SBC: Secure AI-Enhanced Learning Integration
Topic-locked AI coaching embedded inside LMS training with security and data safeguards
AI-enhanced practice embedded inside existing courseware
SBC trains child welfare professionals in environments where reflection, supervision, and decision-making quality directly affect families. Traditional eLearning can explain what good practice looks like, but it rarely creates space for guided practice and feedback.
We implemented a secure AI integration layer that embeds structured practice moments directly within existing LMS training. Instead of a general-purpose chatbot, the AI functions as a topic-locked virtual supervisor, providing coaching-style feedback aligned to SBC methodology while operating within strict safety boundaries.
-
Goal: Add guided practice without replacing the LMS or disrupting SCORM delivery
-
Safeguards: Prevent entry of real client data, constrain responses to approved content
-
Architecture: Server-side AI routing through AWS with secrets secured
-
Result: A maintainable framework that strengthens reflection and coaching without increasing compliance risk
Secure by design.
Instructionally aligned.
This was not a generic chatbot added to training. It was a controlled AI integration built to reinforce SBC methodology, protect sensitive environments, and create structured practice moments that feel seamless to learners.
The challenge: high-stakes practice and feedback gaps
Child welfare professionals need opportunities to practice and reflect safely, but traditional eLearning cannot adapt to individual responses and supervision is not always available in the moment.
The constraint: privacy, proprietary content, and controlled behavior
The integration needed strict safeguards. Learners could not enter real client details, proprietary methodology had to remain protected, and the AI had to stay within approved objectives and language.
The approach: topic-locked virtual supervisor inside the LMS
We embedded a custom interface in the course experience, routed requests server-side through AWS, and used structured prompts and criteria so feedback stayed aligned to SBC coaching models rather than open-ended conversation.
The outcome: responsible AI that can scale safely
The result is a maintainable AI-enhanced framework that strengthens reflection, preserves instructional intent, and supports ongoing refinement without rebuilding core infrastructure or increasing compliance risk.
Interested in safe, controlled AI inside training?
If you want AI to reinforce learning without exposing sensitive data or disrupting your LMS, let’s map the right approach and safeguards.