Faster Maintenance with Augmented Reality

In the not-too-distant future, it might be possible to slip on a pair of augmented-reality (AR) goggles instead of fumbling with a manual while trying to repair a car engine. Instructions overlaid on the real world would show how to complete a task by identifying, for example, exactly where the ignition coil was, and how to wire it up correctly.

A new AR system developed at Columbia University starts to do just this, and testing performed by Marine mechanics suggests that it can help users find and begin a maintenance task in almost half the usual time.

AR has long shown potential for both entertainment and practical applications, and the first commercial applications are starting to appear in smart phones, thanks to cheaper, more compact computer chips, cameras, and other sensors. So far, however, these apps have been mainly limited to providing directions. But researchers are also working on many practical applications, including ways to help with specific repair and maintenance tasks.



 The Columbia researchers worked with mechanics from the U.S. Marine Corps to measure the benefits of using an AR headset when performing repairs to a light armored vehicle. Currently, Marine mechanics have to refer to a technical manual on a laptop while performing maintenance or repairs inside the vehicle, which has many electric, hydraulic, and mechanical components in a tight space

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Artificial Intelligence in Education

systems

1 0

References

1. Alpert, S. R., Singley, M. K., and Fairweather, P. G.: Deploying Intelligent Tutors on the

Web: An Architecture and an Example. International Journal of Artificial Intelligence in

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http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_10/alpert.html

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Educational Applications on the World-Wide Web. In: Brusilovsky, P., Nakabayashi, K. and

Ritter, S. (eds.) Proc. of Workshop "Intelligent Educational Systems on the World Wide

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Japan, ISIR (1997) 78-85, available online at

http://www.contrib.andrew.cmu.edu/~plb/AIED97_workshop/Andre/Andre.html

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at the 4th World Congress on Expert Systems, Mexico City, Mexico, ITESM (1998) 9-16

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Boulay, B. and Mizoguchi, R. (eds.) Artificial Intelligence in Education: Knowledge and

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Lawrence Erlbaum Associates, Hillsdale (1988) 1-19

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10 (1999) 183-197, available online at10, 4 (1985) 159-1758, 5 (1976) 567-5966, 2-3 (1996) 87-129: Web-based education for all: A tool for30, 1-7 (1998)

Augmented Reality Goggles

I held a black-and-white square of cardboard in my hand and watched as a dragon the size of a puppy

 appeared on top of it and roared at me. I watched a tiny Earth orbit around a real soda can, saw virtual balls fall through a digital gap in a table, and viewed a life-sized virtual human sitting in an empty chair.
What made these impressive special effects possible was a pair of augmented reality (AR) glasses—specifically, the Wrap 920AR glasses from Vuzix. Whereas virtual reality shows you only a digital landscape, augmented reality (AR) mixes virtual information, like text or images, into your view of the real world in real-time.
In the last few years, AR has started appearing on smart phones. In that context, software superimposes information on top of your view of the world as seen through the device's screen. But AR eyewear, which provides a more immersive experience, has been confined to academic research and niche applications like medical and military training. That's been largely because older AR hardware has been so bulky and has cost tens of thousands of dollars.
The Wrap 920AR from Vuzix, based in Rochester, New York, costs $1,995—about half the price of other AR goggles with similar image resolution. The company hopes that the glasses will appeal to gamers, animators, architects, and software developers, and it has developed software for building AR environments, which is included with the glasses.

s, which have never been used for

teaching real distance classes. The rest of them, a handful of systems mainly from ELM-ART

and AHA families, were used in a few relatively small classes. At the same time, none of the

dozens of commercial and "university-grown" Web courseware systems that are used in

hundreds of real distance courses applies adaptive and intelligent technologies. Does it mean that

research and practice in Web-based education area will never merge together?

The position of the author is the following. Web-based education itself is relatively young.

Until now different companies producing Web-based education systems were able to compete

on the market with their simple non-adaptive systems. However, a number of research level

systems have already clearly demonstrated the benefits of adaptive and intelligent technologies.

As long as the competition on the market of Web-based educational system will increase, “being

adaptive” or “being intelligent” will become an important factor for winning the customers.

Traditional Web-based education companies will start to include adaptive and intelligent

functionality. Research teams with solid experience in using adaptive and intelligent technologies

will found startup companies to bring their technology to the market. The first technologies to be

used in commercial systems will probably be sequencing technologies (page sequencing and

question sequencing) since they match very well to the current structure of Web-based education

systems. Next will come the turn of adaptive navigation support and model matching. Problemsolving

support technologies will stay on research level for longer, though we could expect the

market debut of small Web-based tutors that are aimed to support teaching a fragment of some

subject. I hope that the next five years will show us a number of examples of commercial-level

adaptive and intelligent systems as well as many new and exciting developments on the research

level.

System Ref. Adaptive

sequencing

Adaptive

navigation

support

Problem

solving

support

Intelligent

solution

analysis

Adaptive

presentation

ELM-ART [12] Page Annotation Partial Server Some

ELM-ART-II [53] Course,

tests

Annotation Partial Server Some

PATInterBook

[11] Page,

remedial

Annotation Partial Server Some

VC Prolog

Tutor

[39] Task,

remedial

Server

Table 1

hypermedia and ITS functionality

Adaptive and intelligent technologies in Web-based educational systems that combine adaptive

System Ref. Adaptive sequencing Adaptive

navigation

support

Adaptive

presentation

InterBook [9] Page Annotation Some

AST [46] Course Annotation Some

ADI [43] Course

(knowledge+interests)

Annotation Some

Adaptive and intelligent technologies for web-based education

roup.

7

Adaptive collaboration support is a very new adaptive technology which was developed

within last 5 years along with development of networked educational systems. The goal of

adaptive collaboration support is to use system's knowledge about different students to form a

matching group for different kinds of collaboration. The pioneering non-WBE (i.e., non-Web, or

non-educational) examples of adaptive collaboration support are known for already a few years.

These examples include forming a group for collaborative problem solving at a proper moment

of time [25; 26] or finding the most competent peer to answer a question about a topic (i.e.

finding a person with a model showing good knowledge of this topic) [31]. Less than two years

ago Brusilovsky [8] predicted that adaptive collaboration support will become a popular

technology. This prediction came true almost immediately. Now we can list already several real

examples of adaptive collaboration support in WBE context. The group from University of

Saskatchevan has extended their original workplace-oriented peer-help technology developed for

PHelpS system [21; 31] to the WBE context in their Intelligent Helpdesk system [22]. Another

similar system was developed and evaluated in the University of Central Florida [32]. In addition

to that, the group in the University of Duisburg known for their pioneering work on adaptive

collaboration support [25] have recently suggested a complete framework for implementation of

intelligent support techniques for distributed internet-based education. This framework can

naturally support their original adaptive collaboration support techniques and provides a

framework for exploring other model matching techniques.

Intelligent class monitoring is also based on the ability to compare records of different

students. However, instead of searching for a match, it search for a mismatch. The goal is to

identify the students who have learning records essentially different from those of their peers.

These students may be different from others in many ways. They cold be progressing too fast, or

too slow, or simply have accessed much less material than others. In any case, these students

need teacher's attention more than others - to challenge those who can, to provide more

explanations for those who can't, and to push those who procrastinate. In a regular classroom the

teacher can simply track students attendance and activity to find students who need special

attention. In a Web-based classroom, the teacher in the best case has only logging data - tables

with numbers which are very hard to grasp. At the same time, the need to identify a small subset

of students who need help more than others is more important. In WBE context, communication

between teacher and students is usually more time consuming and a distance teacher simply can't

individually address more than a small subset of the class. The system HyperClassroom [36]

provides an interesting example of using fuzzy mechanisms to identify deadlocked students in a

WBE classroom. At the time of writing, it is the only example of the intelligent class monitoring

technology known to the author.

Adaptive and intelligent technologies for web-based education

comparison with two-level sequencing in most ITS): the best page is simply selected from the

6

set of acceptable pages using some heuristics. We refer to this way of sequencing as page

sequencing. InterBook and ELM-ART provide good examples of this technology. However, the

difference between these two technologies starts to disappear in the Web context. Web-based

ITS systems are naturally moving to hypermedia platform representing at least some part of the

learning material as a hyperspace. As long as some type of educational material (presentations,

problems, and questions) is represented as a set of nodes in hyperspace, sequencing of it

becomes indistinguishable from direct guidance. To stress this similarity we have represented

adaptive sequencing and adaptive navigation support with direct guidance in the same column of

the tables.

The most popular form of ANS on the Web is annotation. It was used first in ELM-ART

[12] and since that applied in all descendants of ELM-ART such as InterBook, AST, ADI, ACE,

and ART-Web as well as in some other systems such as WEST-KBNS and KBS HyperBook.

ELM-ART and InterBook also use adaptive navigation support by sorting. Another popular

technology is hiding and disabling (a variant of hiding that keeps link visible but does not let the

user to proceed to the page behind the link if this page is not ready to be learned). The options

are either to make the link completely non-functional (nothing happens when the user clicks on

it) as implemented, for example, the Remedial Multimedia System [4] or to show the user a list

of pages to be read before the goal page as done in Albatros [29]. Tables 1 and 2 list all major

systems that use adaptive navigation support and indicates the type of adaptation.

The goal of

page to the user's goals, knowledge and other information stored in the user model. In a system

with adaptive presentation, the pages are not static, but adaptively generated or assembled from

pieces for each user. For example, with several adaptive presentation techniques, expert users

receive more detailed and deep information, while novices receive more additional explanation.

Adaptive presentation is very important in WWW context where the same "page" has to suit to

very different students. Only two Web-based AES implement full-fledged adaptive presentation:

PT [28] and AHA [16]. Both these systems apply a flexible but low-level conditional text

technique. Some other systems use adaptive presentation is special contexts. Medtec [19] is able

to generate adaptive summary of book chapters. MetaLinks can generate a special preface to a

content page depending on where the student came from to this page. ELM-ART, AST,

InterBook and other descendants of ELM-ART use adaptive presentation to provide adaptive

insertable warnings about the educational status of a page. For example, if a page is not ready to

be learned, ELM-ART and AST insert a textual warning at the end of it and InterBook inserts a

warning image in a form of a red bar. A very interesting example of adaptive presentation is

suggested in WebPersona project [3] where an individualized presentation of information in an

educational hypertext is performed by a life-like agent.

the adaptive presentation technology is to adapt the content of a hypermedia

2.3 Web-inspired technologies in Web-based education

The last group of technologies is probably the most exciting one since these technologies

has almost no roots in pre-internet educational systems. Currently this group include only one

technology. We call this technology

because the essence of this technology is the ability to analyze and match student models of

many students at the same time. Traditional adaptive and intelligent educational systems has no

opportunity to explore this technology since they usually work with one student (and one student

model) at a time. On the contrary, in the WBE context this opportunity happens naturally

because student records are usually stored centrally on a server (at least for administrative

reasons). It provides an excellent framework for developing various adaptive and intelligent

technologies that can make some use of matching student models of different students. So far,

we have identified two examples of student model matching, which we call

collaboration support

other and probably could be considered as different technologies within the student model

matching group.

student model matching (or simply model matching)adaptiveand intelligent class monitoring. These examples quite differ from each