Project Name
Course Content Automation for 1,000+ Instructors with AWS Bedrock
Our client is a large-scale EdTech platform headquartered in North America, delivering video-based learning across higher education, corporate training, and professional development markets. Serving thousands of institutions and millions of learners, the platform had established itself as a trusted home for instructor-led video content, a library where every lesson existed first as a recorded video and, critically, as a machine-readable transcript.
Growth ambitions demanded rapid content depth expansion, but the platform’s ability to scale was bottlenecked by manual authoring workflows that required subject matter experts to re-derive and re-write what the transcripts already contained.
The organisation sought an AI-native solution that could treat those transcripts as the primary knowledge source, extracting, structuring, and publishing their content at scale without compromising pedagogical quality or institutional voice.
Thousands of video modules, instructors spending 3 to 5 hours per module on manual content authoring, and no mechanism to generate a first draft without a subject matter expert starting from a blank page.
- Instructor Authoring Overload: Instructors spent 3–5 hours per video module manually creating summaries, FAQs, and quizzes, slowing course launches and diverting SMEs from higher-value work.
- Inconsistent Content Quality: Without a standardized authoring process, the quality and depth of learning materials varied across instructors, resulting in an inconsistent learner experience.
- Limited Scalability for Content Expansion: Every new course required proportional instructor effort, making content growth slow, resource-intensive, and difficult to scale.
- Metadata Gaps Reducing Discoverability: Modules often lacked descriptive metadata, limiting search accuracy and recommendation quality while reducing course visibility.
- AI Adoption Without Governance: The organization needed AI-assisted content generation with mandatory SME review to ensure accuracy, consistency, and brand compliance.
- Transcript Quality Dependency: AI-generated content depended on accurate transcripts, making transcription quality a critical prerequisite for reliable outputs.
- Complex Integration Requirements: The AI solution had to integrate seamlessly with existing transcript pipelines, LMS workflows, and content management systems without disrupting operations.
Ksolves, an AI-first technology company, developed an end-to-end LLM Content Pipeline on AWS Bedrock that uses each video's transcript as the single source of truth. The solution automatically generates summaries, FAQs, quizzes, and metadata while routing every output through a mandatory human review process before publication.
- Video Transcript Ingestion Pipeline: Timestamped transcripts are automatically captured, cleaned, and segmented into context-aware chunks, ensuring all AI-generated content is grounded in the source material.
- AWS Bedrock LLM Inference: Context-driven prompts generate first-draft summaries, FAQs, and quiz questions through AWS Bedrock, delivering scalable AI content generation without infrastructure overhead.
- AI Teaching Assistant Interface: A dedicated review console enables instructors to quickly accept, edit, or reject AI-generated content within their existing workflow.
- Human-in-the-Loop Review Gate: Every AI-generated asset requires instructor approval before publication, ensuring content accuracy, quality, and brand consistency.
- Automated Metadata Generation: Tags, topic labels, and learning objectives are generated automatically during content creation, improving discoverability without adding manual effort.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | AWS Bedrock |
| Processing | LLM Content Pipeline |
| Platform | AI Teaching Assistant |
| Architecture | Human-in-the-Loop Review |
| Integration | Video Transcript Ingestion |
The solution transformed content authoring with faster workflows, standardized outputs, and scalable AI-driven automation.
- 80%+ Reduction in Authoring Time: AI-generated first drafts reduce content creation time from 3–5 hours to under 30 minutes per module, allowing instructors to focus on teaching and curriculum design.
- Faster Course Launches: Automated content generation removes weeks of manual authoring, enabling new modules to become content-ready on the day they are uploaded.
- Improved Content Coverage: The AI pipeline ensures consistent generation of summaries, FAQs, and quizzes, significantly increasing the completeness of learning content across modules.
- Complete Metadata with Zero Manual Effort: Automatic generation of tags, topics, and learning objectives improves searchability and recommendations without additional instructor work.
- Scalable Content Expansion: AI-assisted authoring enables rapid growth of course content without requiring proportional increases in instructor capacity.
By implementing an AI-powered content generation pipeline on AWS Bedrock, Ksolves transformed a manual, time-intensive authoring process into a scalable and efficient workflow. The solution accelerated content creation, improved consistency across learning materials, and maintained quality through mandatory human review. As a result, the platform is now equipped to expand its content library faster while delivering a more consistent and engaging learning experience.
Is Manual Content Authoring Slowing Your Learning Platform’s Growth?
