Beyond the AI Lesson Plan: Building an AI-Assisted Curriculum System for Real Classrooms
Generative AI can produce a lesson plan in seconds. The harder question is whether that lesson actually fits the curriculum, prepares students for assessment, uses realistic resources, aligns with standards, and gives teachers something they can reliably use in a real classroom.
This workshop explores what happens when AI is treated not simply as a content generator, but as one component of a structured curriculum and teaching-materials system. The session draws on an ongoing implementation in an international school in Myanmar, where an AI-assisted platform is being developed to support MYP Sciences across Grades 6–10.
The system connects:
- Curriculum structure and textbook/source material
- Learning objectives and MYP assessment criteria
- Lesson sequencing and practical laboratory requirements
- Standards alignment and teacher-ready materials
Rather than asking an AI model to generate an entire unit in one pass, the workflow separates curriculum planning into reviewable stages. Structured data is used for stable information, AI is applied where pedagogical or content judgement is required, and validation takes place before materials are treated as usable output.
The workshop will examine both what works and what does not, exploring questions such as:
- What should teachers delegate to AI, and what should remain deterministic?
- How can standards alignment be added without rewriting an existing curriculum?
- How can AI-generated practical activities respect real laboratory constraints?
- How do we prevent polished AI output from being mistaken for good curriculum?
- How can schools build AI systems without losing teacher judgement or creating an unmaintainable collection of prompts?
Participants will then apply the framework to one of their own lessons or units, identifying:
- Which parts of curriculum planning and preparation could appropriately be supported by AI
- Which information should remain structured and controlled
- Where human review and professional judgement are essential
The goal is not to introduce another AI tool. It is to help educators think about AI as part of a reliable instructional workflow and leave with a practical framework for designing one.
Facilitated By
Ariel Dahan
MYP Sciences Teacher, Yangon American International School
Ariel Dahan is an international-school science educator with more than seven years of international teaching experience. He currently teaches MYP Integrated Sciences and Biology at Yangon American International School in Myanmar. His work focuses on curriculum design, inquiry-based science education, assessment, and the practical use of educational technology in international-school settings.
Ariel is currently developing and testing an AI-assisted curriculum and teaching-materials platform designed to connect curriculum planning, standards alignment, assessment, lesson sequencing, practical materials, and teacher-ready instructional resources. The project focuses on MYP Sciences across Grades 6–10 and is being developed around the realities of an international school rather than as a generic AI content-generation system.
His approach is to use AI where it can provide meaningful pedagogical or content judgment, while using structured data, deterministic processes, and validation for tasks that require consistency and reliability. Through this work, Ariel is exploring how teachers can use AI to reduce repetitive preparation without giving up curriculum intent, professional judgment, or accountability for what students actually learn.
