At a time of heightened interest in online learning and AI, there is a growing consensus that adaptive educational systems (AESs) can have a transformational impact on the educational landscape. AESs dynamically adjust the level or type of instruction based on individual student competencies and have demonstrated to have the ability to provide high- quality personalised learning opportunities for diverse learners at scale. However, the adoption and impact of AESs has been limited by the need to access large repositories of learning resources commonly created by domain experts. They are expensive to develop and challenging to scale. To address this, The University of Queensland (UQ) developed a learnersourced adaptive educational platform, RiPPLE. The RiPPLE platform partners academics and students to create and evaluate high-quality learning resources and, using explainable AI algorithms, recommends personalised activities to students based on their mastery level. Demonstrated to improve learning, RiPPLE has facilitated the creation of 30,000 resources and engagement with over 1,000,000 personalised activities for 15,000 students.

Featured as an exemplar in the 2019 EDUCAUSE Horizon Report and Review, RiPPLE employs learner-centred and pedagogically supported approaches to engage students in authentic learning experiences. It harnesses the creativity and evaluation power of students as experts-in-training to develop a repository of high-quality learning resources. The RiPPLE platform uses AI algorithms, to calculate a student's level of knowledge on each course topic based on their engagement with resources and recommends personalised learning activities to each student based on their mastery level. To help students regulate their learning, RiPPLE uses transparent and explainable AI models to allow students to understand how their mastery is computed and why particular resources have been recommended to them. This project will focus on a large scale evaluation of RiPPLE's AI models to demonstrate the value of transparent and explainable AI in educational settings.

Project members

Dr Hassan Khosravi

Snr Lecturer, Learning Analytics
Institute for Teaching and Learning Innovation
Affiliate Academic
Faculty of Humanities and Social Sciences
Affiliate Senior Lecturer
School of Business

Associate Professor Gianluca Demartini

Associate Professor
School of Information Technology and Electrical Engineering

Associate Professor Jason Lodge

Associate Professor
School of Education
Affiliate Associate Professor
School of Psychology
Associate Professor
Institute for Teaching and Learning Innovation

Professor Annemaree Carroll

Affiliate Professor
Queensland Brain Institute
Affiliate Professor
National Centre for Youth Substance Use Research
Professor
School of Education