Faculty keep telling us two things that sound contradictory but aren’t: “Make it simple,” and “Let me stay in control.” Human-in-the-loop course authoring is how we honor both.

With ibl.ai, AI assembles the first draft—units, outcomes, suggested activities, and formative checks—so instructors start from a thoughtful scaffold instead of a blank page. Instructors then review and refine every element in their existing authoring workflow. When they’re ready, they publish into the institution’s LMS via LTI, keeping current gradebooks, rosters, and workflows intact. You get speed and academic judgment, in one loop.

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How It Works

• Seed the build. Upload slides, readings, or a syllabus outline.

• AI drafts the structure. The system proposes modules, objectives, activities, and checks for understanding.

• Faculty stay in the driver’s seat. Edit outcomes, swap readings, adjust level/tone—nothing goes live until you approve it.

• Publish to your LMS via LTI. Push approved units into any LMS (Canvas, Blackboard, Brightspace, etc.) with standard integration.

• Iterate continuously. Update and republish—no re-authoring from scratch.

Why Human-In-The-Loop Matters In Higher Ed

• Pedagogical fit. AI drafts quickly, but alignment with course goals and discipline norms requires human judgment.

• Assessment integrity. Faculty validate question banks, instructions, and rubrics to preserve fairness and standards.

• Transparency & trust. Instructors know exactly what’s published because they approved every change.

• Speed without shortcuts. Drafting time shrinks from weeks to days while quality goes up thanks to structured review.

Built For Institutions

• Standard integrations. LTI-based publishing keeps your LMS as the system of record.

• Custom safety & scope controls. Domain-scoped guardrails restrict assistants to course-relevant tasks and content.

• Flexible content ingestion. Drag-and-drop by instructors, or IT-approved API connections for automated ingestion—your call, with security first.

• Faculty enablement baked in. Group workshops, ongoing office hours, and one-on-one sessions help instructors design responsibly and ship great courses fast.

• Control and portability. Institutions retain ownership of their data and can take deployments in-house if needed.

A Typical First Month

• Week 1: Faculty onboarding; pick a pilot course; upload source materials.

• Week 2: AI proposes the unit map; instructor revises outcomes and activities.

• Week 3: Publish approved modules to the LMS via LTI; run a short formative cycle.

• Week 4: Collect feedback, fine-tune instructions and checks, republish updates.

What This Unlocks

• Faster course refreshes before term start—without “summer miracle” heroics.

• Consistent program templates that faculty can personalize while keeping shared outcomes.

• Evidence-friendly artifacts (revisions, approvals, change logs) that support QA and accreditation needs.

• Room for innovation—once boilerplate work is handled, instructors spend more time on authentic projects and mentoring.

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In Conclusion

If you’re looking to move beyond “a chatbot on top of content,” this is the layer that lets AI do the heavy lifting while faculty keep authorship. Draft fast, review well, publish with confidence.

Curious what this looks like on your campus? Let’s start with a small pilot and a single course. We’ll handle onboarding, support your faculty one-to-one, and help you publish to your LMS via LTI. Contact us at ibl.ai/contact.