Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI.

One of the main problems of the sudden digital transition to online education due to the COVID-19 pandemic is the increased isolation of students. On the other hand, one of the main goals of higher education is to develop students’ cooperative competence. This experimental study presents an online learning environment, consisting of a set of web-based resources such as virtual laboratories, interactive activities, educational videos and a game-based learning methodology. The study also examines the influence of the combination of such resources with active and collaborative learning on the improvement of students’ relationships and the development of cooperative competence. To this end, an analysis was conducted based on the data collected from a core subject of the Computer Engineering and Computer Science Engineering degree courses. The answers of an online survey (n = 289) were examined by using the structural equation modeling technique (SEM). The results suggest that the proposed learning environment has a significant and positive impact on the two dimensions of relational coordination; communication and relationships, and plays a key role in the acquisition and development of cooperative competence. Findings also indicate that effective, accurate, frequent and timely communication, positively influences on students’ relationships. Additionally, this study addresses other important issues with significant theoretical and practical implications for higher education.

Modern handheld devices and wireless communications foster new kinds of communication and interaction that can define new approaches to teaching and learning. Mobile learning (m-learning) seeks to use them extensively, exactly in the same way in which e-learning uses personal computers and wired communication technologies. In this new mobile environment, new applications and educational models need to be created and tested to confirm (or reject) their validity and usefulness. In this article, we present a mobile tool aimed at self-assessment, which allows students to test their knowledge at any place and at any time. The degree to which the students' achievement improved is also evaluated, and a survey on the students' opinion of the new tool was also conducted. An experimental group of 20- to 21-year-old nursing students was chosen to test the tool. Results show that this kind of tool improves students' achievement and does not make necessary to introduce substantial changes in current teaching activities and methodology.