adopt hybrid regulatory frameworks to ensure fair and accountable governance.

This systematic review examines recent research on self-regulated learning (SRL) in STEM disciplines in higher education, with particular emphasis on the pedagogical and technological factors that support its development. Following the PRISMA 2020 guidelines, 26 indexed and peer-reviewed studies published between 2022 and 2025 in Web of Science and Scopus were analyzed. Thematic and bibliometric analyses were conducted to identify the main research trends, theoretical frameworks, and forms of pedagogical and technological mediation. The findings suggest an asymmetrical pattern within the reviewed corpus. Although active learning and digital technologies, including learning analytics, simulations, and AI-supported tools, are increasingly used in STEM education, the evidence does not consistently demonstrate a transfer from externally scaffolded regulation to sustained learner autonomy. Cognitive-constructivist and problem-based learning frameworks were more frequently represented within the reviewed corpus, whereas co-regulated and socially shared regulation frameworks were less frequently addressed. The findings further suggest that technological sophistication alone does not consistently support SRL; rather, its contribution appears to be closely associated with the pedagogical functions assigned to digital tools and their integration into learning activities. Finally, the review identifies tensions between externally guided regulation and sustained learner autonomy, as well as concerns related to unequal access, participation, and support in STEM education.

The research focuses on identifying and analyzing the current trends in the accounting program at the Private University Antenor Orrego (UPAO) in Piura during the year 2023. It utilizes technological surveillance, a methodology that involves monitoring, analyzing, and evaluating technological and methodological advancements, to collect relevant data in the field of accounting. This approach allows the identification of new trends, opportunities, and challenges in accounting education and practice, adapting to the constant changes in the technological and business environment. Furthermore, the research proposes the redesign of research strategies in accounting. This involves reviewing and updating current research methodologies, incorporating advanced technological tools and innovative approaches to enhance the quality and relevance of accounting research. This aspect is crucial for maintaining the relevance and applicability of accounting education in a rapidly evolving business environment. The theoretical part of the research also addresses the importance of integrating technological surveillance in accounting education, preparing students for the future challenges of the field and equipping them with the necessary skills to navigate and leverage emerging technologies in their professional practices.

This research aims to assess university students' perceptions of the corporate reputation of a private university in Trujillo. Utilizing the REPTRACK Model, it explores students' views on the university's services. The study, with a mixed approach and descriptive scope, involved 264 students from Trujillo. Methods included a questionnaire and a Focus Group. Results indicated average ratings in Citizenship, Work, and Integrity (Rs=3.4), while Finance scored lower (Rs=2.99), suggesting economic management issues. However, Innovation scored higher (Rs=3.55), indicating adaptability. The Focus Group identified a medium-low reputation, affected by administrative and academic issues, but noted positives like infrastructure quality and flexible payment options for extracurricular activities. Overall, students' evaluation of the university's reputation is neutral.

This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer Nature Link. The inclusion criteria prioritized open-access, peer-reviewed English articles published between 2020 and 2025 that addressed AIoT architectures and explainability mechanisms. The screening procedure involved a dual independent review process, followed by a rigorous methodological quality assessment to minimize the risk of bias, culminating in a final sample of 40 studies from an initial pool of 971 records. The findings reveal a structural paradox: while intelligent systems achieve greater operational autonomy, legal and moral accountability remains inexorably bound to the human operator. Furthermore, 77.5% of the evaluated implementations employ superficial explainability, functioning merely as a psychological buffer to manage automation anxiety rather than providing a genuine interactive control mechanism. It is concluded that programming based on HCAI principles must shift from a post hoc feature to an inherent architectural requirement. Establishing explainability by design is imperative to guarantee an interactive audit capability that comprehensively safeguards operational integrity and preserves human agency, although the exclusive reliance on open-access literature limits visibility into proprietary commercial models.