Discovery Early Career Researcher Award - Grant ID: DE200100898
Funder
Australian Research Council
Funding Amount
$421,979.00
Summary
Information Embodiment Framework for Education using Immersive Technologies. The project aims to develop a framework to apply mixed reality technologies in education, by fusing the information with human physical, physiological, cognitive and emotional perceptions. The current approach translates existing contents into 3D which does not scale and causes cognitive overload. The project conceptually advances the design and development of mixed reality applications. The expected outcome is a mixed ....Information Embodiment Framework for Education using Immersive Technologies. The project aims to develop a framework to apply mixed reality technologies in education, by fusing the information with human physical, physiological, cognitive and emotional perceptions. The current approach translates existing contents into 3D which does not scale and causes cognitive overload. The project conceptually advances the design and development of mixed reality applications. The expected outcome is a mixed reality integration framework for effective communication, applicable to manufacturing, health, tourism and arts. The benefits are enhanced learning with engaging resources, with positive impact on student learning outcomes and motivation, in both formal and informal education settings such as schools, galleries, and museums.Read moreRead less
Human-Machine Teaming:Designing synergistic learning of humans and machines. This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-de ....Human-Machine Teaming:Designing synergistic learning of humans and machines. This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-design and iterative prototyping in the domains of radiology training and environmental learning, we will devise and evaluate exemplar systems that support humans to interactively frame problems, explore and learn, while utilising and improving machine models, leading to a guiding framework for designing human-machine teaming.Read moreRead less