## 4 Discussion  
In compliance with **Item 23a** of the PRISMA guide, this section will be devoted to the general interpretation of the findings obtained in the context of other relevant evidence. In order not to be limited only to listing the identified transversal competencies, the discussion on how to generate them in a sustainable way using Generative Artificial Intelligence (GenAI) in higher education is expanded, thus providing a greater added value to this systematic review. The analysis of the included studies allows the identification of five strategic axes that, together, contribute to the sustainability of GenAI to develop transversal competencies in a lasting manner. First, the critical and ethical appropriation of GenAI is revealed as an indispensable condition to avoid its uncritical use, guarantee academic integrity, and promote digital responsibility. This conclusion aligns with previous research that underlines the urgency of establishing solid ethical frameworks and digital literacy programs in artificial intelligence for the university environment. Thus, higher education institutions should foster critical digital literacy throughout the institution to ensure that both staff and students can use GenAI responsibly and ethically [63]. Secondly, institutional management of the technological infrastructure is seen as an enabling base. Available evidence shows that, without adequate and equitable technical support, GenAI integration tends to be restricted to fragmented experiences, limiting its systemic impact. Institutions should create comprehensive policies that address the ethical use of GenAI, ensuring transparency and accountability [64]. Building a robust technology infrastructure is critical to successful AI integration. This includes investing in cutting-edge technology and ensuring scalability to adapt to future growth [65, 66]. Key challenges include resource constraints, data complexity, and the need for scalable solutions. In addition, institutions in developing regions may face unique challenges related to cost, expertise, and access to technology [67, 68]. Third, teacher development for sustainable integration stands out as a key strategy for strengthening institutional capacities. The studies reviewed agree that teachers must be accompanied in their process of adaptation to the new tools, not only in the technical domain but also in their pedagogical, ethical, and reflective dimensions. Thus, [PERSON]'s study [69] directly supports the teacher development axis for the sustainable integration of AI in higher education by showing that teachers require not only technological but also pedagogical and ethical knowledge to integrate AI tools effectively. His proposal for an Intelligent-TPACK framework reinforces the need for comprehensive teacher training that combines these skills, thus ensuring a reflective, ethical, and pedagogically relevant use of AI in educational contexts. [PERSON]'s work [70] links to the axis of teacher development for sustainable AI integration by highlighting that the effectiveness of intelligent tutors depends not only on the software but also on teacher preparation. The author stresses that the lack of teacher training has been a key barrier to the adoption of these technologies in education. Thus, the development of teaching skills in the pedagogical and adaptive use of intelligent systems becomes essential to take full advantage of their transformative potential in higher education. In this regard, one study points out that, although AI is increasingly applied in educational processes, many research studies lack a clear pedagogical basis, which underlines the importance of training teachers not only in technical aspects but also in formative and ethical approaches for meaningful and sustainable integration [71]. Fourth, curriculum transformation mediated by GenAI is a fundamental area for renewing content, methodological approaches, and evaluation criteria, responding to new learning scenarios.