Artificial Intelligence and Industrial Design Technology in Professional Sports: Examining P3 Technology

By Kristy Butler

Instructional Design and Technology as a discipline covers historical foundations, current practice, and future directions for design, learning, instruction, training and performance. Because it has evolved over the years, it now contains technology as a main mechanism to accomplish learning goals. As a whole, the field is exciting and provides students with the big picture of IDT while laying foundations for study design and learning across various fields and industries from education to healthcare to business to corporate organizations and the like (Reiser and Dempsey, 2018). Lately, sports has become the newest hot industry to utilize principles of IDT to continue its billion dollar reign (Utermohlen, 2019).

The following paper specifically examines Artificial Intelligence (AI) and machine learning (ML) as a new and exciting emerging technology that is not only able to inform the field of IDT across industries but utilizes many theoretical models to design programs that also can exhibit far reaching capabilities based on IDT foundational principles, theories, process and analysis to inform and revolutionize the field and future of professional sports creating a marriage between the two.

AI Background and History

When we think of a new wave of the future based on computers or Artificial Intelligence, what has often been perpetuated in our minds are images of a robots behaving mechanically as humans or taking over for humans with no real independent cognition or learning on the part of the robot (nor any real information exchange on a higher level to the human being from the robot, only from us to them). However, with the advent of AI, this ideology actually could not be further from the truth. In fact, it has been theorized that AI is so advanced that it is actually intellectually sound enough it can make more efficient use of revenue based on data, health related decisions, save on costs, inform on a design for better learning, train for better performance and actually utilize intelligence to minimize time and resources which can skyrocket a company or business’s bottom line, safety and ROI.

Relevant Description of Key implications: AI Importance in Sports and Relevance in learning (i.e. e-learning, deep learning, machine learning, etc).

Well, it is also no secret when we turn on the television in many American households or take a wrong turn on a street leading to a tail-gating frenzy of hot dogs, barbecue and beverages, jerseys and face paint, that sports are a multi-billion dollar a year entertainment and money business, with professional sports as a whole (such as the NFL, NBA, MLB, etc.) collectively being predicted to rake in at least $73.5 billion by the end of 2019 with no signs of slowing down (Utermohlen, 2019).

So, what does AI have to do with sports, learning and technology and these expenses and profits? Coincidentally, technology is starting to play an extremely important role in this bottom line, i.e. the financial expansion of this business to say the least (Utermohlen, 2019). Consequently, AI is the technology (and models of learning) that has become a major part of the narrative to expand sports as a business.

AI, though expensive, is a major trend that through science can help lower the rising cost of ill- constructed unneeded e-learning programs that do not meet the needs of the learner, can advise other instructional and training programs and can eliminate theories being underutilized or utilized wastefully or extraneously. AI and ML through its computer vision have been said to increase money, save businesses and ultimately save lives. (Utermohlen, 2019).

This is no different in the arena of sports. As mentioned above, AI and the newest emergent computational data technology and wearable gear is said to minimize or eliminate sports injuries like concussions, health hot points or fatigue, help referees with costly calls that could be in error and cost an organization to lose a game, and help with successful play calls in offense via the push and utilization of data analytics. According to Utermohlen, a writer for the AI and ML tech publication, Heartbeat (2019), if more successful plays are called then there is more of a chance to showcase the offense, giving more offense visibility, and the more points there are the more wins and more viewers for that team.

So how do you use data and predictive analytics to make that happen?

Utermohlen (2019) brings up critical points, for example what if a receiver was able to learn that every time they made a certain cut on third down, they would get open? Or if a linebacker had a tendency to learn to shift to a specific side depending on a “quarterback’s call at the line of scrimmage?” and what if hours and hours of film study could be eliminated due to AI? (Arpaz, 2018). AI can help compile for the NFL, NBA or other teams, what will be useful for their decisions. This utilization of data analytics and learning via AI could cut down on time via the cost of filming and studying of hours and hours of film to try to learn what should be done by rote repetition vs. AI where data analytics creates new ways of cost-effective, safer, more accurate learning as emerging technology (Arpaz, 2018). Ironically, technology, e-learning all also take considerable money to implement and so they must be implemented strategically, accurately, correctly and soundly and according to the research and latest reports, computational data appears, when used correctly, to eliminate misguided programs.

Major theorists and researchers

AI runs on the following theories and design principles and includes many major theorists and researchers.

One primary foundational premise is that of Machine Learning (ML). Machine Learning theory which is also known as Computational Learning Theory combines tools from Statistics and Computer science (Blum, 2014) and looks at fast-adaptive learning algorithms and performance guarantees. Programs are designed such that rules are learned from the data, there is ability to adapt to changes, and performance improves with experience (Blum, 2014) The goal of ML is to establish and understand the fundamental learning principles as a computational process. It utilizes math to precision to discover what information and capabilities will allow different tasks successfully; and the utilization of computers to get data for improving performance and allow feedback (Blum, 2014).

Other theories AI tends to rely heavily on include: Performance theory, Cognitive theory, Learning Analytics mechanisms and Return on Investment (ROI) and Evaluation. We will see more on some of these below.

Case justification.

Why should we study whether AI works? As stated above, a lot of money is being spent for this technology to increase the bottom line, which is the almighty dollar, for professional sports leagues. This connection could make or break careers and organizations. But, the true bottom line should not be the investor, but the human being. We do not wish for a scenario where robots are being designed to replace the human race, that could be a misconception or could it? What we hope; however, is that these factors can truly assist organizations to resolve pertinent issues, make more efficient use of their time, investment, design and refine and analyze the data to a point that it better educates coaches and can prepare and train the total human being. At the end of it all athletes are still people and not machines. Video games and fantasy leagues can utilize all the computational data in the world, but depending on it to determine human lives and success, even if money is the aim for some organizations, the person is considered the anomaly not a machine. Is it worth the time, belief, investment and risk?

While loss prevention for the investor of their money is the organization’s chief concern, could AI therefore be created more for the investor or for the athlete? Is it now popularized to guard annual revenue over the priority of safety? Or is AI all encompassing, considering the total athlete and the game? For some sports, life-threatening injuries such as brain injuries can take a player out of not only the game of sports, but the game of life for good. People are not just a number, but with the advent of more and more AI, it could make it look like this is what it is all about. The hope is that what is learned through AI and ML, the sports industry, can be used for more good than bad. Part of the purpose of looking at the use of AI and ML is to determine next steps and inform the field as to whether AI is doing a justice or a disservice, gaining an insight into the methods and data collected that will come to inform design, instruction, modes of learning and the usage of technology and evolving trends in technology to accomplish this end.

Artificial Intelligence and Industrial Design Technology in professional sports.

The CASE of Peak Performance Project (P3): When, Where, Who, What, How and Why

We have seen in the instructional design field that technology can be utilized for performance, instruction, design and learning. It is making such a big mark in the field due to new emergent, innovative, almost futuristic designs now spilling over into a number of fields, with recent trends and increased popularity in the professional sports arena. While it is no secret professional sports organizations want to make money by recruiting top talent, generate sustainable revenue to exceed a return on investment, sustain their athletes from injury and sustain their top performances and wins for as many years as possible, technology and learning via new emerging programs and mechanisms have taken a front seat and are doing just that.

Their secret weapon: Artificial Intelligence (AI) and Machine Learning (ML). AI is known to utilize data-driven analysis for decision-making, seen in games like chess or other games for learning, healthcare, judicial system, transportation industry and for high intelligence organizations (Reiser, 2017), while in sports there are physical performance aspects of the software. The unique antidote is that not only is physical learning going on for the athlete, coaches and team, but via multiple IDT theoretical and analytical mechanisms, teams and organizations are supposedly using data from AI and machine learning to choose better, coach better, prevent injuries and move through analysis and cognitive learning to assist with the total package, as well as eventually depending on technology on how to guarantee a win! There are a number of programs coming forward that utilize AI and ML, and are competing across industries, the Peak Performance Project (P3) is one of them. It is not depending on human power, where as the saying goes, may the best man win, but now potentially, may the best AI software and ML program win.

What: Peak Performance Project (P3). Though there are other similar AI sports cases, such as (sports education with young kids to and even STATS, patented and adapted by the Orlando Magic (Ogus, 2019).

The following looks at the case of P3, a new emergent technology developed by Dr. Marcus Elliott a Harvard-trained Physician who as the Founder and Director of the project for professional “power-based” sports, such as basketball, baseball, football and soccer. Dr. Elliott leads a team of approximately 20 sports scientists and engineers along with data specialist to utilize athlete data to prevent injuries and develop performance in professional sports. Dr. Elliott’s focus and specialization is on performance enhancement and development of “elite” athletes as he has worked across the world from the U.S. Olympics to International science and sports institutes (P3 website). The P3 website states that he is committed to utilizing cutting edge science application for the development of optimal athletes (P3 website) even working with the NFL’s New England Patriots from 1999 through the Super Bowl ( P3 website).

According to the 2019 Aspen Ideas Festival, through his educational pursuits, the goal of Dr. Elliott was to eliminate the “cookie-cutter” myth and ideology that to be elite in sports a person would have to either work hard, be genetically predisposed with natural ability have the sport aptitude or possess a strong work ethic to be successful.

Through his study and the basis of P3, it was found that an athlete’s biomechanical factors could take a front seat along with the ability to link successive segments put teams and athletes a cut above the rest, surpassing and giving a competitive advantage over teams who utilized the age old coaching or training regimen of weights and sprints in hopes of gaining an advantage (P3).

Purpose of P3 and for Who. Specifically, P3, according to the P3 website involves Artificial Intelligence (AI) and Machine Learning (ML) software developed for the coaches, owners, organizations and athletes from the young and inexperienced to the professional. Though the plan does not seem to be to replace NBA or NFL players or other professional athletes, with superhuman robots anytime soon, one of its primary uses that get surprisingly close to that type of magnitude and scale is how Dr. Elliott’s P3 technology is utilized to compare and contrast athletes who are considered elite, i.e. have “numbers” ranging from just surpassing the norm to superhuman magnitudes and status (P3). In essence, P3 was designed to presumably discern the elite from the average and the unique from the run of the mill to help business and athletes improve, prevent, increase and soar to the top.

How P3 works (P3 website). P3 works by utilizing a 3D Motion Capture and Force Plate technology to look at how movements are executed by the athlete (P3 website). Cameras are set up to record athletes performing certain athletic tasks, data is recorded, and learning analytics are performed, data analysis and software solutions are provided to reshape multiple dynamics within the organization. Via P3 coaches learn and figure out how to better coach specific athletes, the overall team and develop strategies in opposition to other teams. In fact, the NBA coaches have been quoted as calling P3 machine learning and AI the “magic sauce” to training athletes.

After recording, quantitative data is analyzed from scientific standpoint looking at variables such movement, angles, pressure, torque. All of this data informs higher level information on performance, chances at preventing injury and cost effectiveness and higher level ROI of the team regarding the athlete, as well as machine learning algorithms to compare to other NBA players. Higher level as in not just recording and spitting out information, but actually redirecting training and intricately solving issues the human mind may not have thought of or be privy to.

Multiple branches of data analysis and in fact can compare longevity of NBA careers based on certain data points to shorter careers and to early injured and to those who can be predicted to have a few big seasons but short careers due to information from machine learning (P3). It apparently surpasses the human eye in what the ”right” recruiting size, weight and symmetry of movement and physical systems say according to Elliot. It is the “first training center to utilize advanced sports science technologies to assess injuries in professional athletes before they occur.” (P3 website ). According to Elliot’s report, the lab and machine learning are able to compare data to even assess super young athletes before full development to assess if they are able to achieve optimal levels in the future as athletes and predict their future beyond what the human eye of a recruiter can do. P3 has also compared James Hardin and Doncic saying they were determined by P3 to be elite humans. AI gave the NBA draft the competitive edge and insight giving way to programs like P3.  Specifically, how P3 functions, according to the website, the program:

• Utilizes biomechanical equipment and technology along with “rigorous testing” and science experts to interpret data;
• Provides access to performance and prevention intelligence and what they call “Money Ball” data metrics (P3 website)
• Incorporates “Elite Therapy,” which utilizes manual therapy with biomechanical analysis and correction that surpasses the standard rehab methods which contain passive traditional methods and focus instead on active healing and correcting movement flaws by selectively healing the damaged tissues to return the athlete to a more superior level.

According to Dr. Elliott, utilization of Newtonian physics (Newton’s Laws of Motion) (Wallace, 2017) can pinpoint and foresee errors, prevent injuries and predict the best in the world via mere cameras, angles and motion data (P3).

Why the Need for P3, AI and Programs Like It? P3 and programs like it are being developed at a rapid rate. So, there is a marked need to look more into AI, across the board, as AI is now being utilized in multiple fields, but especially in professional sports to not only help business innovators and leadership examine emerging and current trends in AI (Kumba, 2019) and sports technology or to have a chance to develop, acquire, and design the most suitable program to competitively set up for “the win, ” but to identify the validity, soundness and structure according to the special needs of athletes and consider the ways they learn cognitively, physically and intuitively to not only prevent life threatening injury, but hopefully use that prevention (and appropriate recovery) to save lives..

Design Principles in AI and Learning Sciences from Reiser and Dempsey (2018) and if associated with P3, AI and ML.

• AI apparently utilizes supportive learning environments as described in Reiser and Dempsey Chapter 8, pg. 73. The P3 program by Elliott eludes to having supportive learning environments merely by its design in testing and training. Also post testing, a program is structured to help the coach tailor to that athlete’s needs. I wonder how in depth the supportive learning environment is if the athlete is not considered as elite as others, i.e. what time is committed if the athlete is on the roster but not part of the starting line-up versus for an athlete who is considered the next James Hardin or Michael Jorda
• Supportive learning environment in evaluation without “direct instructivist” interaction to get a clearer picture, thus allowing the athlete to have some autonomy. But, not informal learning. P3 is very structured and formal. Provides room for self-directed learning like learning to ride a tricycle/bicycle, the book gives as an example, but definitely the program has some constraints as opposite of some apps, such as Homecourt, which allow athletes (or anyone) to amp up their shooting while not “directly” being monitored (Chemi, 2019).
• Constructivism from a cognitive perspective where the learner is not only machine learning in a physical way, but cognitive processes are going on that should also be a psychological learning process.
• P3 also represents a learner centered classroom or learner centered environment
• Computer scaffolding involves support tasks and human cognition where the computer follows along with the learner (athlete) as they advance through certain processes or limited processes. Computer supported intentional environments possibly (Reiser, pg. 73)The example of learning sciences and expert chess players as compared to novice chess players is an example of the key findings from learning sciences and expert thinking which appears relevant in the P3 program, e.g. exercises being “executed easily without conscious thought” could separate elite players from the novice NBA, though physical prowess and seemingly natural ability, could be strategy and cognitive as well as skill improvisation (Reiser, pg. 72). Automatic skills allow for free cognition to solve other issues. And Metacognitive processes allow for longer-lasting learning. All of these, not heavily stated, seem at work in P3.
• Theory of multiple intelligences may have to do with learning preferences Reiser cites Gardner (Reiser, 2017). In looking at this one, there is more evaluation needed.  But, because the game of basketball or football requires multiple thought processes beyond someone’s natural ability at shooting and scoring a touchdown, this is likely a theoretical bases of P3’s design.  And if it is not, it should be.
• Newell and Simon’s unified theories of cognition (Reiser cites Newell, 1990) states that the mind is flexible but has specific limitation in processing information and has to rely on specialized brain system for vision memory attention motor skills, planning and language (72) and deeper conceptual knowledge. Cognition and cognitive psychology. Context importance- “ The recognition of the importance of context suggested that the unit of analysis for understanding learning had to be larger than the individual person. People learned things with other people and generally learned with culturally developed tools and artifacts “ and later says People’s cognitive processes are created in conjunction with the tools of the culture and at the same time the tools of the culture are enhanced by the thinking of people and societies. “ (Reiser, Chapter 8, pg. 69
• Computer science integration- Artificial intelligence by Anderson, Boyle and Reiser in 1985 said that AI could be utilized to problem solve and the computer could give info to the computer… intelligent tutoring. Authentic problems were utilized to motivate. (Reiser, pg. 69). This is evident in P3’s design.
• Data Analytics usage and learning models– utilization of algorithms which can be tuned and optimized to help generate better results that allow for applied knowledge and support decisions (Systemic Review, Springer). P3 proposes and highly depends on complex use of algorithms to structure plans for training (i.e. learning plans tailored for the athlete and designed for the trainers), rehabilitation, team strategy, to reduce injury and select draft picks. According to the P3 accolades and articles, Dr. Elliott has been very good at conquering this.
• Computational data the 4 C’s Content, Compute, Communicate and Capture (Reiser, et al. pg. 245- 246) provides information to supplement formal learning and performance support, computational capabilities and capture performances and to reflect, share, communicate and cooperate. P3 heavily relies on this. Computational data is the epitome of the success of P3 and the goal of ML and to increase the bottom line of the NBA and other major sports.
• Other important theories include: Learning analytics theory (Chapter 12); Performance improvement and performance support (Section 5, Chapter 15 and Chapter 25); Adaptive learning and motivation.
• ROI technology-based and Evaluation Learning (Chapter 11)- Reiser (2017) talks about the criticalness of this and how they are tied together. Organizations want a return on their investment and evaluation is part of the equation. This is definitely an aim of P3 and AI/ML technology like it. This specifically speaks to the organizational goal to profit.
• Deep learning and Machine Learning– AI involves machines doing tasks that utilize human intelligence, which is inclusive of machine learning where the machines can utilize experience and attain attributes and skills without the involvement of a human being (Marr, 2018). Deep learning is part of ML where there are algorithms involved and intricate neural processes influenced and similar to how the human brain works which take in huge amounts of data an learn from the data (Marr, 2018). What does a deep learning algorithm do? As it takes in this information repeatedly during a task, it is able to revise and tweak the activity and data to give valuable information similar to how we as humans learn from experience, the machine learns from experience, i.e. repetition to improve learning capacity in its deep layers (Marr, 2018). It solves problems just as a human would think to solve problems (Marr, 2018). Deep learning requires a ton of data in which to learn and data that we output daily is near 3 quintillion bytes (Marr, 2018) and web-assets.domo.com ). Strong computing needs and this need of data and the utilization of AI algorithms to efficiently utilize deep learning (Marr, 2018) and provide what is needed for organizations is exactly why P3 exists.
• Cognitive load theory and motivation– requires that mental effort go beyond the text book by taking off the limitations (Sweller, Ayres and Kalyuga, 2011 ) and it investigates what the instructional consequences are of limitations of processes associated with human cognition ( Sweller, Ayres and Kalyuga, 2011). The theory primarily consists of knowledge on two bases: biologically primary knowledge and biologically secondary knowledge (Sweller, 2011).

According to Sweller (2011) who begin his work in the 1980s in regard to CLT (Rouse and Bernstein, 2018), primary knowledge comes from the evolution of knowledge over generations. Secondary knowledge comes from recent cultural knowledge that is acquired. Cognitive load theory primarily focuses secondary knowledge and assumes “natural information processing system, biological evolution” where there is a plethora of knowledge structures and domains and the aim of design and instruction for the learners acquisition of that knowledge (Sweller, 2011 and Rouse and Bernstein, 2018). The two basic structures associated with human cognition and instructional design are working memory and long-term memory (Sweller, 2011). Sweller (2011) informs that instructional procedures are generated from this theory to unload extraneous working memory and allow for the formation of long term memory or germaine load which transfers short term to long term memory (Rouse and Bernstein, 2018), which would likely be needed in professional sports especially when looking at AI and ML which Rouse et al. state should play a major role in instructional (i.e. coaching) strategies. Whether P3 is doing this, it appears to be if strategy and long term results are happening. However, the site does not specify all of the science being utilized. Sweller (2011) states,

“Cognitive load theory assumes a limited working memory used to process novel information and a large, long-term memory used to store knowledge that has been acquired for subsequent use. The purpose of instruction is to store information in long-term memory. That information consists of everything that has been learned, from isolated, rote learned facts to complex, fully understood concepts and procedures. Learning is defined as a positive change in long-term memory. If nothing has changed in long-term memory, nothing has been learned (Sweller, 2011).”

In this, he advises how to design instructional e-learning programs that are more learner and long-term memory- focused. When technology is used to present information to learners, the modality and format of the presentation is crucial. If the mode of presentation is changed from simple to complex, There is a criticality that results when Limited human working memory is presented with transient, technology-based information and the result could be negative and have dire instructional consequences. Another major issue that appears to effect cognitive process is in the emotional state and mood of the person, be it negative, positive, anxiety-ridden or perception of one’s own inadequacy (Sweller et al., 2011). There is strong evidence that it can effect how a task is actually performed as well as cognitive load (Kalyuga 2011). This according to Sweller, Ayres, Kalyuga (2011) is critical information for designing an affective instructional design. Other important issues could stem from redundant situations, split-attention, advanced schemas and knowledge or inadequate experience (Sweller, Ayres, Kalyuga, 2011). On the other hand, long-term memory and positive states eliminating too much external outside guidance for advanced learners is better and creates more motivation (Kalyuga, 2011).

It is likely cognitive processes are part of the consideration of P3 though not emphasized, where athletic performance is just importantly based on long-term memory and the reduction of any cognitive load. So, the science design by Dr. Elliott, I am hopeful has been influenced by this model and instructional (coaching) design in addition to physical/mechanical design to help P3 create the elite athlete it desires to do. If not, I would definitely challenge Dr. Elliott and his team on how sound it is in respect to cognitive load and distractions, because after all they are still dealing with human beings with natural abilities but differing minds.

Who can benefit, instructor-led/Coaching/Evaluation, computing, the instructional designer who enhances e-learning to be more cost-effective and not wasteful (e.g. if ill developed or overpurchased, improper training or underutilized when needed, it can be expensive). More research is definitely needed here.

SUMMARY

The case for using AI and ML in sports has been set. And as organizations seek to increase their bottom line, P3 and other trends like it will gain more and more popularity and develop more and more innovative ways to utilize and apply the data to do just that. Again, it is hopeful that the person will be more considered and take a front seat as designs evolve and that design and the desire for more cash flow will not veer so far to the innovative that the true science that proves beneficial from a health perspective is lost.

Case Strengths, Weaknesses and Challenges against Known Design Principles. Where does it leave the athletes, teams and fans? Can it replace human and factors uncontrolled for? No, humans are absolutely needed (Schlenker, 2015). ML proves that . In essence, Mistakes that are costly financially and physically, mistakes that could be advantageous vs. at a disadvantage. P3 has major strengths. It is an innovative technology that is said to do amazing things, so kudos on preventing injuries via real time computational data, better coaching and training through data analytics and the incorporation of scaffolding and constructivist learning models and some real life sporting techniques via Newtonian physics. However there are weaknesses that the math could be correct, but what about machine errors, the focus on money as the bottom line for organizations and authentic real play or changing non predictable situations even for an elite athlete? What about the athlete’s cognitive load, how can the data be sure in that given moment under certain pressure that has been compensated for?

This model because it has been created for professional sports is heavily reliant on athleticism and P3 specific predictors and training, but what about mental clarity, change in strategy and overall competence not just physically but mentally, emotionally and cognitively? Is P3 on top of that? It appears P3 could be utilizing more cognitive models or some cognitive load theory for secondary knowledge to shape and design strategy, but not sure how. Is there a gap here?

Potential Negatives, Disadvantages and Limitations. What happens when the computer is wrong? If the ML computational data is wrong? And they miss a great recruit, cannot predict a random distraction causing a unexpected hit which causes a life-threatening injury, or give a training program that is adverse to the player due to an error in data analysis? What if P3 or AI programs are not a crystal ball? On a positive note, the athlete was better than they (i.e. the computational data) thought, but missed out on a lifelong riches, career dream or the sports Hall of Fame? On a negative, it adversely affected any athlete negatively who could not handle fame, rejection or just being another number? Is AI well equipped to predict emotional maturity or cracks in self-esteem if effected by the software’s results?

Also, while AI programs such as P3 look good, from a hard data perspective, in the article Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systemic Review, the review questions computational techniques, statistical techniques and collected data that may or may not give a holistic view where context is concerned, how there is a responsibility to discriminate between public and private data, and questionable as to whether it can convey real world information.

Safety and health of the players, whether they should be back out there and re-injure or cause lifelong injury, i.e. such as concussion. As long as it helps and does not hurt. More research is definitely called for to be sure before we one hundred percent rely on machines to tell us our value, worth, or our future. We are more than a number.

Datarobot (2019) is a specific platform to assist with automated machine learning data to target it in the correct way. STATs, according to a Forbes article by Simon Ogus earlier in 2019, competes with P3 and is exclusive to the Orlando Magic (Ogus, 2019). Stacking up against other programs STATS and Data robot which utilize similar design principles in regard to data, AI/ML design methods and mechanisms. There is a big market for AI and much more study is needed.

Next Steps.

It appears that the Learning/Development and Industrial Design and Technology industries are being infiltrated by a growing technological trend in Artificial Intelligence (Schlenker, 2015) and according to an “Artificial Intelligence Market” prediction from 2017, there are no signs of it slowing down growing to almost $200 billion as an industry in the next 5 years (Markets and Markets, 2017). It is shaping learning, instruction and educational programs far beyond the traditional scope similar to how social media and the advent of YouTube took over and now has become the norm. Similarly, with anything new, there is a debate as to whether this is a good move because Artificial Intelligence has been looked at as so reliant on the use of technology that it is potentially a trend that could be so good at teaching learning and behavioral changes, that some think its technological value could threaten to replace more and more humans in many arenas (Schlenker,2015).  When we think of Artificial Intelligence we have before tended to think of it as robots who can do and behave and learn quicker than human beings changing the Human Performance field forever. Meaning learning can be manipulated and possibly surpass human barriers by encoding and pouring “knowledge” into a machine, taking that information to instruct human beings and blow human cognition, thought and experience out of the water to a far superior level than what even the “best” humans can learn, see, do or be themselves.  Infiltration of technology can appear threatening.  The company or industry who are concerned about the almighty cash flow, can figuratively and literally cut the middle man, cut extraneous superfluous cost and can design and program the learning result they want, ie. Elite human or non-human robots.

With this advent and integration of technology, could it be that utilization of AI and ML programs, like P3 are taking over? Could they actually better integrate principles of IDT into Artificial Intelligent software and program design (Claudino et al, 2019; Schlenker, 2015)? And can they lead several fields to benefit including professional sports leagues, NFL, NBA to give them not only a better platform for learning (physically) mentally and emotionally for their athletes, but also help them naturally select athletes that could make them a bigger ROI than other computer determined “mediocre” athletes?  

And how early could that be determined, when and to what level? Building a super team that also informs mental and physical aptitude and places them almost like fantasy football without (or with the perpetual puppet strings) similar to the skill and orchestration of a video game controller? It makes you consider fantasy leagues and video games if they are simply desensitizing us to accept that we are fallible and the powers that be could maneuver learning either by computers and technological advances which could create superhuman human beings, clones or the like. Can constantly evolving AI technology do us more of a service or disservice in the future by creating such “elite” learners that only the strongest survive, giving new meaning to Darwin’s theory of Survival of the Fittest?

References

Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography – Global Forecast to 2025. (2018, Feb). Retrieved from https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html

Arpaz, J. 3 Ways Artificial Intelligence Can Save the National Football League, (2018, Jan 11). Forbes. Retrieved from https://www.forbes.com/sites/joeharpaz/2018/01/11/3-ways-artificial-intelligence-can-save-the-national-football-league/#9fc00347da74

Aspen Ideas Festival. Dr. Marcus Elliott (2019). Retrieved from https://admin.aspenideasfestival.production.a17.io/search?term=Dr.+Marcus+Elliott

Blum, A. Topics in Machine Learning Theory. CS-598 (2014, August 2). Retrieved from https://www.cs.cmu.edu/~avrim/ML14/lect0827.pdf

Chemi, E. How artificial intelligence is making better basketball shooters, with just your iPhone. CNBC April 17, 2019. https://cnbc.com/2019/04/17/how-artificial-intelligence-is-making -better-basketball-shooters-with-just-your-iphone.html

Claudino, J.G., Capanema, D.., de Souza, T.V. et al. Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systemic Review. Sports Med – Open (2019) 5: 28. Retrieved from DOI https://doi.org/10.1186/s40798-019-0202-3 03 July 2019                                                                                                                                                           

Cohen, B. The Big Data Behind the NBA’s Next Big Thing. (2019). Wall Street Journal. Retrieved from https:/www.wsj.com/articles/luka-doncic-big-data-nba-11552413548

Data Robot. The 10 Steps of Automated Machine Learning. (2019) Retrieved from https:www.datarobot.com/platform/what-is-automated-machine-learning

Data never sleeps 5.0: How much data is generated every minute. Domo. (2017, July 17). Retrieved from https://Web-assets.domo.com/blog/wp-content/uploads/2017/07/17_domo.data-never-sleeps-5-01.png

Marr, B. What is deep learning AI? With 8 practical examples. (2018, Oct 1). Forbes. https://www.google

Morneu, D. Ghost in the Machine: No it’s a robot and its here to help. 2018, Nov 12) Forrester. Retrieved from https://go.forrester.com/blogs/ghost-in-the-machine-no-its-a-robot-and-it-is-here-to-help/

Ogus, S. Orlando Magic Enter Exclusive Deal With STATS To Begin Using AI Player Tracking Technology. (2019, Feb 27). Retrieved from https://www.google.comp/amp/s/2019/02/27/orlando-magic-enter-exclusive-deal-with-stats-to-begin-using-ai-player-tracking-technology/amp/

Peak Performance Project. Dr. Marcus Elliott. P3 Website. Retrieved from www.p3.md/team/marcus-elliott

Reiser and Dempsey. Trends and Issues in Instructional Design and Technology, 4th Edition 2018 Pearson.

Rouse M. and Bernstein, C. Cognitive Load Theory (CLT). Whatis.com, whatistechtarget.com, 2018. https://www.google.com/amp/s/whatis.techtarget.com/definition/cognitive-load-theory-CLT%3famp=1

Schlenker, L. Artificial Intelligent Instruction Design. Retrieved from https://www.litmos.com/blog/articles/artificial-intelligent-instructional-design

Schlenker, L. Putting AI First. 2019. Towards Data Science. Retrieved from https://towardsdatascience.com/putting-ai-first-2790e8dd0211

Schlenker, L. Artificial Intelligent Instruction Design. Retrieved from https://www.litmos.com/blog/articles/artificial-intelligent-instructional-design

Sweller, J., Ayres, P., Kalyuga, S.: Cognitive load theory. Psychology of learning and motivation. Springer, New York (2011) Vol 55. Pages 37-76. Retrieved from https://doi.org/10.1016/B978-0-12-387691-1.00002-8

Sweller, J. Cognitive Load Theory and e-learning. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg.

Utermohlen, K. 4 Ways AI is Changing How We Watch and Play Sports-Heartbeat- Fritz AI. Jan 29, 2019. Retrieved from https://heartbeat.fritz.ai/4-ways-ai-is-changing-how-we-watch-and-play-sports-bdbb64607b3

Wallace D. Fundamental and Emergent Geometry in Newtonian Physics. The British Journal for the Philosophy of Science, (2017, December 7). Retrieved from https://doi.org/10.1093/bjps/axx056David Wallace