## 2 Related Work
The findings revealed that higher-quality prompt engineering skills significantly predict the quality of LLM outputs, highlighting prompt engineering as an essential skill for the goal-directed use of generative AI tools. Furthermore, the study showed that certain aspects of AI literacy play a crucial role in improving prompt engineering quality and enabling targeted adaptation of LLMs within educational contexts. Building upon the findings from related works, which highlight the existence of a wide range of techniques for prompt construction, this study proposes a synthesized framework comprising two groups of techniques: circumstantial and request-based. Circumstantial techniques are defined as those that provide context and structure to the prompt, forming a foundational layer to guide the interaction. For this study, 10 circumstantial techniques were identified and defined (see Table 1). On the other hand, request-based techniques refer to those explicitly used to articulate the query or task to the LLMs. The 11 request-based techniques proposed in this framework are detailed in Table 2. This categorization serves as a practical synthesis of the existing body of knowledge, offering a structured approach that can be applied to analyze the work produced by students, as further elaborated in the methodology and results sections. \begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Ac** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Context} & Provides a framework to guide the response. The context can be more or less explicit depending on the amount of additional information to be included. Example: “I’m preparing a pitch for investors...”. \\  & & Crucial for tasks that may have multiple interpretations. This technique removes unnecessary ambiguities or clarifies potentially confusing terms. Example: “I’m preparing a pitch for investors in a startup focused on renewable energy”. \\  & & Specifies how the response should be structured (e.g., paragraphs, lists, tables). Ensures that the output aligns with the desired style or level of clarity. Example: “Respond with a bulleted list of the key benefits of machine learning.” \\  & & Incorporates specific assumptions or conditions that the model must consider to respond appropriately, reducing undesired interpretations. Example: “Assume the reader has basic knowledge of statistics.” \\  & & Guides the response by specifying the underlying objective or reason behind the prompt. This helps the model align its output with the intended purpose of the request. Example: “I’m planning a workshop for beginners on artificial intelligence and want to make the session engaging. Provide practical and interactive activities I can include.” In this case, the objective is to design an engaging and interactive workshop. \\  & & Defines the style or tone of the response, such as academic, conversational, technical, or humorous. Adjusts the language to suit the intended audience. Example: “Use simple language without technical jargon”. \\  & & Specifies whether a comprehensive response covering all possibilities is expected or a concise one focusing on key aspects. Example: “Provide an exhaustive list of the main machine learning algorithms”. Indicates a specific timeframe or perspective to guide the response (e.g., “currently” or “in the next five years”). Example: “Describe the current state of machine learning in the healthcare industry”. \\  & & Defines the role from which the response should be constructed, such as “you are a doctor” or “you are a teacher.” \\  & & Specifies the perspective for constructing the response, distinct from the role assigned to the model. This may include viewpoints like \\
10 & Perspective or Point of View Indicator & “analyze from an economic perspective” or “from the public health standpoint.” Example: “Explain the impact of AI from the perspective of an ethics specialist”. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identified circumstantial techniques.