Articles for tag: AI prompt engineering, instructional design

August 31, 2026

Ariel Elyah

Slide titled The Raw Prompt and the Hidden Gaps with broad intentions and undefined details

6 Checks for Making AI-Generated Simulations Classroom-Ready

A teacher asks AI to design an interactive simulation about photosynthesis and receives an idea that sounds energetic – but cannot fit the class, the materials, the timetable, or the science. This is the central challenge of educational AI. A broad request may express admirable intentions: make an abstract concept concrete, involve every student, use minimal materials, and include a debrief. Yet those intentions alone leave too much for the model to interpret. The result can be a lesson with more roles than students, excessive preparation, vague facilitation, or an oversimplified scientific model. Better prompt design does not mean adding

August 31, 2026

Ariel Elyah

Slide comparing a raw prompt with an optimized prompt

Make AI-Generated PBL Plans More Rigorous With These 5 Core Design Principles

Project-based learning (PBL) can turn a classroom topic into meaningful work: students investigate a real problem, make decisions, collaborate, and create something for an audience beyond the teacher. But designing that kind of experience takes careful planning. Standards, time, available technology, student needs, assessment, and authentic outcomes all need to work together. AI can support that planning, but the quality of the result depends heavily on the request. A generic prompt may list the ingredients of a project without explaining the level of rigor, the instructional priorities, or what a usable final plan should contain. A stronger prompt makes those