Conference Agenda

The objective of this systematic literature review was to examine how artificial intelligence (AI) impacts human talent management and its effect on organizational sustainability. The methodology employed was a systematic literature review, with the PRISMA method used for selecting and filtering studies. This allowed the analyzed studies to demonstrate that artificial intelligence contributes to optimizing human resource processes through the use of data analytics, automation, and decision support, leading to improvements in organizational performance and effectiveness. Furthermore, the review indicates that its proper application has the potential to strengthen sustainability in its economic, social, and environmental aspects. However, challenges related to ethical, cultural, and technical issues continue to restrict its effective implementation. This necessitates a more comprehensive and responsible approach.

This paper presents CHIMLE, an intelligent chatbot prototype based on a Large Language Model (LLM) designed to interact directly with institutional student academic databases. The proposed system supports natural language-based access to academic information, automated and personalized report generation, responses to frequently asked questions, and assistance with administrative processes. Its objective is to enhance accessibility, usability, and operational efficiency in academic information management within higher education institutions. The solution is implemented in Google Colaboratory integrating a generative language model with SQL-based tools that allow secure and dynamic interaction with structured institutional data. The architecture supports text-to-SQL translation, multi-turn conversational queries, and real-time data retrieval. The prototype was evaluated through a set of representative academic queries related to student performance, course information, and aggregated statistics. The results demonstrate that CHIMLE can accurately interpret user intentions and generate reliable responses with low execution time, highlighting its potential as a flexible and scalable support tool for academic environments. This work extends a previously reviewed preliminary version by incorporating refined architecture, a complete implementation, additional functional tests, and an expanded discussion in relation to recent advances in LLM-based educational chatbots.

This research evaluated the impact of an intelligent system with computer vision to improve avocado crop management in an agricultural entity during the year 2025. To this end, a pre-experimental design was applied with a sample of 15 workers selected according to inclusion criteria. The methodology included data collection using structured questionnaires, which were processed in XLSTAT software using Student's t-test and Wilcoxon's test. The statistical analysis showed substantial improvements in key management indicators: information availability increased by 50.18%, operational planning increased by 45.16%, monitoring frequency improved by 35.32%, and resource efficiency increased by 28.42%. These findings confirm that the proposed technological solution significantly optimizes monitoring and decision-making processes within the agricultural organization.

This study addressed the challenges of efficiency and accuracy in quality inspection at bottling plants, where manual methods lead to errors, high costs, and risks to brand reputation. An automated system based on artificial vision was implemented and evaluated to identify filling defects in water bottles at the Cielo brand plant in Iquitos during 2025. The objective was to determine the relationship between the implementation of the system and the improvement in defect detection. A quantitative and experimental approach was used, collecting and preprocessing a set of image data to train and validate a model from the YOLO family in Python. The model's performance was evaluated using metrics such as Mean Average Precision (mAP), accuracy, and recall. The results demonstrated high effectiveness in identifying bottles in good condition (87% recall) and moderate ability to detect defects (52% recall), reaching an inference speed of 30 FPS, suitable for real-time operation. It is concluded that the system is technically viable for improving the speed and accuracy of quality control, although further training is required to reduce the false negative rate in defective bottles.

Abstract– This paper addresses the use of artificial intelligence in voice analysis to recognize the emotions experienced by university students. The objective of this project was to create a voice recognition prototype to improve students' emotional well-being and enrich their learning process. The study highlights the importance of AI in education for comprehensively evaluating student participation, intrinsic motivation, and emotional well-being—essential factors for their holistic development. The research was quantitative and experimental, conducted with first-year university students. A total of 4,723 audio recordings were collected and categorized into six emotions: happy, sad, angry, scared, surprised, and neutral. Various techniques were then applied to balance and expand the model's training dataset. The trained machine learning model performed excellently, achieving 98% effectiveness in training and 91% in validation. Neutrality and Sadness were found to be recognized with high consistency; in contrast, Surprise was the most difficult emotion to recognize. The findings highlighted the influence of these emotions on the learning process, providing evidence for the subsequent development of a Socio-emotional-Constructivist Pedagogical Model that integrates pedagogical strategies to develop more comprehensive, supportive, and effective learning environments.

Intelligence (AI) has achieved remarkable progress in recent decades, mainly driven by parallel computing architectures such as Graphics Processing Units (GPUs). However, as AI models grow in complexity and energy demand, GPUs show fundamental limitations in scalability and biological plausibility, restricting further progress toward Artificial General Intelligence (AGI). In contrast, neuromorphic computing reproduces the dynamics of biological neural systems through event-driven and massively parallel architectures, offering ultra-low-power processing and adaptive learning capabilities.

This paper presents a Systematic Literature Review (SLR) following PRISMA 2020 and PICOC frameworks to identify how SpiNNaker, a brain-inspired architecture from the University of Manchester, contributes to AGI research when compared with GPUs. The review considers studies published between 2024 and 2025 retrieved from Scopus, resulting in 36 primary sources after applying inclusion and exclusion criteria. Findings show that SpiNNaker and related neuromorphic chips demonstrate energy-efficiency improvements of up to two orders of magnitude, enhanced scalability, and real-time adaptive behavior. These results confirm that neuromorphic hardware represents a feasible path toward sustainable and biologically plausible AGI systems.