Prompt Engineering — AI Ed Wiki

Prompt engineering — the practice of designing and refining inputs to large language models to achieve desired outputs. In education, prompt engineering serves dual roles: as a learner skill (students must learn to prompt effectively) and as a system design lever (developers craft prompts that shape AI tutoring behavior).

Prompt engineering is central to effective Generative AI use in education. Unlike traditional programming interfaces, LLMs respond to natural language — but the quality, accuracy, and pedagogical value of those responses depend heavily on prompt design. Research in this wiki reveals that prompting is not a neutral act: it reflects how students think, plan, and allocate cognitive effort.

How prompt engineering appears in the research

Prompting as cognitive trace: Misiejuk et al. show that prompt patterns reveal cognitive offloading — high-quality work uses context-rich, polite, and instructional prompts; low-quality work shows reactive disagreement without domain grounding

Prompting as literacy: Tracing GenAI literacy and K-12 prompting literacy research frame prompting as a core AI Literacy component

Prompting as system design: CoTAL uses human-in-the-loop prompt engineering for formative assessment scoring; anchor-based prompting improves automated essay scoring

Prompt modalities: Voice vs. text input research examines whether prompting modality affects learning outcomes

Scaffolded prompting: Guided LLM scaffolding and critical engagement scaffolding teach structured prompting as a learning intervention

Connections to broader concepts

Prompt engineering connects to Scaffolding — well-designed prompts can scaffold student thinking rather than bypass it. It intersects with Metacognition and AI Literacy, as effective prompting requires understanding both the AI's capabilities and one's own learning goals. The Cognitive Offloading research directly links prompt quality to whether AI use supports or undermines learning.