Conference Agenda

Skin cancer, specifically melanoma, represents a growing public health challenge in Chile, with a 116% increase in the mortality rate over the last two decades. Early detection is critical, but clinical diagnostic accuracy varies significantly, and access to specialists is limited. This work presents the development of an artificial intelligence model for clinical decision support based on deep learning for the automatic classification of skin lesions (benign vs. malignant). The performance of a custom-built Convolutional Neural Network (CNN) architecture, trained from scratch, was compared to a transfer learning model based on InceptionV1.

The experimental results indicated that, while the transfer learning model achieved greater overall sensitivity, the proposed architecture attained superior accuracy (69.75% vs. 67.33%), demonstrating a greater capacity to reduce false positives. This validates the effectiveness of designing lightweight and specialized architectures, which achieve competitive and efficient performance without relying on massive pre-training, opening new avenues for the implementation of computer-assisted diagnostic tools in environments with limited computational resources.

The integration of professional standards such as the Project Management Book of Knowledge knowledge areas into graduate-level project management programmes has increased the need for robust and scalable assessment strategies. At the same time, Large Language Models have emerged as potential tools for supporting academic evaluation. This exploratory study examines the feasibility of using assisted assessment to evaluate argumentative essays structured around knowledge areas in a Master’s programme. A limited sample of student essays was analysed using predefined evaluation metrics focusing on conceptual integration, argumentative coherence, cross-domain synthesis, and alignment with knowledge area domains. Model-generated evaluations were examined to identify patterns in domain coverage, scoring consistency, and analytical depth. The study does not aim to validate automated grading but to explore methodological viability and identify strengths, limitations, and risks associated with AI-assisted evaluation in professional graduate education. Findings highlight both the potential of LLMs to detect structural alignment with the knowledge areas and the need for human oversight to ensure contextual and critical judgment. This pilot contributes to ongoing discussions on AI-supported assessment in higher education. The study analyzes essays written by postgraduate students in a PMI-aligned Master’s program and compares dimension-level scoring patterns between human evaluators and GPT-based evaluation.

Accessing relevant, context-specific project management guidance in real time remains challenging, even with comprehensive frameworks like the PMBOK Guide. Traditional documentation and training resources lack adaptability under dynamic conditions, a limitation particularly critical where consistency, traceability, and standards compliance are essential. This study develops and evaluates a Retrieval Augmented Generation (RAG) system that delivers standards aligned decision support grounded in the ten PMBOK Knowledge Areas. The knowledge base integrates PMBOK summaries, ISO 21500 documentation, case studies, and open educational resources. Using Langchain, we segmented the documents into overlapping text chunks and generated embeddings with multilingual models from HuggingFace and OpenAI. The retrieval system employed FAISS (Facebook AI Similarity Search) for vector indexing, while the generation component used DeepSeek V3 0324, while generation is performed by a large language model using structured, PMBOK-aligned prompts. Evaluation combines Precision@k retrieval metrics, expert Likert-scale ratings for clarity, relevance, and alignment, and scenario-based assessments. Results indicate strong performance in stakeholder and cost management tasks, with slightly reduced completeness in complex multi-step scenarios. The findings demonstrate the system’s potential as a real-time decision support tool for project managers and highlight its applicability in both certification training and live project environments.

This study analyzes the implementation of artificial intelligence--based chatbots as a strategy for virtual academic tutoring in higher education. A mixed-methods approach was adopted through a case study conducted in the Financial Mathematics course at the Universidad Tecnológica de El Salvador. Using the Landbot platform, conversational chatbots with structured dialogue flows were designed to provide automated academic support, activity organization, and continuous asynchronous student guidance. The results reveal a highly positive student perception regarding usability, functional usefulness, and pedagogical support, highlighting improvements in learner autonomy and academic time management. However, limitations related to response personalization and reliance on internet connectivity were also identified. The study concludes that AI-driven chatbot-based tutoring represents an innovative and scalable solution with strong potential to enhance academic support processes in virtual and asynchronous higher education environments.