Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labor inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this article, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction, and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.

Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labour inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this paper, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.

Human-Computer Interaction, including technology-aided instruction, is beginning to focus on virtual reality (VR) technology due to its ability to support immersive learning, teaching through simulation, and gamification of learning. These systems can deliver high-level multisensory learning experiences that are important in the teaching of many subjects, especially those involving abstract concepts or requiring spatial skills, such as organic chemistry. Haptic experiences with VR, however, remain a challenge. In addition, development have focused on general entertainment/gaming; VR systems in chemistry implement simulations of the chemistry laboratory and other advanced systems whereas those that support safe, game-like, immersive and multisensory learning of organic chemistry with haptics at pre-university education levels are scarce. We developed the VR Multisensory Classroom (VRMC) as an immersive learning environment within a VR head-mounted display, where learners employ hand movements to build hydrocarbon molecules and experience haptic feedback through gloves with built-in sensors and hand-tracking with the Leap Motion system. We report here the evaluation of the first prototype by learners from diverse backgrounds who reported on the ability of the VRMC to support high engagement, motivation, interest and organic chemistry learning as well as diverse learning styles. The VRMC is a novel VR classroom that supports immersive learning in molecular organic chemistry with haptics for multisensory learning.

With recent focus on deep neural network architectures for development of algorithms for computer-aided diagnosis (CAD), we provide a review of studies within the last 3 years (2015-2017) reported in selected top journals and conferences. 29 studies that met our inclusion criteria were reviewed to identify trends in this field and to inform future development. Studies have focused mostly on cancer-related diseases within internal medicine while diseases within gender-/age-focused fields like gynaecology/pediatrics have not received much focus. All reviewed studies employed image datasets, mostly sourced from publicly available databases (55.2%) and few based on data from human subjects (31%) and non-medical datasets (13.8%), while CNN architecture was employed in most (70%) of the studies. Confirmation of the effect of data manipulation on quality of output and adoption of multi-class rather than binary classification also require more focus. Future studies should leverage collaborations with medical experts to aid future with actual clinical testing with reporting based on some generally applicable index to enable comparison. Our next steps on plans for CAD development for osteoarthritis (OA), with plans to consider multi-class classification and comparison across deep learning approaches and unsupervised architectures were also highlighted.

Access to medical care, especially in poor economies, is a key global issue. Telemedicine and other technology-aided approaches have been applied in addressing the challenge of access with many solutions that focus on closing the physician-patient gap. Medical diagnosis has however not fully represented in areas addressed through technology as fully automated and reliable diagnosis procedures are still far from being realized. Though a number of web-based applications are available, they employ text-based inputs rather than conversation in natural language. Ontology-based chatbot technology is being deployed to address this challenge through conversational agents. We explored the utility of ontology-based chatbot technology in medical diagnosis through a systematic review of the most recent related literature. English articles published in the last 5 years (2011-2016) returned 233 hits which yielded 11 relevant articles after screening of duplicates, title and abstracts. Findings showed that the creation of expert systems had been the focus of many of these studies with system training based mostly on expert knowledge and the physician-system-patient framework. The applications are designed to merely serve assistive purposes. The systems are deployed on the web or mobile phones. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the ‘physical examination’ part of diagnosis, connection to data sources on patients’ vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Focus should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options。