Scholarlink

This chapter discusses prompt engineering’s customised usage for researchers, educators, and students in educational technology. Iterative prompting, role-playing, and topic prompting can help instructors develop dynamic, AI-integrated class plans and exams that increase feedback accuracy and customised learning. Context expansion and chain-of-thought prompting help pupils understand, think critically, and solve problems. Prompt engineering simplifies writing, data analysis, and literature reviews for researchers. Researchers can write useful comments with few-shot prompting, template filling, and quick reframing. The chapter also discusses how prompt engineering might improve critical pedagogy and adaptive learning. Respectful AI use is promoted by discussing data protection, equity, and academic honesty. Using these traits, this chapter shows how prompt engineering may make learning more personalised, interesting, and productive across educational fields. It concludes with suggestions for long-term AI-driven learning research.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Education Through AI Prompts: Techniques and Applications for Teaching, Learning and Research

  • 章节

  • 发表日期：

    2026年

查看全文
  • Alta van der Merwe

摘要

This chapter discusses prompt engineering’s customised usage for researchers, educators, and students in educational technology. Iterative prompting, role-playing, and topic prompting can help instructors develop dynamic, AI-integrated class plans and exams that increase feedback accuracy and customised learning. Context expansion and chain-of-thought prompting help pupils understand, think critically, and solve problems. Prompt engineering simplifies writing, data analysis, and literature reviews for researchers. Researchers can write useful comments with few-shot prompting, template filling, and quick reframing. The chapter also discusses how prompt engineering might improve critical pedagogy and adaptive learning. Respectful AI use is promoted by discussing data protection, equity, and academic honesty. Using these traits, this chapter shows how prompt engineering may make learning more personalised, interesting, and productive across educational fields. It concludes with suggestions for long-term AI-driven learning research.