Cent. Eur. J. Eng. • 3(4) • 2013 • 606-619DOI: 10.2478/s13531-013-0122-9

Central European Journal of Engineering

Envisioning engineering education and practice in thecoming intelligence convergence era - a complexadaptive systems approach

Vision Article

Ahmed K. Noor1∗

1 Center for Advanced Engineering EnvironmentsOld Dominion University, Hampton, Virginia, USA.

Received 18 June 2013; accepted 01 September 2013

Abstract: Some of the recent attempts for improving and transforming engineering education are reviewed. The attemptsaim at providing the entry level engineers with the skills needed to address the challenges of future large-scalecomplex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. Themajor characteristics of the coming intelligence convergence era (the post-information age) are identified. Theseinclude the prevalence of smart devices and environments, the widespread applications of anticipatory comput-ing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines.Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recentgame changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that cansignificantly impact engineering education in the coming era are identified. Among these are open educationalresources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruc-tion / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significanttransformative changes in, and mass customization of, learning are envisioned to emerge from the synergisticcombination of the game changers and other technologies. The realization of the aforementioned vision requiresthe development of a new multidisciplinary framework of emergent engineering for relating innovation, complex-ity and cybernetics, within the future learning environments. The framework can be used to treat engineeringeducation as a complex adaptive system, with dynamically interacting and communicating components (instruc-tors, individual, small, and large groups of learners). The emergent behavior resulting from the interactionscan produce progressively better, and continuously improving, learning environment. As a first step towards therealization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collabo-ration between the various stakeholders of engineering education, and to accelerate the development of a skilledengineering workforce. The major components of the ecosystems include integrated knowledge discovery andexploitation facilities, blended learning and research spaces, novel ultra- intelligent software agents, multimodaland autonomous interfaces, and networked cognitive and tele-presence robots.

Keywords: Anticipatory computing • Augmented reality • Complex adaptive system, Convergence • Flipped classroom • Gamechangers • Immersive 3D learning • Intelligence era • Learnscapes • Tele-presence robots© Versita sp. z o.o.

∗E-mail: aknoor@odu.edu606

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1. IntroductionThe engineering profession is facing a number of majorchallenges, including the increasing complexity and in-terdisciplinary nature of engineering systems; the accel-erated pace of change of lifecycle tools and processes; thegeographically distributed diverse workforce; and work-force skill shortage. High-tech industries are having in-creasing difficulty in recruiting positions of strategic im-portance to maintain their competitive position in theglobal market. Traditional engineering disciplines, andformal engineering programs, proved to be inadequate formeeting the challenges. Some studies and reports haveadvocated transformative changes in engineering educa-tion (see, for example, [1–8]). Others have proposed com-plementing formal learning with practical applications andlifelong learning to address current and emerging needs.Also, a number of pilot programs have been developedby academic institutions, industry, and research organi-zations to address some of the needs and challenges ofthe engineering profession, and to promote pedagogicalreforms. These include:

• New interdisciplinary degrees and minor programsas supplements to the formal traditional engineer-ing programs. A number of emerging interdisci-plinary fields were identified in [9].• Capstone, co-op, extracurricular, and service learn-ing programs- combining formal instruction withinquiry-based activities, and related services in thecommunity.• Bachelor of innovation and entrepreneurship pro-grams, a family of degree programs at a numberof academic institutions, including the Universityof Colorado at Colorado Springs [10], the Universi-ties of Adelaide and Canberra in Australia. ThesePrograms have a common core in innovation andentrepreneurship for providing long-term multidis-ciplinary team experiences.

A report published by the US National Academy of Engi-neering in 2012 [11] describes attempts by 29 engineeringprograms at colleges and universities across the nationto infuse real world experiences into engineering educa-tion. It highlights best practices for schools seeking tocreate new programs. The present paper outlines someof the frontier sectors and future challenges of engineer-ing; provides an updated version of the major character-istics of the coming intelligence convergence era (post-information age); and describes the major game changersin learnscapes (learning paradigms, technologies, plat-forms, spaces and environments) that can significantly

Figure 1. Some of the components of a smart power grid

impact engineering education in the coming era. A vi-sion for engineering education and practice is presented.The need is described for developing intelligent adaptivecyber-physical ecosystems to facilitate collaboration be-tween the various stakeholders of engineering educationand to accelerate the development of a skilled engineeringworkforce for the new era.2. Frontiers of engineering and fu-ture challengesA broad array of frontier sectors and future challenges forengineers were the focus of several meetings and reportsby the US National Academy of Engineering, and othernational and international organizations. The frontier sec-tors include smart grids; urban mobility; smart cities, cog-nitive manufacturing, 3D/4D printing, programmable mat-ter, big data and predictive / prescriptive analytics. Theseare briefly described subsequently.2.1. Smart GridsSmart grids are digital networks that use informationand communication technologies, in automated ways, toimprove the efficiency, reliability, and sustainability ofthe production and distribution of electricity. Ideally,smart grids are also intended to work with multiple powersources, including wind and solar sources, and perhapseventually small individual sources and ones that provideautomotive power. New energy sources can be integratedinto a smart grid at different times as well (see, for exam-ple [12, 13] and Figure 1).Because of the complexity of the grid, and the need to en-sure stability over a wide range of operating conditions,some recent work has been devoted to future intelligentpower grids, wherein neurobiology is combined with engi-neering to develop brain-like approaches for adaptive op-

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Envisioning engineering education and practice in the coming intelligence convergence era - a complex adaptive systems approach

timal control of very large grids. A multidisciplinary teamfrom Clemson University, Georgia Tech, and Missouri Sand T is working on the project, which is called Brain2Grid(http://brain2grid.com/), and is sponsored by the Of-fice of the Emerging Frontiers in Research and Innovation(EFRI) of the US National Science Foundation. The inte-grated sensing, communications and control of the powergrid can transform the modernized end-to-end electricpower system into a "smart grid" - an integrated, self-healing and electronically controlled secure and resilientpower system. By knowing how failures occur, assess-ing vulnerabilities and working out self-healing strategies,a holistic risk management, monitoring and control sys-tem can be designed and deployed. Application of smartgrid concepts to water distribution and management isdescribed in [14]. Interdependent networks of fuel/watersupply, end-to-end electric power systems, telecommuni-cations and financial networked systems have a normal,undisturbed state and an alert model that senses precur-sors to an emergency state. When they are in an aggra-vated state they attempt to restore to a normal one. Thekey is to build systems that are simple and smart, thatfocus on reliability, robustness, efficiency and security.2.2. Urban mobility and intelligent connectedtransportation

Significant work has been devoted to the developmentof a fully connected multi-modal intelligent transporta-tion systems, including networked autonomous vehicles,that can lead to a smart urban mobility. Such systemsuse telematics (integrated use of telecommunication andinformatics), along with other technologies, to integrateworkplaces, residential buildings, travel service providers,airlines onto a single platform. The platform can pro-vide a seamless transportation experience that increasessustainability and productivity for communities (see, forexample, [15]). The vehicles in the transportation systemshave new kinds of attractive functions and features thatconnect several aspects of human activity. The realiza-tion of the smart urban mobility goals require innovativesolutions to tackle the two opposing objectives:1. To improve the safety, comfort and time associatedwith transportation, getting individuals and goodswhere they need to be, and when they need to bethere; and2. To reverse the alarming, unsustainable energy andenvironmental trends associated with transporta-tion, and devise transportation systems that materi-ally enhance sustainability and societal well-being.

Smart Mobility, along with smart energy, smart technolo-gies, smart healthcare, smart buildings, smart infrastruc-ture, smart governance and smart citizens are identifiedin [16] as the new mega trends.3. Smart CitiesSeveral ongoing programs in the US, Brazil, Denmark,South Korea, and other countries are focused on trans-formation of cities to smarter cities to overcome their so-cioeconomic problems, associated with urban growth andthe significant stress on their infrastructures [17]. TheIBM smart cities concept is based on using information,communication and other technologies to reduce cost, im-prove efficiencies, and deliver quality of life to citizens.The IBM concept is based on treating the city as an in-terconnected system of systems, integrating a set of in-terdependent public and private systems that the city canoptimize to achieve new levels of efficiency and effective-ness. The component systems include Infrastructural sys-tems (such as energy, water and transportation), human-centered systems (such as education, healthcare and so-cial programs), and administrative planning and manage-ment systems. The central component of the IBM conceptis an intelligent operations center to coordinate and sharedata in a single view, creating the big picture for the de-cision makers and responders who support the smart city.3.1. Cognitive manufacturing andSmart/Cognitive Factories

Manufacturing systems have evolved over the years inresponse to many external drivers, including the intro-duction of new manufacturing technologies and materi-als, the evolution of new products, the increased empha-sis on quality as well as the need for responsiveness,agility and adaptability. Cognitive manufacturing is anew paradigm in which machining and measurements aremerged in order to form more flexible and controlled en-vironment [18]. When unforeseen changes or significantalterations happen, machining process planning systemsreceive on-line measurement results, make decisions, andadjust machining operations accordingly in real time. Fur-ther development of the Cognitive manufacturing conceptcan lead to the concept of Smart / Cognitive Factories thatenable a more flexible, adaptable, and reliable produc-tion (Figure 2). According to this concept, the machinesand processes in a factory are equipped with cognitivecapabilities that involve reasoning about goals, percep-tion, actions, collaborative task execution, etc., to allowthem to assess and increase their scope of operation au-608

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Figure 2. Smart / Cognitive Factory Concept

tonomously. Causality-based formal representation andautomated reasoning methods, from artificial intelligence,can be used for such a cognitive factory with multipleteams of robots and humans, where each team tries tocomplete a complex assembly task, and where teams com-municate with each other for efficiently sharing commonresources.3.2. 3D / 4D printers3D printing refers to the process of making a three-dimensional solid object of virtually any shape from adigital model. Additive manufacturing - the industrialversion of 3D printing is considered distinct from tra-ditional manufacturing techniques, such as casting andmachining, which mostly rely on the removal of materialby methods such as cutting or drilling (subtractive pro-cesses). 3D printing is achieved by using an additive pro-cess, where successive layers of material are laid downin different shapes [19]. The technology of 3D printing isused for both prototyping and distributed manufacturingin many fields, including aerospace, industrial design, ar-chitecture, automotive, medical industries, education, ge-ographic information systems, and civil engineering. It istransforming manufacturing from mass production to masscustomization. The exploration of the 3D printing pro-cess from design to production can open up new possibil-ities for learning activities (see, for example, descriptionof the projects at the Center for Bits and Atoms at MIThttp://cba.mit.edu/). Printed 3D models can be usedto illustrate complex concepts, or illuminate novel ideasand designs. A recent development, beyond 3D print-ing, is the 4D Printing process, developed by researchersat the MIT self - assembly Lab and Stratasys Inc. (seehttp://www.sjet.us/MIT_4D%20PRINTING.html ). Theprocess entails the added capability of self-assembly (em-bedded transformation from one shape to another), directlyoff the print-bed. This new technique offers a streamlined

path from idea to reality with full functionality built di-rectly into the materials. It can provide robotics-like be-havior without the reliance on complex electro-mechanicaldevices. A new software system, called Cyborg, is be-ing developed by Autodesk to serve as a design platformspanning applications from the nano-scale to the human-scale, to take advantage of the 4D printing technology,from idea conception to reality. The software allows forsimulated self-assembly and programmable materials, aswell as optimization for design constraints and joint fold-ing. The objective is to tightly couple the Cyborg newcross-disciplinary, and multi-scale, design tool with thereal-world material transformation of 4D printing. Thetightly coupled software and hardware tools will eliminatethe traditional paradigms of simulating then building, orbuilding then adjusting the simulation. Several excitingpotential applications of 4D printing can be cited. Forexample, a self-driving car can be printed, rather than as-sembled. The car can be packed for shipment by a robot,and then send to the consumer in a flat, streamlined form,ideal for packing and shipping. With the addition of water(the medium currently used for self-assembly), the car isable to reconfigure itself in a matter of moments. Anotherapplication is that of developing simple space systems thatcan be shipped compactly to orbit and then expand andbecome fully functional on demand while in orbit. Thesystems can be fully reconfigurable to various other highlyfunctional systems.3.3. Programmable matter

Programmable matter concept is a digital material com-posed of small intelligent modules having computation,sensing, actuation, and display as continuous properties,which are active over its whole extent. It is the resultof convergence of nanotechnology with autonomous com-puters. Programmable matter systems are composed ofmillimeter-scale autonomous microsystem particles, with-out internal moving parts, bound by electromagnetic forcesor an adhesive binder. They are able to form a variety ofmacroscale objects with specific material properties in re-sponse to commands or stimuli. They have the poten-tial of many exciting applications, like shape-changingrobots and tools, rapid prototyping, paintable displays,and sculpture-based haptic interfaces. Researchers atCarnegie Mellon University are developing self-assemblymodular robots by using very large number of "claytronicatoms", or catoms - basic computers (less than a mil-limeter in diameter) housed in tiny spheres that canconnect to each other, and rearrange themselves (seehttp://www.cs.cmu.edu/~claytronics/). The real-ization of this concept could enable development of fully

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Envisioning engineering education and practice in the coming intelligence convergence era - a complex adaptive systems approach

Figure 3. The 5 Vs of Big Data

programmable objects, able to construct, and/or decon-struct themselves. For example, car’s surface that changecolor, tires that adapt to different terrains, or self-heal.3.4. Big data and predictive/prescriptive ana-lyticsBig data refers to a collection of large, diverse, complex,heterogeneous, longitudinal, and/or distributed datasetsthat are difficult to process using traditional databasemanagement tools. Big datasets generated by large-scalesimulations and visualizations in engineering and scienceled to the emergence of the fourth research paradigmof "Data Intensive Scientific Discovery" (after Experimen-tal, Theoretical, and Computational Sciences). Today bigdatasets are also generated from instruments, sensors, in-ternet transactions, social networks, and other availabledigital sources. The five characteristics of big data thatcan help in augmenting traditional value processes arevolume, variety, velocity, veracity (verification or viabil-ity) and value. These characteristics are referred to asthe 5Vs of Big Data (Figure 3). Big data represents anew era in data exploration and utilization [20]. Someof the recent activities result from three major shifts ofinterlinked mindset, namely:

• Ability to analyze vast amounts of data about atopic, rather than settle for smaller sets• Willingness to collect and use larger pools of data,and to embrace real-world messy data• Attempt to find correlations in the data, rather thanelusive causality

Big data analytics refer to the process of examining largeamounts of data of a variety of types to uncover hidden

Figure 4. Four levels (phases) of the Data Analytics Hierarchy

patterns, unknown correlations and extract other usefulinformation. Gartner identified four levels (phases) in thedata analytics hierarchy, namely, descriptive, diagnos-tic, predictive, and prescriptive (Figure 4). The first twophases answer the questions what happened and why didit happen. The next one, predictive analytics [21] answersthe questions what might happen and why did it happen. Ituses systematic scientific means, including machine learn-ing, data mining, and visualization, to extract useful infor-mation from massive data (turning big data into smart dataand big ideas), to develop, and continually improve predic-tions. The final phase is prescriptive analytics, which goesbeyond predicting future outcomes by suggesting actionsto benefit from the predictions, and showing the implica-tions of each decision options. It uses combination of AIand operations research techniques to identify the bestcourse of action to take advantage of opportunities.A number of new technologies have been developed foranalyzing and mining massive amounts of structured andunstructured data for new insights (see, for example, theopen source software framework Apache Hadoop http://

hadoop.apache.org/index.html) The information pro-vided by data analytics can enable breakthrough discover-ies in engineering, science, and education. It can allow fordata-intensive decision-making, provide competitive ad-vantages over rival organizations, and result in businessbenefits, such as more effective marketing and increasedrevenue. Big data analytics can also be used for evidence-gathering concerning new learning technologies (learningdata mining, and learning analytics) to help in design-ing adaptive learning environments that respond to thelearner’s progress in real time, fostering more engage-ment in the learning process. Learning analytics can helpin setting policy, practice and funding decisions.610

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4. Characteristics of the coming in-telligent convergence era

A brief overview of the coming intelligence convergenceera (the post-information age) as envisioned in 2011 wasgiven in [9], along with examples of convergence of tech-nologies, devices and disciplines. Herein, an update ofthat vision is briefly described. The new era is char-acterized by the prevalence of adaptive, functional andsmart environments. The environments will be filled withsensor webs (sensors smaller than the eye could see,joined together into networks larger than the mind couldcomprehend), mobile and tablet computing, cognitive andtele-presence robots, and computers that respond to brainwaves (i.e., computers that can be controlled by thought).Incorporation of novel smart multifunctional wearable de-vices in the environment, enable their use in place of ac-cessories the users already wear, such as glasses andwrist watches, and allowing the users to comfortably ac-complish variety of tasks. The environments will also in-clude cognitive and intelligent C3 systems (follow-ups onthe IBM Watson project), integrating cognitive sensing,computing, communication and networking. The new in-terdisciplinary field of Deep Learning, which is the resultof convergence of neuroscience, bioinformatics, and arti-ficial intelligence, has one of its major goals as buildingmachines that can process data in much the same way asthe human brain does [22]. These intelligent machines willprovide real-time commonsense situational awareness, in-cluding scene perception and understanding, perceptualdata analytics and high-level control of autonomous sys-tems. Several applications of deep learning that can helphumans more effectively recognize patterns and make in-ferences, are being explored by Google, Microsoft, IBM,and others. The result will be a fundamental shift in theway humans interact with the environment, and a symbi-otic relationship between humans and machines. Devicesand cognitive machines will be able to learn from, andwith, humans in a natural collaborative way [23]. Humansin the environment can have cybernetic assistants (cogni-tive assistant systems, consisting of ultra-intelligent elec-tronic agents) that watch the users’ different modalities ofinteraction with the environment, use predictive / prescrip-tive analytics to provide them with what they need beforethey ask for it. The purpose of the environment will beto protect and serve, and not only to make humans moreproductive, but also to support them in the enjoyment oftheir lives.

Figure 5. Learnscapes

5. Game Changers in engineeringeducation, and learning, in generalMajor advances have taken place in a broad front of learn-ing paradigms, technologies, environments, platforms, andspaces for personal, collaborative, formal and informallearning. These are collectively referred to herein asLearnscapes. Recent development in communication andnetworking of physical objects (e.g., the internet of things)enables the creation of cyber-physical adaptive learningecosystems combining several aspects of the learnscapes(see Figure 5). Educause, a nonprofit organization whosemission is to advance higher education by promoting theintelligent use of information technology, publishes annualreports identifying the trends and challenges of higher ed-ucation, and highlighting the six promising technologiesfor that year (see, the 2013 horizon report [24]). Also, aninteresting infographic presentation (informative graphicposter), which attempts to organize a series of emergingtechnologies that are likely to influence education up tothe year 2040, is given in [25, 26]. Some of the majorgame changers for engineering education, and learningin general, are briefly described subsequently. These in-clude Open educational resources; technology-rich class-rooms; immersive interactive learning and augmented re-ality; Gamification of learning and instructions; reverseinstruction / flipped classroom; robots in the classrooms;and adaptive personalized learning. These are briefly de-scribed subsequently.5.1. Open educational resourcesSince the beginning of the twenty first century, a growingtrend of "opening of information" has emerged [27]. Thetrend is likely to result in disruptive changes in learn-

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Envisioning engineering education and practice in the coming intelligence convergence era - a complex adaptive systems approach

ing, teaching, assessment and research through havingfreely accessible tools and facilities, residing in the pub-lic domain. The MIT OpenCourseWare Project launchedin 2002, and aimed at posting all MIT undergraduate andgraduate courses online, is credited with sparking a globalopen educational movement. This was followed in 2005by the formation of the OpenCourseWare Consortium, withother leading academic institutions joining MIT in orderto extend the reach and impact of open course materials,foster new open course materials, and develop sustainablemodels for open course material publication. The Con-cept of Massive Open Online Courses (MOOCs), aimingat large-scale interactive participation and open accessvia the web, originated about 2008 within the open educa-tional resources movement. New institutions have formedaround this idea, such as Coursera, edX, Khan Academy,UDACITY, Big Data University, and others are likely tofollow. MOOCs are being widely explored as alterna-tives and supplements to traditional courses. In additionto traditional learners, professionals can use MOOCs totake enhancement classes and maintain an edge in theirdisciplines (Life-long learning). Examples of massivelyopen online courses in use in higher education settingsare given in [24]. As online instruments, simulation tools,data mining, and presentation methods evolve, virtual lab-oratories can be developed in which some experiments areconducted in simulated space. Also, new forms of archiv-ing massive collections of data that can help in severalaspects of research and learning can be developed (e.g.,computer-tracking of student activities to identify weak-nesses in the teaching; using remotely accumulated mea-surement and analysis tools to conduct research on datagathered by others, or reanalyzing earlier research datato confirm or refine theories).5.2. Technology-rich classrooms

Rapid advances in technology have revolutionized the wayin which people learn, communicate, socialize and play.A variety of technological gadgets and facilities are nowfixtures of our culture. These include smart portable andmobile devices, information appliances (see the animation,Figure 6), wireless networks, robots, educational games,social network sites, and other electronic resources. Ashift is taking place from print-centric to high qualityinteractive, and adaptive digital facilities. For example,tablet computers equipped with wireless connectivity, highresolution screens, and a wealth of applications, are prov-ing to be powerful learning tools inside and outside theclassroom.The aforementioned innovations have created a new cul-ture of learning, which is significantly different from tra-

See ESM1Figure 6. An Animation showing a smart information appliance with

wireless communication

ditional classroom culture [28]. The evolution of tech-nology has added a new dimension into learning, result-ing in more learner-centered, active learning experiences.Learners can master vital skills and critical thinking in acollaborative manner. Social media and digital librariesconnect learners to a wide range of informational re-sources. Learners use tablet computers, along with adap-tive learning systems and real-life projects, to learn attheir own pace, and instructors take on the role of coaches.The new learning technologies offer powerful capabili-ties for creating high quality learning resources, such ascapabilities for visualization, simulation, games, interac-tivity, intelligent tutoring, collaboration, assessment andfeedback. Also, these technologies enable iterative im-provement. Several attempts have been made to employpowerful technology integration strategies in classrooms,including the creation of technology-based lessons andinstructional activities, some of which can be publishedon the Web and used by students for both in-class andout-of-class learning activities. These efforts include "vir-tual / physical studios" in which traditional classroomsare replaced by studios and virtual instruction, resultingin bridging the online-offline gap. Learning then becomesa continuous, interconnected effort, allowing learners tocope with a perpetually changing world.5.3. Immersive interactive learning and aug-mented reality

3D is rapidly entering our life. 3D simulation based e-learning takes learners on an interactive journey and in-volves them in the course. It not only helps learners watchthe course, but also to participate in it. Also, 3D immer-sive simulation training allows the instructor to identifythe experience curves of each learner and analyze thelearner’s interaction and socializing while actively helpingthem work towards their defined goal. An opportunity torepeat modules of the course, as with all e-learning, helpslearners overcome the fear of failure and reach their goalsfaster. This adds a layer of interactive immersion thatengages learners and makes learning more effective [29].Therefore, it is usually referred to as in-depth learning,and is associated with other learning paradigms such asvisual learning, simulation-based learning, and engagedlearning. A research study conducted in the Virtual Hu-man Interaction Lab at Stanford University showed thatvirtual reality had a dramatic effect on the real-life be-612

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havior of learners. The more engaged people were in animmersive 3D environment, the more they engaged andlearnt about the subject matter. A 3D virtual world min-imizes the distance between the learner and the subjectmatter and enables rapid knowledge sharing and instantaccess to information. Learners from different places canaccess information at the same time and exchange docu-ments or files like they would in real life. It establishes aknowledge base for interconnected communities and con-verts traditional online teaching methods into a social ex-perience. It allows learners to demonstrate complex con-cepts, make simulations and invite others for discussionon various scientific or artistic creations. Participating ina 3D world seems equivalent to participating in a real-world social event. This social aspect can increase learn-ing ability and help with the acquisition and retentionof knowledge. Augmented (enhanced or blended) real-ity refers to the layering of digital information over 3Dspace to generate a new interactive experience for theuser. A key characteristic of augmented reality is its abil-ity to respond to user input. This interactivity providessignificant potential for situational learning and assess-ment [30]. Learners can gain new understanding based oninteracting with virtual objects, and understanding howto see contexts. Dynamic processes, large datasets, verylarge and very small objects can be brought into thelearner’s personal space at a scale, and in a form, easyto understand and work with. Smart mobile and wear-able devices (e.g., Google glass https://plus.google.

com/+projectglass#+projectglass/posts) are bring-ing new expectations for augmented reality applications(see, Figures 7 and 8).5.4. Gamification of learning and instruction

Gamification of learning refers to the use of game thinkingand game mechanics in order to engage the learners, andto make learning tasks appear like games [31]. A num-ber of classes, and curricula have been developed basedon serious games, or game-based learning. Several ad-vantages have been identified for gamification, includingtechnological literacy, critical thinking, creative problem-solving, multitasking, teamwork, long-range planning, andindividualized instruction. However, some concerns havebeen voiced about gamification .These include, the highcost associated with developing serious games, distractionof the learners from other valuable skills, social isolation,and shortened attention span. Current work on gamifica-tion is focused on designing adaptive games and effectivegame frameworks that transform the learning experience.This is done by exploring the manner in which learnersengage with games - their behaviors, mindsets, and moti-

Figure 7. Google augmented reality enabled glass with voice com-mand capability

Figure 8. Augmented reality glasses

vations.5.5. Reverse Instruction / flipped classroom

Reverse instruction (or flipped classroom) is a form ofblended learning which encompasses use of technologyto leverage the learning in a classroom, freeing the in-structor to interact with the students, instead of lecturing.Lectures (knowledge transfer) are delivered outside theclass via some type of streaming video, and learners areexpected to watch them on their own time. Turning lec-tures into homework and projects enables the learners toperform the most cognitively difficult work in class, when613

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Envisioning engineering education and practice in the coming intelligence convergence era - a complex adaptive systems approach

they have the instructors and peers to help them. Thiscan result in boosting student engagement and increasingtechnology-fueled creativity [32].5.6. Robots in the classrooms

Significant work has been devoted to the design of artifi-cial tutors, having some of the human capabilities, with theaim of helping to increase the efficiency achieved with ahuman instructor. The introduction of robots in the class-room, both at school and college and even as extracurric-ular activity, promotes a host of values such as creativity,participation, support, and teamwork. Instructors can re-motely operate robots in distant classrooms. Robots offera wide variety of learning modalities. Intelligent robotscan use built-in domain knowledge to help students learnmath and science through experience. Learners can makepredictions with math and then verify them with robots.South Korea is using robots with stereo vision, and theability to move around and interact with students. Theyuse facial expressions to communicate with students. Theycan articulate gestures, coordinated with the facial ex-pressions, to wink, yawn, and cheer. In the future, robotswill not only respond to what the learners say, but to howthey say it - factoring in social cues like intonation, ges-tures and facial expressions. The robots can then respondwith appropriate body language. Also, small inexpensiveprintable robots might be widely used in the classroom.5.7. Adaptive, personalized and Mass cus-tomized learning

In recent years electronic books have generated a stronginterest in the consumer sector, and are now increas-ingly available on campuses. A suite of adaptive learn-ing products for higher education, called LearnSmart Ad-vantage, was developed by McGraw-Hill Education. Ittakes adaptive learning beyond the realm of course studytools-providing students with more dynamic, personalizedlearning experiences across the learning experience. In-cluded in the suite are SmartBook, LearnSmart Prep,and LearnSmart Labs. SmartBook is an adaptive e-book, which enable the learners to focus their atten-tion on the content that is most critical to their learn-ing. LearnSmart Prep, provides "before-the-course" adap-tive resource designed to prepare students entering com-plex courses that are critical to the completion of theirmajor or degree. LearnSmart Labs, provides a photo-realistic virtual lab experience that enables meaning-ful scientific exploration and learning, while eliminatingmany of the practical challenges of a physical lab set-ting. In 2012, a digital textbook provider, CourseSmart,

in collaboration with some other partners, launched theiranalytics package, CourseSmart Analytics (http://www.coursesmart.com/go/institutions/analytics). Theanalytics package closely tracks the learners’ activity asthey interact with online learning material, and interpretsthat data for the instructors, providing them with an En-gagement Score for each text. The instructors can use thisdata to select effective engaging digital resources. Thefacility also provides better insights to help drive learnerretention, and better learning outcomes.6. Intelligent Adaptive Cyber-Physical Ecosystems for EngineeringEducationA holistic approach and a comprehensive strategy areneeded to meet current and future needs in engineeringeducation, and to put engineering activities on an am-bitious trajectory that pushes the frontiers of innovation,discovery and economic development [33]. This can be pro-vided by viewing engineering education and practice, andeven innovations, as complex adaptive systems [34, 35].Complex adaptive systems have a number of major char-acteristics, including having a number of heterogeneouscomponents; with the components dynamically interactingwith each other. The systems have emergent behaviorswhich result from the interactions, such that the wholebecomes greater than the sum of the parts. The appli-cation of the complex adaptive systems approach to en-gineering education enables using the concepts of com-plexity science to continually enhance the effectivenessof learning. The independent components of the sys-tem (instructors, individual, small and large groups oflearners) dynamically interact and communicate with eachother, and with other components (e.g., instructional ma-terials, learning technologies, assessment tools, and poli-cies). Design of the system should focus on equippingthe components with the needed capabilities (e.g., noveltechnologies and advanced tools), and enabling the in-teractions to meet the dynamic goal of continually im-proving learning. A step towards the implementation ofthe strategy is the development of Intelligent AdaptiveCyber-Physical Engineering Ecosystems to connect thevarious stakeholders of engineering education, and to pro-vide an infrastructure for collaborative innovation. Theecosystems, which can be thought of as multifaceted en-tities spanning technology and sociology, should exploitand integrate several of the game changers in engineer-ing education, as well as the innovative capabilities andtechnologies that emerged in the last few years. The ca-pabilities include the CIF21 - cyberinfrastructure frame-

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work for the 21st Century Science and Engineering ofthe US National Science Foundation, and its applica-tions, including the nanoHUB, and DIA2 - Cyberinfras-tructure for Engineering Education. The overall goal ofthe CIF21 (http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504730) is to provide a comprehensive, in-tegrated, sustainable, and secure cyberinfrastructure toaccelerate research and education and new functional ca-pabilities in computational and data-intensive science andengineering. The nanoHUB (http://nanohub.org/),which is a multiuniversity network led by Purdue univer-sity, provides extensive online educational, simulation andcollaboration facilities for various aspects of nanoscienceand nanotechnology, including manufacturing of complexnanomachines. The DIA2 (http://www.ci4ene.org/) isa multi-institutional collaboration project funded by NSF.It is built on the previous NSF-funded project called theInteractive Knowledge Networks for Engineering Educa-tion Research (iKNEER), [36] (http://ci4ene07.ecn.purdue.edu/ikneer/) , which is data-intensive knowl-edge platform being developed by researchers from Pur-due, Virginia Tech, Stanford and Arizona State University.The iKNEER project is used to collect, index, and enablesense making of a large sets of documents on engineer-ing education, as well as to identify possible collaborativepartners in this field. The proposed ecosystems are envi-sioned as broad ecologies of dynamic networked smart de-vices, cyber collaboration and collective intelligence facil-ities; blended physical and multisensory virtual environ-ments; novel interaction technologies; telepresence andcognitive robots (with high-level reasoning, planning, anddecision making capabilities); and new types of predictive/ anticipatory search facilities. The ecosystems will linkresearch and academic institutions with industry, profes-sional societies, technology providers, and other stake-holders. It will combine the novel technologies, facilitiesand devices being developed to exploit and augment hu-man capabilities. Humans will have multisensory, immer-sive 3D experiences in blended spaces. Specifically, theecosystems would provide:

• Knowledge-rich, multisensory immersive environ-ments for integrating engineering practice withlearning, training, and workforce developmentneeded for future complex systems and projects.• Platforms for facilitating and accelerating innova-tions in future virtual product creation (see Figure9), and for developing new interdisciplinary fields[9], as well as for expanding the scope of the currentones.• Environments for adaptive, personalized and mass-customized multisensory learning, providing 24/7

Figure 9. Components of future virtual product creation

access to information, and enabling learning any-where, anytime with any device.• Facilities for assessing the effectiveness of newlearning paradigms, based on the 4 C’s, namely:

– Critical thinking and Problem solving,– Communication, social networking and perva-sive computing– Collaborative intelligence and crowdsourcing,and– Creativity and Innovation.

New patterns of organized culture and new paradigms andtechnologies for learning and engineering practice willemerge. For example, disintermediation, or undoing thetraditional learner-instructor model, and replacing it byadaptive personalized learning (described in a succeedingsubsection) The components of the ecosystems are contin-ually updated and expanded. The ecosystems cannot befully defined a priori, but rather emerge from the interac-tions among the components, as well as the interactionsof the users with the environment. Therefore, the designof the ecosystems cannot be based on the traditional top-down systems engineering approach. Rather, a bottom-upemergent engineering approach is used, in which the com-ponents are designed and the interactions are engineeredto enable the system to change and expand as needed.Through evolutionary adaptation processes, progressivelybetter (and continuously improving) learning environmentsare generated.6.1. Major componentsSome of the major / key components of the ecosystems,which are not currently available in the NSF-supported

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Envisioning engineering education and practice in the coming intelligence convergence era - a complex adaptive systems approach

cyberinfrastructure are Knowledge discovery, customiza-tion and exploitation facilities; blended learning facilities;visual simulation tools with immersive 3D stereo capa-bility, autonomous interfaces with anticipatory / predic-tive search engines; novel intelligent software agents andadvanced multimodal interaction. These are briefly de-scribed subsequently.• Integrated knowledge discovery, customization

and exploitation facilities - These enable accessto massive online data and open educational re-sources, and provide the right knowledge for theright purpose at the right time. They will in-clude comprehensive information retrieval and cus-tomization tools, decision support tools, conceptmaps, mind maps, interactive visualization, auto-matic summarization and recommender systems forproviding personalized information and expert ad-vice. They should leverage Big data predictive/ prescriptive analytics tools and computationalknowledge technologies developed by the researchteams at the IBM Watson project, Wolfram Al-pha, Grok (https://www.groksolutions.com/),and other lead organizations. New types of pre-dictive search facilities should be incorporated fordynamic applications, where information is flowingcontinuously to and from users and devices. An ex-ample of the predictive search facility is the Mind-Meld facility developed by Expect Labs. It operateslike a combination of Siri and Google Now.• Blended Learning and research facilities - Incor-porating the latest 3D immersive virtual worlds,virtual holography, augmented / enhanced reality,and novel classroom technologies, to facilitate dis-tributed collaboration and to provide a 360 degreemultisensory experience for the user (see Figures10 to 12). The multisensory representation of largecomplex engineering data and models through thecombined use of visualization, haptic feedback, andother modalities of interaction can significantly in-crease the bandwidth of human-computer interface.• Novel Intelligent software agents (Cybernetic As-

sistants - These have cognitive learning and un-derstanding abilities, and serve as virtual assis-tants and / or effective learning companions. Theyhave the capability to recognize the user’s reactionscommunicated, for example, through facial expres-sions and the vocal intonation, and provide appro-priate response. The interaction with the user willbe more like face-to-face interaction. Advances incognitive neuroscience and neuro-informatics might

Figure 10. Virtual world Lab showing Einstein avatar giving lectureon particle physics

Figure 11. zSpace tablet providing Interaction with 3D Virtual Holo-graphic displays

provide the cybernetic assistant with more insightand understanding of the human thought develop-ment. This can result in engaging the users and op-timizing (personalizing) the learning for each user.• Multimodal and Autonomous Interfaces - includ-ing novel mobile / portable / wearable devices andwireless communication, touchless interfaces (seethe animations in Figures 13 and 14), brain-basedand neural interfaces, multitouch, gestures, adapt-able and adaptive interfaces, and media-rich com-munication tools. Visual interfaces, included in thesystem, will help in perceiving abstract data andgaining insight from it. With these facilities theuser interaction with computing devices, physicalsystems, and information in general will be simple,natural, and seamless.

Autonomous interfaces, with predictive / anticipatory com-puting engines, can allow more detailed understanding of616

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Figure 12. Immersive Interactive Classroom Concept (learners caninteract with the 3D model on the large screen, with 3Dvirtual holographic displays on tablets, and with other in-formation on smart mobile devices)

See ESM2Figure 13. Animation showing Eon Reality Touchless Interface

the learners’ attention and comprehension, thereby en-abling individualized and connected learning. This canbe accomplished, for example, by tracking the eye move-ment and facial expressions of the leaners, and using bigdata learning analytics tools, to make carefully calculatedadjustments and suggestions to keep learners motivatedas they master concepts or encounter stumbling blocks.• Cognitive robots, telepresence robots,bots and

robotars(visible and virtual) - operating au-tonomously in some tasks, monitoring and control-ling informational processes, and capable of detect-ing when the user needs assistance.7. Concluding remarksThe role and scope of the engineering profession aretransforming rapidly. Both what engineers do, and howthey do it, are changing. A number of emerging trends arelikely to shape the future of engineering education, and oflearning, in general. These include the widespread avail-ability of information; the accelerating rate of technologydevelopment; the new interdisciplinary fields; future grandchallenges, and the expanding role of engineering in thesociety. To meet future challenges of the rapidly chang-ing engineering fields, a strategy is needed for increas-ing the pipeline of skilled workforce. Moreover, in thepost-information age and the coming of the intelligenceconvergence era, new cyber, visual, novel media skills willbe required from the entrants into the engineering work-force. A holistic perspective and a comprehensive strategyare needed to put engineering activities on an ambitious

See ESM3Figure 14. Animation showing Touchless Interface using holo-

graphic device

trajectory that pushes the frontiers of innovation, discov-ery, and economic development. A step toward the imple-mentation of that strategy is the development of intelli-gent adaptive cyber-physical ecosystems. The ecosystemswould amplify human cognitive and perceptual capabili-ties, revolutionize learning, and enable the engineeringworkforce to perform increasingly complex and imagina-tive tasks of synthesis and creativity. Engineers can beworking with experts in artificial intelligence and othertechnology teams on transforming many current products,industries, and practices into complex adaptive systems.Future engineering systems (e.g., vehicles, bridges and oilplatforms) may be able to alert their human minders thatthey need repair before failure occurs. Also, biologically-inspired, self-healing engineering systems, will be devel-oped. An example is provided by the bio-inspired Shapeshifting, Self-healing Aircraft concept proposed by NASALangley (see the animation in Figure 15).See ESM4

Figure 15. Animation depicting bio-inspired Shape shifting, Self-healing Aircraft concept proposed by NASA Langley

The ecosystems will also facilitate the development of the21st century information, digital, and visual skills amongthe learners. They will provide 3D immersive environmentfor promoting active collaborative learning, critical think-ing, interpretative analysis, problem solving and knowl-edge creation, as well as adaptation to rapid change. Theinnovations that can be realized in the proposed ecosys-tems extend well beyond anything we can currently imag-ine. New patterns of organized culture, new paradigms ofengineering practice, and new models of engineering edu-cation and organizations will emerge within these ecosys-tems, and support future engineering practice.References

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publications/ikneer_report.pdf

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//www.aee.odu.edu/futengprac/. The website, cre-ated as a companion knowledge repository to this arti-cle, contains links to material on various topics related toFuture Engineering Education and Practice.

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