Moving Towards More Authentic Learning Experiences – Stream 1: Using Generative AI to Create Authentic Learning Experiences

Stream 1 Overview

This research stream examines Authentic Learning Experiences and explores how AI can be used to deliver them in new and innovative ways.

The aim of Authentic Learning Experiences is to deliver the learning content using problems, challenges and situations the learner might encounter in their day-to-day work.

Therefore, this research stream covers topics such as:

• Learner Profiling: how can AI be used to build a profile of the learner that can then be used to build learning experiences that feel both authentic and relevant to the learner. This will overlap with and work closely with research stream 2 where the focus will be on assessment.
• Authentic Scenarios: creating learning scenarios that replicate on-the-job tasks presented in contexts with which the learner can relate. While the focus in research stream 1 will be on the overarching experience, this will be enabled through the content generation research from research stream 3.
• Fostering Skills: building learning experiences that address the learner’s skills gap(s) identified using data collected from the learner through interactions with the learning content
• Dynamic Interactions: generating content and learning pathways dynamically in order to maximise the authenticity and efficacy of the learning experience
• Reflection: examining how to incorporate reflective activities into the learning experience that encourage the learner to consider the personal relevance of the learning experience
• Validating Experiences: looking at how to validate that any experiences generated are appropriately aligned to the learning outcomes
• Complementary Technologies: exploring how AI might be leveraged to enhance Authentic Learning Experiences delivered using AR and VR

The Research Stream will aim to highlight the characteristics of Authentic Learning that contribute to better learning outcomes thus informing the assessments to be considered in Research Stream 2.