1. Project Scope & Approach
Equans sought to modernize its learning and development approach by implementing an Adaptive Learning Management System (LMS). The objective was to transform existing training materials into a more dynamic, AI-driven learning experience that could personalize content for individual learners. AIOpener conducted a three-phase approach:
Phase 1: Discover & Define – Stakeholder sessions, LMS system evaluation, and project scoping.
Phase 2: Develop – Conversion of existing training content into an adaptive AI-powered learning module.
Phase 3: Deploy – LMS configuration, user testing, and feedback iterations before final implementation.
By leveraging LLMs (such as ChatGPT), the project aimed to create a cost-efficient, scalable e-learning system that could adjust to users' knowledge levels and learning speeds.
2. Output: Generate Image and audio learning content
To enhance the learning experience, AI-generated images and audio content were integrated into the LMS:
Text-to-Image AI created custom visuals to illustrate complex technical concepts*
Text-to-Speech AI provided audio narration, making learning more accessible and interactive.
Interactive learning paths allowed users to engage with content in a way that best suited their learning style.
The result was a scalable, efficient, and interactive training platform that significantly reduces the time and cost of traditional course development, while improving user engagement and knowledge retention.
*= fun fact: Based on the Brand guide, we created a style reference for the visuals, so all the synthetic content was aligned with the guidelines.
3. Adaptive Module Development
The adaptive e-learning module was designed to respond to user interactions and performance, dynamically adjusting content complexity and pace. This included:
Automated content adaptation – Learners receive tailored material based on their progress.
AI-driven knowledge assessment – The system evaluates users’ understanding in real time and adjusts accordingly.
Data-driven optimization – Insights from user interactions help refine training effectiveness.
This methodology ensures higher engagement and retention, as employees receive a personalized learning experience rather than a one-size-fits-all approach.
