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.

The possible applications of GenAI (Generative AI) in education alone are so manyfold and overwhelming that it is useful to have an overview of the many possibilities that are opening up. In this paper, we try to organize some of the low-hanging fruits that can help instructors, learners, and educational managers use GenAI applications to improve educational performance. For instructors, GenAI can help in gaining a deeper understanding of the topics to be taught, preparing educational materials, and facilitating the enactment phase in class. Learners can be assisted in getting personalized content and feedback, having GenAI as a participant in forums, or for self-reflection and emotions detection. Managers and other stakeholders can profit from Academic Analytics, bias detection, course repurposing, and many other uses. In this paper, we also present a use case detailing some initial actions we are implementing for a Programming with Java course. One action is to explicitly identify, in each problem set, the competencies being developed. Another one is the development of a chatbot, fine-tuned with the course material, which can be used by students as a tutor. The third action is to use a GenAI tool to generate questions aimed at assessing whether students truly grasp the programming project they have supposedly developed. AI is here to stay, in spite of the issues it opens up, and therefore it is never too early to start experimenting with it in practice.