Artificial Intelligence, Data, and Education

Education is short of funding. Teachers are not paid enough. The University cannot afford this. There is much more money to be made in finance or healthcare or sports.

All this is about to change. We have all heard some form of these statements. Why are the realities they express changing? What does it mean that education is entering the fourth industrial revolution or the digital wealth system?

Change worthy of the term “revolution” must connote a change to the substance, not merely accidental change. Moving from blackboard chalk to the Surface Hub is not revolutionary. Revolutionary change is what we see as we move from an educational system that was meant to identify the few, to a need for a system that pushes everyone to their fullest potential.

Homo sapiens technology grows with geometric acceleration. Further, a tipping point may occur where technologies that have been slowing developing, suddenly (to sapiens’ perceptions) move forward with amazing speed as work from related areas converges to create a wave of innovation and discovery. Consider the steam engine. Puttering along in mines from the early 18th century, only becoming a railway in 1830, more than 100 years later. Within mere decades of that first railway, the landscape of the British Isles was woven with thousands of miles of rail lines.

In similar fashion, we find ourselves witnessing a watershed of activity around what is called AI. This acronym is understood to be Artificial Intelligence: machines thinking like humans. We have been contemplating it and working on the concepts since the mid 20th century. Arguably, we are some way from realizing this vision today, but there is no question that machine learning is accelerating at a breathtaking pace.

How does this relate to the work of education? In a world where our wealth is driven by the creation of digital assets, the need for creativity and diversity in that creative process is fueled by a huge number of people participating. This is a material change from the world of agriculture or industry where the pyramid of participation is quite flat, with more people at the bottom and considerably fewer at the top. In this digital wealth system, we need more people. We need the education system to stop filtering.

In the agricultural wealth system, very few entered the educational institutions of the day, and fewer still succeeded within them. Exemplars might be Thomas Wolsey, a butcher’s son who rose to serve as the King’s most valued servant as Chancellor of England, or perhaps his successor, Thomas Cromwell, a blacksmith’s son who also rose to serve as the King’s most valued servant as Chancellor of England.

In the industrial wealth system, we also are filtering. We need a general state of literacy and numeracy, but we also need a larger cohort of administrators to manage the businesses. This paralleled the growth of the British Empire that also had great need for administrators. This resulted in general primary education considered minimal, and then led to a pinnacle with the general secondary education that is now considered minimal.

With the digital wealth system, we need more people, with diverse educational and life experiences, to fuel the creativity that results in digital innovation. We need to see the filtering removed and an educational experience that looks to catch everyone and bring them to their fullest potential. This was not desirable in the past, and it was also not economically or even physically possible.

The impossible can be done. With the developments in data estatemanagement and the subsequent application of machine learning, we can now consider practical the education of everyone to their fullest potential. If in my generation a student might encounter family difficulties that distract from the lessons of the moment, that student might then fail to understand key concepts in mathematics. In a class of 30, with six of these classes to teach, a math instructor cannot identify one or two or ten students out of 120 who might have a diversity of life challenges or prior learning gaps that cause confusion.

Imagine, however, that the instructor of 500 students at a large university has tools that scan the learning and social and institutional data of all 500 students and provide the student, the instructor, the academic advisor, and the institution with information that allows any of these people, within agreed privacy constraints, to take action to improve a learning gap. The student missed a concept for whatever reason? We can identify that and act on that information to the benefit of the student, and ultimately, society.

What is needed? First, we must consider the question of data. Where is it? Is it digital? Is it organized? Mastering the data estate of a learning institution is critical to reaching a place where we can ask the machine to learn from the data, analyze it, and predict based on the patterns it finds. This is a journey, but not a linear journey. An institution will look to its current state and begin the rationalization of the data estate and perhaps even some machine learning applications based on what is possible today. At the same time, that same institution may identify areas where the gathering of data is not optimal or not extant. New systems may be needed to gather data. These may even be passive, such as the data generated by the growing Internet of Things (IoT).

As we move this data into spaces where we can approach it, we will be able to apply machine learning to understand the learning experience of individual students and adapt our institutions, programs, and courses.

It is about scale. In education, we have always wanted to take care that every learner in our charge reaches their full potential. We know that small ratios of instructors to students leads to good outcomes. We understand that knowing each person in our course leads to good outcomes. We are clear that the complex of events leading to our carefully crafted learning experience must go well to ensure that experience leads to good outcomes. This need for scale to succeed in the digital wealth system is one that will drive investment. This is where we will being to see deep value in education that matches the aspirations of educators of the industrial revolution. So many need to learn. So many need our expertise. So many are needed in the developing digital wealth system. Only with our tools can we practically grasp the aspiration to empower every person on the planet to learn more.

What do you think?