## 1 Introduction  
The rise in generative artificial intelligence (GenAI) marks one of the most transformative technological shifts in recent decades, reshaping how individuals learn, communicate, and solve problems. Its rapid proliferation underscores the need to reassess the skills required for citizens to interact effectively with these advanced tools. This new scenario began when the company OpenAI (San Francisco, CA, USA) launched ChatGPT (version 3.5) in 2022. However, other tech giants such as Google (Mountain View, CA, USA), Microsoft ([PERSON], WA, USA), and Meta (Menlo Park, CA, USA) already have their own GenAI platforms, widely accessible to people around the world. The rapid development of artificial intelligence (AI) has brought notable changes to various fields, particularly education. Within this context, ChatGPT has gained prominence due to its advanced conversational capabilities and its potential to enhance teaching and learning processes. As a chatbot powered by AI, ChatGPT can offer tailored and interactive assistance to students by addressing complex queries, providing feedback on academic tasks, and facilitating open-ended dialog. Thanks to its flexibility and practical applications, this tool holds the potential to reshape traditional models of higher education [1]. These advancements are referred to as large language models (LLMs), which are trained on vast volumes of text to process and generate natural language. They are considered part of the field of natural language processing (NLP), which enables models to 'understand,' generate, and analyze human language. This represents a significant advantage, as it allows users to interact with LLMs using natural language rather than code, removing the need for programming skills to effectively engage with them. Typically, the process of interacting with a GenAI involves a user providing information in various formats, such as text or audio, through a prompt. The GenAI then processes the input using a large language model and generates an output, also in multiple formats. The user does not know how the GenAI analyzes their input to transform and generate the output. Therefore, the only way to interact with the GenAI is by using the prompt. Various authors [2,3] point out that managing the use of prompts is a skill that can be acquired and improved by following certain rules to enhance the richness of grammatical information provided in the input, incorporating context, roles, output formats, and other aspects. In this context, concepts such as prompt engineering, prompt strategies, and prompt techniques have been emerging. However, distinguishing between strategies and techniques is not straightforward, as they sometimes share the same names or are grouped into a generic category. On the other hand, considering that the use of prompts in the context of GenAI is relatively new, it is not yet clear which strategies or techniques yield the best results in the quality of responses provided by GenAI. Educational communities are beginning to explore how these technologies can be integrated. Nonetheless, the lack of time and resources makes it difficult to develop a road map that facilitates their systematic integration and measures the real impact of GenAI on the teaching-learning process. Students are also facing the challenge of interacting with GenAI, requiring them to develop new skills, such as effective prompt design. These skills are not always intuitive and require strong guidance and support to leverage the use of GenAI in a critical and ethical manner. In summary, the arrival of GenAI is redefining the concept of digital literacy, shifting from basic technology skills to the ability to communicate effectively with complex artificial intelligence systems. This shift implies a transformation in educational programs, emphasizing proper interaction with these tools while addressing risks such as technological dependence and the lack of critical thinking skills.