This paper on artificial intelligence in education (AIEd) has two aims. The first: to explain to a non-specialist, interested, reader what AIEd is: its goals, how it is built, and how it works. The second: to set out the argument for what AIEd can offer teaching and learning, both now and in the future, with an eye towards improving learning and life outcomes for all. Computer systems that are artificially intelligent interact with the world using capabilities (such as speech recognition) and intelligent behaviours (such as using available information to take the most sensible actions toward a stated goal) that we would think of as essentially human. At the heart of artificial intelligence in education is the scientific goal to make knowledge, which is often left implicit, computationally precise and explicit. In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.

Intelligence Unleashed: An argument for AI in Education

Haptic interfaces enable person‐machine communication through touch, and most commonly, in response to user movements. We comment on a distinct property of haptic interfaces, that of providing for simultaneous information exchange between a user and a machine. We also comment on the fact that, like other kinds of displays, they can take advantage of both the strengths and the limitations of human perception. The paper then proceeds with a description of the components and the modus operandi of haptic interfaces, followed by a list of current and prospective applications and a discussion of a cross‐section of current device designs.

/pdf/haptic-interfaces-and-devices-afidslr94r.pdf

Haptic interfaces and devices

English has been recognized as a means of communication around the globe. However, owing to the lack of realistic English practicing contexts, EFL (English as Foreign Language) students generally have few opportunities to communicate with people in English, not to mention to get feedback from others for making reflections. In this study, a spherical video-based virtual reality (SVVR) environment was developed to situate students in authentic English-speaking contexts; moreover, the peer assessment (PA) strategy was employed for guiding students to provide comments on peers' speaking performance and to make reflections on their own performance. To evaluate the effectiveness of the proposed approach, an experiment was conducted in a high school English course. The experiment results reveal more positive effects of the peer-assessment-based SVVR approach compared with the non-peer-assessment-based SVVR approach in terms of the learners' English speaking, learning motivation, and critical thinking skills, as well as reducing their English learning anxiety. Moreover, it was found that the ratings of the students were statistically correlated with those of the teacher. This study further analyzed the types of peer comments by categorizing them into four types: Praise, Criticism, Opinion, and Irrelevant. It was found that Praise feedback was helpful for the students' English-speaking performance, while Criticism feedback might have been unfavorable in this case. Additionally, Irrelevant feedback was not significantly correlated with the students’ performance in the earlier PA stage, but had a significantly negative correlation in the later stage.

Effects of peer assessment within the context of spherical video-based virtual reality on EFL students’ English-Speaking performance and learning perceptions

In this study, we reviewed articles on technology-enhanced language learning and teaching. We aimed to summarize the content of reviewed articles in the following categories: (1) the number of articles published by journals and by year; (2) languages and skills; (3) technology used; (4) promising technologies. We reviewed 398 research articles. The highest number of articles was published in 2017 (n = 80), whereas the lowest number was published in 2014 (n = 53). The Computer Assisted Language Learning journal published the highest number of articles (n = 100), whereas the lowest number of published articles appeared in IEEE Transactions on Learning Technologies (n = 3). The most common target language was English (n = 267). Writing, speaking, and vocabulary gained the most attention in published articles. Twenty-three different technologies were identified and they were used 406 times. Based on our results, we made several implications and suggestions for future studies. This review study can serve as a guide for teaching and research communities who plan on designing language learning and teaching activities supported by technologies.

Review of Studies on Technology-Enhanced Language Learning and Teaching

The use of mobile phones while driving—one of the most common driver distractions—has been a significant research interest during the most recent decade. While there has been a considerable amount research and excellent reviews on how mobile phone distractions influence various aspects of driving performance, the mechanisms by which the interactions with mobile phone affect driver performance is relatively unexamined. As such, the aim of this study is to examine the mechanisms involved with mobile phone distractions such as conversing, texting, and reading and the driving task, and subsequent outcomes. A novel human-machine framework is proposed to isolate the components and various interactions associated with mobile phone distracted driving. The proposed framework specifies the impacts of mobile phone distraction as an inter-related system of outcomes such as speed selection, lane deviations and crashes; human-car controls such as steering control and brake pedal use and human-environment interactions such as visual scanning and navigation. Eleven literature-review/meta-analyses papers and 62 recent research articles from 2005 to 2015 are critically reviewed and synthesised following a systematic classification scheme derived from the human-machine system framework. The analysis shows that while many studies have attempted to measure system outcomes or driving performance, research on how drivers interactively manage in-vehicle secondary tasks and adapt their driving behaviour while distracted is scant. A systematic approach may bolster efforts to examine comprehensively the performance of distracted drivers and their impact over the transportation system by considering all system components and interactions of drivers with mobile phones and vehicles. The proposed human-machine framework not only contributes to the literature on mobile phone distraction and safety, but also assists in identifying the research needs and promising strategies for mitigating mobile phone-related safety issues. Technology based countermeasures that can provide real-time feedback or alerts to drivers based on eye/head movements in conjunction with vehicle dynamics should be an important research direction.

/pdf/understanding-the-impacts-of-mobile-phone-distraction-on-50a5uv8wuo.pdf

Understanding the impacts of mobile phone distraction on driving performance: A systematic review

This paper presents a new observability index to quantify the selection of best pose set in robot calibration. This noise amplification index is considerably more sensitive to calibration error than previously published observability indices. Support for the proposed index as provided analytically and geometrically, and also through comparison against previous indices by a simulation for a 3-link planar robot and by an experiment for a 3-DOF redundant parallel-drive robot.

/pdf/the-noise-amplification-index-for-optimal-pose-selection-in-9fe2e3k61w.pdf

The noise amplification index for optimal pose selection in robot calibration

A method is presented for autonomous kinematic calibration of a 3-DOF redundant parallel robot. Multiple closed loops are used in a least squares optimization method. Ill-conditioning, column scaling of the gradient matrix, and observability indices for the best pose set of robot calibration configurations are discussed. Experimental results are presented and compared with the results using an external calibration device. >

/pdf/calibration-of-a-parallel-robot-using-multiple-kinematic-y6qayhzkol.pdf

Calibration of a parallel robot using multiple kinematic closed loops

A haptic display system is presented for manipulating virtual mechanisms derived from a mechanical CAD design. Links are designed and assembled into mechanisms using Utah's Alpha-1 CAD system, and are then manipulated with a Sarcos Dextrous Arm Master. Based on the mechanism's kinematics and the virtual grasp, the motion of the master is divided into motion of the mechanism and constraint violation. The operator experiences the dynamic forces from the mechanism plus constraint forces.

/pdf/haptic-manipulation-of-virtual-mechanisms-from-mechanical-2asnzgfv1l.pdf

Haptic manipulation of virtual mechanisms from mechanical CAD designs

In this paper, we present an intelligent architecture, called intelligent virtual environment for language learning, with embedded pedagogical agents for improving listening and speaking skills of non-native English language learners. The proposed architecture integrates virtual environments into the Intelligent Computer-Assisted Language Learning. This architecture supports visual, auditory, and haptic channels of interaction. It allows pedagogical ideas about language skills to be implemented and validated with a minimum design time. Moreover, we design a computational model to evaluate learner's proficiency level, and an automatic adaptation mechanism which adjusts to the learner's learning curve. We have implemented two scenarios based on the proposed architecture to teach learners how to communicate in public places such as airports and TV stores. Inputs to this system include learner's speech and hand motion, and outputs include graphical scenes, force feedback, and speech by a few embodied agents. ...

Design and implementation of an intelligent virtual environment for improving speaking and listening skills

We present a physics-based training simulator for bone machining. Based on experimental studies, the energy required to remove a unit volume of bone is a constant for every particular bone material. We use this physical principle to obtain the forces required to remove bone material with a milling tool rotating at high speed. The rotating blades of the tool are modeled as a set of small cutting elements. The force of interaction between a cutting element and bone is calculated from the energy required to remove a bone chip with an estimated thickness and known material stiffness. The total force acting on the cutter at a particular instant is obtained by integrating the differential forces over all cutting elements engaged. A voxel representation is used to represent the virtual bone and removed chips for calculating forces of machining. We use voxels that carry bone material properties to represent the volumetric haptic body and to apply underlying physical changes during machining. Experimental results of machining samples of a real bone confirm the force model. A real-time haptic implementation of the method in a dental training simulator is described.

Physics-Based Haptic Simulation of Bone Machining

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