AI-Assisted Instructional Design Architecture for STEM Education
A recent submission on arXiv (2608.07364) introduces a six-phase instructional design framework that utilizes AI, grounded in the Curriculum as Code concept. This architecture combines Generative AI with LaTeX and Python to streamline the development of reproducible, visually coherent, and technically accurate resources for STEM education. The paper addresses the significant burden on educators to produce tailored materials for active learning, as well as the shortcomings of conventional presentation tools for technical subjects. Existing AI tools often generate inaccuracies and do not effectively formalize the instructional design process, which limits their effectiveness in rigorous academic settings. The framework aims to decrease preparation time while maintaining mathematical precision, compliance with institutional visual standards, and the retention of instructors' implicit teaching knowledge through defined guidelines. The design consists of a six-phase pipeline, and the paper is now publicly available at the specified URL.
Key facts
- Paper arXiv:2608.07364 presents a six-phase AI-assisted instructional design architecture.
- Based on the Curriculum as Code paradigm.
- Integrates Generative AI with LaTeX and Python.
- Aims to automate creation of reproducible, visually consistent, and technically precise materials for STEM education.
- Addresses heavy workload on faculty for creating customized instructional materials.
- Current AI applications often hallucinate and fail to formalize the instructional authoring process.
- Framework aims to reduce preparation time while ensuring mathematical accuracy and adherence to institutional visual identity.
- Preserves instructor's tacit pedagogical knowledge through explicit rules.
Entities
Institutions
- arXiv