Miguel Ángel Hombrados-Herrera's research while affiliated with University Carlos III de Madrid and other places

The emergence of Large Language Models (LLMs) has marked a significant change in education. The appearance of these LLMs and their associated chatbots has yielded several advantages for both students and educators, including their use as teaching assistants for content creation or summarisation. This paper aims to evaluate the capacity of LLMs chatbots to provide feedback on student exercises in a university programming course. The complexity of the programming topic in this study (concurrency) makes the need for feedback to students even more important. The authors conducted an assessment of exercises submitted by students. Then, ChatGPT (from OpenAI) and Bard (from Google) were employed to evaluate each exercise, looking for typical concurrency errors, such as starvation, deadlocks, or race conditions. Compared to the ground-truth evaluations performed by expert teachers, it is possible to conclude that none of these two tools can accurately assess the exercises despite the generally positive reception of LLMs within the educational sector. All attempts result in an accuracy rate of 50%, meaning that both tools have limitations in their ability to evaluate these particular exercises effectively, specifically finding typical concurrency errors.

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.