Embracing AI to Automatically Create Student-Centred Curricula

In this presentation we will address the question of how AI can improve learning processes and enhance online engagement and collective learning. The speakers will share a case study of how AI is applied to aggregated and anonymous profile data sets, not only to discover how people learn but also what they want to learn.

Based on the theoretical framework of connectivism (Siemens, 2004), the speakers explored how connectedness through technology enables systems to combine thoughts, information, and interest in a useful manner, providing young learners in South Africa with unique opportunities to make individual choices about their learning journey.

They deployed a network of over 2500 dedicated educational devices across multiple schools in South Africa, with learners utilising these devices to structure their learning process and to engage with other students, enhancing collective learning.

They used AI to create a unique digital identity for each user. After, they profiled and personalised the access given to each student based on their interests, learning styles, and capabilities. By analysing the data, it is then possible to make a case on each individual's learning style and verify the right metrics to improve impact.

The AI system not only uses profile data to discover how people learn but also what they want to learn. The approach centres around the learner rather than a predetermined outcome. AI is used to discover the learner's interests and to auto-create interest-based peer- and content groups that facilitate collective learning, enable knowledge sharing, and enhance engagement online.

To ensure data security and privacy, they created a closed system from hardware (IoT) right through to the cloud, with data use control managed at a single learner level. Therefore, the AI is collaborative and transparent right down to the end-user.

Overview

• Examine the fundamentals, challenges, and successes of a real-world application of a collaborative learning experience.
• Receive insights into tailored learning processes using AI.
• Discover alternate uses of AI and how it can be used collaboratively with each end-user without the loss of agency or predetermined bias as it assists in creating human-to-human connections across the network.
• Collaborate and co-create to scale and improve systems with global peers.

This presentation argues that fast changes in the requirements of the labour market have made the process of creating and maintaining learning pathways and individual curricula inefficient and time consuming for the learning content authors. The way forward is building a personalised learning environment based on AI and automated methods.

In recent decades, we have faced a significant gap between the supply of learning content offered by educational systems, and what individuals actually need to learn to be able to carry out their daily (including job-related and social-related) activities. The COVID-19 pandemic intensified this challenge as due to the dramatic, and often existential situation of businesses in a number of industries forced people to re-skill themselves online in order to remain employable in post-COVID times. 

For instance, skills that are important for offering online services (e.g. software development and delivery), or soft skills related to online collaboration and communication, are extremely demanded in the labor market and expected to play key roles in the near future.

Subsequently, the regular updating of personal skill-sets and fast changes in the requirements on the labor market side have made the process of creating and maintaining learning pathways and individual curricula inefficient and time consuming for the learning content authors.

As a consequence, educational systems should be developed that consider individual learning objectives, personal learning context, and individual learning preferences in order to provide personal and relevant learning recommendations. To build such a personalised context-aware system, large amount of educational resources with various properties (e.g. language, expertise level, and format type) is required. 

However, the applicability of educational materials has been limited due to the lack of intelligent methods to facilitate quality control and property extraction processes. Without these automated methods, essential services like high-quality learning content recommendation or educational material search services cannot perform well enough. In this line of research, therefore, we aim at:

1. Proposing a method that facilitates the utilisation of information on skills for learning processes, based on timely labor market information.
2. Decomposing those skills into meaningful learning objectives and their components (skills, learning topics) and offer individualised learning pathways for learners.
3. Building a personalised educational content recommendation system that helps learners to achieve their goals through recommended learning paths.
4. Utilising artificial intelligence to help authors and experts to create a high quality knowledge base, which serves as a foundation to define a vast number of learning pathways.

Overview

• How to curate dynamic curricula in a changing world.
• How can in-context personalised learning increase the learning performance.
• How to combine Artificial Intelligence and crowdsourcing techniques to offer a personalised learning environment.