AI-Assisted Instructional Design
Professional position, Quality Matters alignment, and frequently asked questions
Professional Position Statement
AI-Assisted Instructional Design as Legitimate Professional Practice
Angela Westmoreland, M.Ed. | FTCC Instructional Support & Quality Assurance Analyst | Spring 2026
Quality Matters — the organization whose 7th Edition Rubric governs this program — used Claude AI, the same tool used in the HQCDP, to develop their official guidance on AI ethics in education. They disclosed this in every section of their toolkit and called it appropriate professional practice. This is not a footnote. It is the governing body’s own standard.
The Key Distinction
AI Automation
A faculty member inputs a course name and accepts whatever AI produces. No professional judgment applied. No verification. No alignment check. The AI makes all decisions.
AI Augmentation ← This Program
A credentialed instructional designer makes all pedagogical decisions, uses AI to execute document production efficiently, and verifies every output against professional standards. Human expertise drives every outcome.
Quality Matters’ Explicit Position
QM’s AI Integration Toolkit (2025) establishes three core principles that directly support the HQCDP approach.
Human-Centered Design
“Use AI to enhance, not replace, human connection and critical thinking.”
Transparency and Trust
Open, documented, evidence-based process for all AI-assisted design decisions.
Continuous Improvement
“Demonstrate flexibility to evolve with technological advancement.”
“From March 5–July 18, 2025 Claude 3.7 Sonnet AI was used iteratively as one of many thought partners in the development of this toolkit. All output provided by this tool has been reviewed and significantly edited, amended, and expanded by QM experts and advisors throughout the development process.”
— Quality Matters AI Integration Toolkit (2025)
“AI can significantly improve the efficiency with which institutions develop content that is closely aligned with the curriculum and course objectives, but requires careful oversight to maintain quality.”
— QM AI Integration Toolkit, Section III (2025)
The Professional Work AI Cannot Do
The following design decisions require professional expertise that no AI tool can replicate.
- Primary Source Curation — Each of 16 primary sources chosen for specific pedagogical reasoning. Cortés pairs with Module 1’s Columbian Exchange analysis. Abigail Adams anchors Module 3’s revolutionary contradictions. MLK’s Letter from Birmingham Jail anchors Module 6 for its analytical complexity.
- Outcomes Architecture — CLO → MLO → Activity → Assessment alignment chain constructed by applying QM 7th Edition, Bloom’s Taxonomy, and NCCCS curriculum requirements simultaneously.
- RSI Design — 48+ instructor-initiated touchpoints, exceeding federal requirements by 10× under 34 CFR § 600.2.
- Workload Compliance — 135+ documented student hours using CITL methodology.
- Scaffold Design — Learn → Practice → Apply → Assess cycle, DOK progression, alternating Submit/DB pattern — research-informed instructional design decisions.
The Broader Professional Context
Professional consensus supports AI-augmented instructional design as current best practice.
EDUCAUSE (2024)
“Augmented Course Design: Using AI to Boost Efficiency and Expand Capacity”
QM Professional Development
QM now offers workshops on AI-assisted course design: “Leveraging AI for Course Design (LGAI)” and “Transforming Curriculum with AI (TCGAI).”
QM Framing
“Strategic integration” of AI described as “essential.”
An instructional designer who declined to use available professional tools when producing course documentation at scale would not be demonstrating superior rigor — they would be demonstrating unfamiliarity with current professional practice.
Results Confirm the Process
Measurable outcomes demonstrate that expert-driven, AI-augmented design produces verifiably high-quality courses.
These outcomes cannot be produced by submitting a course name to an AI tool. They require expert instructional design judgment. The AI produced the documents. The designer produced the course.
Frequently Asked Questions
Common questions about AI-assisted instructional design in the HQCDP.
No. AI did not build these courses. Every instructional decision — which outcomes to target, which primary sources anchor each module and why, how activities scaffold toward higher-order thinking, how the semester-long portfolio threads across eight modules — was made by a credentialed instructional designer with 20+ years of experience and four Quality Matters certifications. AI was used as a production tool to format, structure, and generate documents efficiently. The intellectual architecture, pedagogical rationale, and every alignment decision are original professional work.
- The course designer chose every primary source and articulated why each serves a specific pedagogical function
- The course designer built the CLO → MLO → Activity alignment chain; AI did not determine outcomes or standards compliance
- The course designer constructed the inquiry-driven 8-module framework with alternating Submit/DB milestones
- AI produced clean, consistently formatted documents from those decisions
- The results confirm this: 22/22 QM Essential Standards cannot be achieved by automation; they require expert judgment
AI tools were used for document production and formatting — converting structured instructional decisions into professionally formatted Word documents, teaching plans, course maps, and presentation slides. This is analogous to using a spreadsheet to build a grade book. Specifically: formatting course maps and teaching plans, generating consistent structured documents across 8 modules for two full courses simultaneously, producing presentation slides, building interactive HTML tools.
Yes — and this is explicitly supported by Quality Matters. QM’s AI Integration Toolkit (2025) states that “AI can significantly improve the efficiency with which institutions develop content that is closely aligned with the curriculum and course objectives.” QM’s own toolkit was developed using Claude AI.
The course designer brings 20+ years of instructional design experience, four QM certifications, M.Ed. and Graduate Certificate in Computer Education from ECU, and elected leadership as VP of NC Community College Association of Distance Learning.
“Prompt and accept” produces generic, unverified output. This program applied extensive professional judgment before (defined outcomes, mapped standards, selected sources), during (gave expert-informed instructions), and after (reviewed against QM standards, verified accuracy, edited for alignment).
QM’s 2025 AI Integration Toolkit explicitly supports AI-assisted course development. QM Core Principle: “Use AI to enhance, not replace, human connection and critical thinking.” QM’s AI Integration Rubric covers “AI used to develop course materials and activities” focusing on verification and oversight, not prohibition. QM used Claude AI to build their own toolkit.
QM’s guidance on disclosure focuses on student-facing transparency — not the production tools used by instructional designers. The HQCDP courses use no AI-generated content delivered to students — all instructional content is OER (American YAWP) and instructor-created activities. The AI produced formatting and document structure, not course content.
Yes — replicability was an explicit design goal. The HQCDP model, teaching plan template, course map template, and workload calculator are all discipline-agnostic. The GitHub dashboard demonstrates scalability to HIS-114, BUS-253, and CIT courses already in development.