The primary focus will be on how we can take advantage of new technologies and techniques, such as generative AI, to complement and build on the state of the art when it comes to adaptive/personalised sequencing of learning content.
This research focuses on the recommendation and adaptive sequencing of learning content to support the delivery of authentic learning and assessment experiences. The primary focus will be on how we can take advantage of new technologies and techniques, such as generative AI, to complement and build on the state of the art when it comes to adaptive/personalised sequencing of learning content.
There are two different levels of granularity that can be explored. At a macro or program level we will explore how generative AI as well as AI more generally can enhance the recommendation of learning experiences to address the learning needs of learners. Beyond ‘point of need’ recommendation of learning, we will also explore the potential role of these technologies in creating learning plans/paths that can take a learner from where they currently are to where they need to be with respect to their knowledge and skills.
At a more micro level we explore how traditional approaches to the ‘adaptive sequencing’ of dynamically generated learning content can be enhanced through AI. This work will complement the research and activities of research project Generative AI & Digital Learning Content Creation where the focus will be on the generation of learning content and learning experiences in general. Sequencing will obviously play an important role in creating meaningful and educationally sound experiences but the specifics of how best to optimise the relationship between the pedagogical sequencing and the selection and/or generation of appropriate content/assets will be further explored.
