This paper explores the application of large language models (LLMs) to automate the evaluation of programming assignments in an undergraduate “Introduction to Programming” course. This study addresses the challenges of manual grading, including time constraints and potential inconsistencies, by proposing a system that integrates several LLMs to streamline the assessment process. The system utilizes a graphic interface to process student submissions, allowing instructors to select an LLM and customize the grading rubric. A comparative analysis, using LLMs from OpenAI, Google, DeepSeek and ALIBABA to evaluate student code submissions, revealed a strong correlation between LLM-generated grades and those assigned by human instructors. Specifically, the reduced model using statistically significant variables demonstrates a high explanatory power, with an adjusted R² of 0.9156 and a Mean Absolute Error of 0.4579, indicating that LLMs can effectively replicate human grading. The findings suggest that LLMs can automate grading when paired with human oversight, drastically reducing the instructor workload, transforming a task estimated to take more than 300 h of manual work into less than 15 min of automated processing and improving the efficiency and consistency of assessment in computer science education.

The proposed system assists in the automatic creation of three-dimensional (3D) meshes for all types of objects, buildings, or scenarios, using drones with monocular RGB cameras. All these targets are large and located outdoors, which makes the use of drones for their capture possible. There are photogrammetry tools on the market for the creation of 2D and 3D models using drones, but this process is not fully automated, in contrast to the system proposed in this work, and it is performed manually with a previously defined flight plan and after manual processing of the captured images. The proposed system works as follows: after the region to be modeled is indicated, it starts the image capture process. This process takes place automatically, with the device always deciding the optimal route and the framing to be followed to capture all the angles and details. To achieve this, it is trained using the artificial intelligence technique of Double Deep Q-Learning Networks (reinforcement learning) to obtain a complete 3D mesh of the target.

Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address this problem by optimizing last-mile delivery processes using a genetic algorithm (GA) designed to minimize rerouting costs while respecting customer time preferences. The study compares the performance of the proposed GA with a Simulated Annealing (SA) algorithm, assessing their efficiency in route optimization. Through detailed simulations, GA reduces operational costs by over 35,000 euros annually by considering customer preferences. It significantly outperforms the SA algorithm in scenarios with high customer variability, highlighting its potential for cost-efficient last-mile delivery solutions. Additionally, the GA consistently respected 4–7 more customer preferences per route compared to traditional methods, leading to enhanced customer satisfaction. This work contributes to the field by providing a robust methodology for balancing cost efficiency and user satisfaction in last-mile deliveries, offering actionable insights for logistics optimization.

The increasing adoption of Large Language Models (LLMs) in higher education has created a need for high-quality, publicly available benchmarks for automated assessment. Existing datasets often rely on synthetic responses or lack detailed human feedback. This paper presents a multilingual dataset of 3041 authentic student answers to 50 open-ended Computer Science questions, collected from real university assessments during the 2025–2026 academic year. The dataset includes the original student responses (Spanish) and their parallel translations (English), instructor (or teacher) defined ideal answers, blind human grading with qualitative feedback, and structured evaluations from three state-of-the-art LLMs (DeepSeek-chat-V3.2, Qwen-flash-2025-07-28, Gemini-2.5-flash-lite-001) using a unified JSON schema. This resource enables reproducible research in automated grading, feedback generation, and cross-lingual educational NLP.

Information about the paper titled "INTRODUCING SYSTEMS THINKING SKILLS TO IMPROVE THE LEARNING PROCESS ABOUT COMPLEXITY AND COMPLEX SYSTEMS" at IATED Digital Library