LLM/Storyline

Personalized Learning at Scale: Leveraging AI for Dynamic Feedback in eLearning

Scaling personalized practice is difficult in traditional eLearning—especially when learners need coaching, not just right/wrong feedback. To explore a more adaptive approach, I built a functional prototype that connects Storyline with an LLM to generate tailored coaching based on the learner’s written response.

The Process

THE CHALLENGE

The Need for Engaging and Personalized Feedback

Static feedback in traditional eLearning can be generic and less engaging, hindering learner motivation and comprehension. Dynamic, personalized feedback that adapts to individual learner performance is essential for creating more effective and impactful learning experiences.

THE SOLUTION

Bridging the Gap: Integrating AI-Powered Feedback into Storyline

  • ChatGPT Integration: By engineering precise prompts and structuring JSON for API communication, I successfully integrated ChatGPT into a Storyline module. This allows for real-time, AI-generated feedback based on learner input captured through Storyline variables.
  • Prompt Engineering: I focused on crafting prompts that would elicit relevant and contextually appropriate feedback from ChatGPT, addressing both correct and incorrect answers with targeted explanations and resources.
  • Storyline Development: This project leveraged Storyline’s robust functionality, including variables, triggers, and JavaScript integration, to seamlessly connect with the ChatGPT API and display the AI-generated feedback.

ADDRESSING BIAS IN CUSTOMER SCENARIOS

Creating Inclusive and Representative Learning Experiences

Future iterations of this project will use US demographic data to generate diverse and representative customer personas for contact center simulations. This approach aims to eliminate unconscious bias in scenario design and create more inclusive and relatable learning experiences.

IMPACT AND FUTURE APPLICATION

Transforming eLearning with AI-Powered Personalization

This integration has the potential to significantly enhance learner engagement, improve knowledge retention, and provide personalized learning at scale. Future applications include expanding to more complex scenarios and integrating with other learning platforms.

Preview the Prototype

00:00 User enters incorrect response
00:10 Fetches GPT response
00:16 User receives feedback
00:24 User clicks try again
00:26 User enters better answer
00:41 Fetches GPT response
00:49 User receives feedback



 

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