Prompt Engineering as a Cognitive Interface: Reframing Human-AI Collaboration and Digital Literacy in the Age of Generative AI

The rapid evolution of generative artificial intelligence (AI), typified by systems such as GPT-4, Claude, and DALL-E, has introduced new paradigms for human-machine interaction. At the center of this transformation lies the practice of prompt engineering, the act of crafting natural language instructions to elicit specific, high-quality responses from AI models. Traditionally regarded as a technical workaround or hack, prompt engineering is increasingly recognized as a structured, strategic, and cognitively rich process. This paper reframes prompt engineering as a cognitive interface, a conceptual bridge through which humans externalize mental models, intentions, and iterative hypotheses into prompts that shape AI behavior. Drawing on a mixed-methods study that analyzed 300 real-world prompts and 15 interviews with expert prompt designers, we introduce the Prompt Cognition Loop (PCL). This novel framework describes prompting as a four-phase cycle: 1) mental modeling, 2) semantic projection, 3) dialogic feedback, and 4) intent refinement. This model aligns with key theories in cognitive science and human-computer interaction, including schema theory, mental models, and reflective practice. We further explore the pedagogical potential of prompt engineering in digital education, suggesting curricular and design strategies for developing prompt literacy.