## 2 Related Work
(c) Prompt planning: At this level, prompting evolves into a more complex and recursive process. Students construct a series of interconnected prompts designed to structure a learning process. This approach moves beyond generic communication with AI models, focusing on creating tailored and purposeful interactions that support specific learning goals. Various studies are being conducted to understand and classify prompt techniques, as well as to evaluate their effectiveness. [PERSON] et al. [3], using the PRISMA methodology for a systematic review, proposed an ontological taxonomy of text-based prompting techniques. This taxonomy includes 58 techniques distributed across six major categories: (a) zero-shot, (b) few-shot, (c) thought-generation, (d) ensembling, (e) self-criticism, and (f) decomposition. [PERSON] [13] proposes five prompting techniques: (a) instructive prompt, (b) system prompt, (c) question-answer prompt, (d) contextual prompt, and (e) mixed prompt. An instructive prompt allows the user to employ a guide to direct their writing toward a specific task. A system prompt provides a starting point or context to help users build their content effectively. A question-answer prompt facilitates the structuring of inputs around specific research questions. A contextual prompt offers additional background or details within the prompt to help users focus on particular aspects of their response. Finally, a mixed prompt combines multiple elements, enabling users to construct their inputs in a comprehensive and understandable manner. [PERSON], [PERSON], and [PERSON] [24] conducted a study to investigate how undergraduate students used ChatGPT for problem-solving, the prompting strategies they developed, the relationship between these strategies and the model's response accuracy, the presence of individual prompting tendencies, and the influence of gender in this context. The study involved 56 senior students enrolled in the course. The findings revealed that students predominantly employed three types of prompting strategies: single copy-and-paste prompting (SCP), single reformulated prompting (SRP), and multiple-question prompting (MQP). ChatGPT's response accuracy using SRP and MQP was significantly higher than with SCP, with effect sizes of \(-0.94\) and \(-0.69\), respectively. Recent advancements in prompt engineering have led to the development of diverse techniques aimed at optimizing interactions with conversational LLMs. [PERSON] and [PERSON] [25] proposed a comprehensive and interdisciplinary framework that categorizes these prompting techniques into seven conceptual groups: logical and sequential processing, contextual understanding and memory, specificity and targeting, meta-cognition and self-reflection, directional and feedback, multimodal and cross-disciplinary, and creative and generative. Each category encompasses a set of strategies tailored to specific interaction goals, such as reasoning through complex problems, eliciting reflective responses, or generating creative content. This taxonomy offers a roadmap for practitioners and educators to select the most appropriate prompting strategies based on their pedagogical objectives or domain-specific applications. According to [PERSON] et al. [24], although there is a growing body of research on GenAI prompts, most studies have focused on their use by researchers rather than by students. Few authors have explored how students interact with GenAI tools such as ChatGPT, highlighting a significant gap in understanding student-specific behaviors and strategies in this context. In this regard, [PERSON] et al. [22] examined how AI literacy among higher education students influences their interactions with LLMs. For this study, the author defined AI literacy as \"a set of competencies that enables individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool online, at home, and in the workplace\" [26] (p. 2). The study involved a sample of 45 students aged between 19 and 35 years.