Recent studies show that AI-driven code generation tools, such as Large Language Models, are able to solve most of the problems usually presented in introductory programming classes. However, it is still unknown how they cope with Object Oriented Programming assignments, where the students are asked to design and implement several interrelated classes (either by composition or inheritance) that follow a set of best-practices. Since the majority of the exercises in these tools' training dataset are written in English, it is also unclear how well they function with exercises published in other languages. In this paper, we report our experience using GPT-3 to solve 6 real-world tasks used in an Object Oriented Programming course at a Portuguese University and written in Portuguese. Our observations, based on an objective evaluation of the code, performed by an open-source Automatic Assessment Tool, show that GPT-3 is able to interpret and handle direct functional requirements, however it tends not to give the best solution in terms of object oriented design. We perform a qualitative analysis of GPT-3's output, and gather a set of recommendations for computer science educators, since we expect students to use and abuse this tool in their academic work.

The broad availability and low cost of smartphones have justified their use for structural health monitoring (SHM) of bridges. This paper presents a smartphone application called App4SHM, as a customized SHM process for damage detection. App4SHM interrogates the phone’s internal accelerometer to measure accelerations, estimates the natural frequencies, and compares them with a reference data set through a machine learning algorithm properly trained to detect damage in almost real time. The application is tested on data sets from a laboratory beam structure and two twin post-tensioned concrete bridges. The results show that App4SHM retrieves the natural frequencies with reliable precision and performs accurate damage detection, promising to be a low-cost solution for long-term SHM. It can also be used in the context of scheduled bridge inspections or to assess bridges’ condition after catastrophic events.

The introduction of gamification principles into the classroom may enhance and extend the student engagement in learning activities, and therefore, contribute to a more efficient knowledge acquisition. In this paper, we report a case study whose methodology is based on awarding merit badges as a result of the successful completion of learning activities focused on either soft and hard skills. This initiative was implemented in two universities, involving 221 participants, aimed at assessing how the proposed model may contribute to students' motivation, appealing for its participation in the classroom, and measuring their stimulation for continuous study. Early stage results are promising and suggest not only a higher acceptance of the system and its effectiveness, but also, the suitability of gamification and reward systems on education. Further studies should be implemented in order to determine the effect of the reward system on students' grades.