## 2 Related Work
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Request-Based Technique (RBT)** & **Definition** \\ \hline \multirow{4}{*}{11} & \multirow{4}{*}{Direct Request} & Involes asking for a specific action in a simple sentence without adding context. Example: “Define what machine learning is”. \\  & & Combines multiple instructions in a single prompt, such as \\  & & \"Describe machine learning and provide examples of its use in medicine.\" Enables richer responses. \\ \multirow{4}{*}{12} & \multirow{4}{*}{Mixed Request or Multiple Tasks} & \"Explain the desired level of detail, such as “Briefly describe” or \\  & & or Focus & “Explain each step of the learning process in detail.” Example: \\ \cline{1-1}  & & \"Provide information related to i, ii, iii”. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Identified request-based techniques. Building upon the findings from related works, we developed a 21-item checklist through a three-step process grounded in a functional classification of prompt engineering techniques. First, an exploratory review of academic and technical literature was conducted to identify common prompting techniques relevant to educational contexts. From this review, 21 techniques were consolidated based on frequency, conceptual clarity, and applicability in higher education. In the second step, a functional analysis grouped these techniques into two overarching categories based on their communicative role: (i) circumstantial techniques (n = 10), which provide structural or contextual cues for shaping the model's response; and (ii) request-based techniques (n = 11), which explicitly articulate the action or task expected from the LLM. Each technique was clearly defined and illustrated with an example (see Tables 1 and 2), forming a practical and pedagogically oriented framework for analyzing prompt construction. This classification informed the design of the 21-item binary checklist used in the study, enabling a systematic analysis of student-generated prompts. The application of this framework is further elaborated in the methodology and results sections. While this study centers on the functional structuring of prompts, the impact of grammatical and syntactic features on LLM outputs has been explored in detail in a recent publication by [PERSON] et al. [27], which specifically examines these linguistic aspects. To better situate this study within existing educational paradigms, it is important to consider how current theories of AI literacy and digital competence frame user interaction with generative AI. [PERSON] [28] defines AI literacy as a multidimensional construct that includes not only technical knowledge but also cognitive and ethical understanding, allowing users to make informed decisions about the use of AI. This concept aligns with recent calls to  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Request-Based Technique (RBT)** & **Definition** \\ \hline
14 & Open-Ended Question Request & Uses open-ended questions like “What does it imply...?” to elicit broad and exploratory responses. \\
15 & Sequential Request & Indicates that the response should address several aspects in a specific sequence, such as “First define, then provide examples, and finally analyze the limitations”. \\
16 & Request for Analysis from Multiple Disciplines or Perspectives & Asks the model to approach the response from multiple disciplines or perspectives, valuable for multidisciplinary topics. Example: “Analyze the impact of artificial intelligence from medical, ethical, and technological perspectives”. \\
17 & Request for Fact-Based or Data-Specific Response & Seeks a response grounded in concrete facts or known statistics, even hypothetically, for a more objective and evidence-based focus. \\  & &  
develop AI literacy programs that empower students to question, interpret, and strategically engage with AI systems in educational settings.