ibl.ai equips online university instructional design teams with purpose-built AI agents that automate course development, personalize learning pathways, and reduce student attrition at scale.
Online universities face a compounding crisis: instructional designers are stretched thin across hundreds of courses while student isolation and disengagement drive attrition rates above 40%.
Traditional course design workflows are manual, slow, and inconsistent — leaving faculty under-supported and learners without the adaptive experiences they need to succeed.
Scaling quality instruction without scaling headcount demands a new approach. AI-native tools purpose-built for instructional design can close the gap between course demand and design capacity.
Instructional designers at online universities manage 3–5x more courses than their on-campus counterparts, leaving little time for quality iteration or faculty collaboration.
Average ID-to-course ratio at online universities: 1:47Without in-person touchpoints, online students disengage silently. Courses designed without adaptive feedback loops fail to detect and respond to early dropout signals.
Online university attrition averages 40–55% in first-year cohortsManual accessibility audits across large course catalogs are error-prone and time-consuming, exposing institutions to ADA and Section 508 compliance risk.
Over 70% of online course content has at least one accessibility violationAssessment design varies widely across faculty, undermining learning outcomes measurement and accreditation readiness without centralized ID oversight.
Only 38% of online assessments align to stated course learning objectivesDesigning assessments that are both scalable and resistant to AI-assisted cheating is a growing challenge with no clear manual solution for large online cohorts.
Academic dishonesty incidents in online programs rose 27% post-2020Generate course outlines, module structures, learning objectives, and scaffolded activities aligned to competency frameworks — in a fraction of the manual time.
Continuously scan course content across your LMS for ADA, WCAG 2.1, and Section 508 compliance issues, with AI-generated remediation recommendations.
Create varied, competency-aligned assessments that adapt to learner performance and are designed to uphold academic integrity in fully online environments.
Deploy always-on AI agents that guide faculty through course build processes, answer LMS questions, and surface instructional design best practices on demand.
Automatically adapt course content sequencing and pacing recommendations based on individual learner engagement, performance, and risk signals.
Transform existing course materials into engaging video content with AI narration, captioning, and interactive overlays — no production team required.
Audit existing course catalog, LMS configuration, and ID workflows. Connect ibl.ai agents to Canvas, Blackboard, or your existing LMS and SIS via secure API integrations.
Configure Agentic Content and faculty support agents for your institution's course standards and competency frameworks. Pilot with a cohort of 5–10 courses and 2–3 faculty partners.
Deploy adaptive assessment generation and learner pathway personalization across pilot courses. Train instructional design staff on agent oversight and quality review workflows.
Scale AI-assisted design workflows across the full course catalog. Establish continuous improvement loops using learner outcome data and ID team feedback.
Instructional designers manually build course outlines, source content, and coordinate with faculty over weeks of back-and-forth email.
AI agents generate draft course structures, learning objectives, and content recommendations in hours, with IDs reviewing and refining rather than building from scratch.
Periodic manual audits catch only a fraction of violations; remediation backlogs grow faster than teams can address them.
Continuous automated scanning flags violations in real time with prioritized remediation guidance, keeping the entire catalog compliant at scale.
Faculty submit LMS help tickets and wait days for ID team responses, slowing course launches and frustrating instructors.
AI faculty support agents answer LMS and instructional design questions instantly, 24/7, escalating only complex issues to human IDs.
Faculty create assessments independently with minimal ID input, resulting in inconsistent rigor and poor alignment to learning outcomes.
AI-assisted assessment generation produces competency-aligned, integrity-aware question banks that IDs review and approve before deployment.
Engagement data sits siloed in the LMS; IDs and advisors lack early warning signals until students have already disengaged.
AI agents continuously analyze engagement patterns and surface at-risk learners to IDs and advisors before dropout decisions are made.
Core tool for AI-assisted course design, learning objective generation, content adaptation, and accessibility-compliant material creation across the full online course catalog.
AI-native LMS layer that enables adaptive learning pathways, real-time engagement monitoring, and seamless integration with existing Canvas or Blackboard environments for online universities.
Enables instructional design teams to produce, caption, and enhance video lecture content at scale without dedicated production resources — critical for online-first course delivery.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.