Beyond Content

Analysing the 'How' of Learning with AI

Welcome to the second article in my series exploring the intersection of corporate learning and artificial intelligence. In my previous piece, I introduced you to xAPI and NLP, two foundational technologies driving the AI revolution in education. Today, we're diving deeper into how these technologies shift our focus from what people learn to how they learn.

The Shift from Content to Process

Traditionally, corporate learning has been heavily content-focused. We've measured success by completion rates, test scores, and the volume of material covered. But what if we're missing the bigger picture?

We can now analyse the learning process by leveraging AI, particularly the combination of xAPI and NLP. This shift allows us to understand what knowledge employees are acquiring and how they're acquiring it, applying it, and even feeling about it.

xAPI: Capturing Fine-Grained Learning Behaviors

xAPI's power lies in its ability to track various learning experiences. But it's not just about monitoring the completion of e-learning modules anymore. With xAPI, we can capture data on the following:

- How often learners revisit specific content

- The sequence in which they approach learning materials

- Their interactions with peers in collaborative learning environments

- Real-world application of skills learned

For example, xAPI can track when a sales representative accesses a product knowledge base right before a client call, indicating real-time application of learning.

While xAPI tells us what learners are doing, NLP helps us understand the quality and nature of their engagement. By analysing text-based interactions, we can gain insights into:

- Comprehension levels through analysis of discussion posts

- Emotional engagement with content through sentiment analysis

- Areas of confusion or curiosity based on the questions asked

- Development of critical thinking skills through analysis of written assignments

Imagine automatically identifying when learners are expressing frustration or excitement about a topic, allowing for timely intervention or encouragement.

Case Studies: Improved Learning Outcomes Through Behavior Analysis

Let me share a couple of real-world examples of how this approach is transforming learning outcomes:

1. A global manufacturing company used xAPI to track how employees accessed and used standard operating procedures on the factory floor. By analysing this data, they identified workers struggling with specific procedures during night shifts. This led to targeted microlearning interventions, resulting in a 30% reduction in errors.

2. A financial services firm applied NLP to analyse customer service chat logs alongside learning data. They discovered that representatives who engaged more deeply in scenario-based e-learning (as measured by xAPI) used more positive language in customer interactions (as identified by NLP). This insight led to redesigning their training program, emphasising practical scenarios.

The Promise and the Challenge

This shift towards analysing the 'how' of learning offers immense potential:

- Personalised learning paths based on individual learning behaviours

- Real-time feedback and support

- More accurate measurement of learning effectiveness and ROI

However, it also presents challenges. We must be mindful of data privacy concerns and ensure our analysis doesn't become intrusive. It's crucial to use these insights to support and empower learners, not to penalise them.

Thanks for reading! ??

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