The Promise, Peril, and Possibility of Data, Analytics, and AI in Higher Education (2 of 7): A Coming Shift in How People Find and Select a College

In article 1 of this 7-part series on data science and AI in higher education, I introduced a simple categorization for thinking about five ways in which data is used and shaping the present and future of higher education: college search and connection data, student retention and success data, student learning/mastery/progress data, organizational health data, and data that enables graduates to connect with other people and organizations. In this second article in the series, I am focusing upon that first category, college search and connection data. Some of my regular readers may be less interested in this category, while this is right in the sweet spot for others. For those who cringe at language about recruiting, admission, and marketing; I contend that what I write here has quite a bit of relevance for the nature of the learning communities in higher education as well, so I invite you to consider this article with that perspective in mind. Regardless, be assured that the next articles will focus upon the learner experience in higher education.

How do colleges find and recruit students?

There is not a single answer to this question. When it comes to how colleges find and recruit students, the answer depends upon the type of student (prospective traditional undergraduates, adults seeking a first degree, adults returning to complete a degree, adults seeking to return for a second degree or part-time graduate student, people seeking full-time graduate study, etc.). It also depends upon the type of institution. Nonetheless, the modern world of data science plays no small role. People search for almost everything online, and that is certainly true when it comes to exploring opportunities for a college degree. However, the dominant ways in which people use the Internet to search for and reach out to possible schools has more than a few limitations. Consider this one.

The Facebook Peer Advice Story

As part of my research about how people search for colleges, I informally interviewed lots of adults seeking to return for graduate degrees. I also identified some of the online platforms, online resources, and communities targeting people who are looking for degrees. Beyond that, I played the role of a prospective student and tried my luck at using Google as I typed in various questions, taking special note to which featured and paid ads showed up for me and followed me around the web (search re-targeting).

However, one of the more interesting experiences came in some of the education Facebook groups in which I participate. I noticed as people asked others in the group for advice on good online graduate education programs. Then I read through the hundreds of suggestions and comments. It was amazing to me how many people claimed that the online degree program of their choosing was the least expensive, most flexible, highest quality, etc. Granted, some of these insights are going to be subjective, but price is not. Yet, countless people thought they were getting the lowest price. Or, perhaps on the subjective side, they thought that they were getting the best value. Looking at the advice as objectively as possible, I can say with confidence that it was riddled with errors and misleading information. This was more like a debate about favorite sports teams than a careful investigation of schools and analysis of the features/attributes that are most important to a given prospective student.

Such folk systems work for people today. They have no small influence on how traditional students choose colleges as well. A reference from a trusted source, even if it is not well-informed or a best fit, is a major factor in people’s decisions. In fact, in a world of information overload, trusted sources, even wrong ones, are increasingly valued by people.

How do students search, learn about, and select colleges?

They Google. They browse YouTube. They talk to peers, mentors, and trusted people in their lives. They read articles and follow social media about various schools. They ask people in person and digitally. They are unquestionably influenced by media, marketing, and advertising. If they were not, there is no way that so many schools would be spending tens of millions (or in some cases more) annually for digital ad campaigns. Yet, I can say with confidence that these ad campaigns do not improve the quality of match or connection between student and school. A school is not the right fit simply because it was willing to pay more than another school to show up at the top of a Google search. This current system is hiding learning communities that would be a great fit for some people, and they are driving people to the learning communities with the most clever marketers or that prioritize marketing in their budget over other things.

The Dominant Metaphor

Right now the dominant metaphor related to college search and connection, especially in the adult and graduate world of part-time and online learning is a sales metaphor. We advertise to students. Even if schools resist the language, they are selling. Students are shopping. As such, much of what goes with that metaphor informs how people select schools. They shop. They are influenced by some brands more than others. Sales strategies and tactics are used and result in students choosing. There is buyer’s remorse in some cases. Even the attitude and mindset of students once they are enrolled continues to be influenced by this sales metaphor. In addition, schools are often thinking about recruitment as sales and competing with similar “products” and “services.”

The Changing Metaphor

There is a strong competitor to the sales metaphor for college search and connection, and it is growing. It is the dating metaphor.

Note: While some might not appreciate the shopping or dating metaphors because they would prefer something from high culture, or at least not from popular culture, these are the metaphors that I notice animating the language and systems that people are using.

With regard to the dating metaphor, think about the idea of a dating website. People don’t shop for a date. They enter data and the system makes recommendations about others who might be a good match. They might also have the opportunity to search for a “good match” but it is qualitatively different from dating. It is more about seeking out a good connection or relationship and less about buying something. In addition, some of the algorithms behind these dating websites are getting increasingly sophisticated, drawing from years of insights about what types of matches did or did not work well.

The dating metaphor points us to the growing role of algorithms and potentially full artificial intelligence to match and connect people with other people, people with organizations, as well as people with various products and services. Consider MyOptions (formerly Admitted.ly), a site that collects up to hundreds of bits of data about a prospective college student, and compares that with the data set they have about a large number of colleges. Based upon everything from weather preference to level of academic challenge, the system makes recommendations for students to consider. While it started as a system only for prospective traditional undergraduates, in my interview with the founder in 2017, she indicated plans to extend it to other populations as well. For better or worse, this is going to be how a growing number of people select education opportunities in the future.

Reliance upon trusted networks and sources will remain a strong (perhaps the strongest) source that informs decisions for quite some time, however. Yet, these algorithmic developments will likely begin to further shape the nature of recommendations within these trusted networks, leaning more and more toward a dating versus a shopping metaphor.

The Change

This will change the nature of recruitment and marketing, especially for the adult, part-time and online student. In fact, there is a good chance that it will disrupt the multi-billion dollar business of higher education advertising in the digital space. Without question, there will be platforms that claim to be a neutral source of matching students and colleges (these already exist), but they are actually getting paid by colleges to rank some over others. Yet, the platforms that truly are neutral and that combine both trusted networks and algorithms will win the day. There will also be (or rather there already are) systems that simply rank colleges on the basis of something like the College Scorecard. Those lack complexity and matching capabilities, however, and will likely be secondary to the more personalized and algorithmic platforms.

This will challenge some schools that spend 20% or more of tuition revenue to recruit students through traditional advertising. As more people turn to these “dating” platforms for finding educational options, the advertising wars of higher education may well begin to wane, and I consider that an incredibly positive shift.

Note that there is no small number of entrepreneurs seeking ways to capitalize upon this shift from the sales to dating metaphor. For example, in a 2017 interview with John Katzmann (founder of the Princeton Review, 2U, and Noodle Companies), he articulated a vision for a an Amazon.com of the education space. Granted, Amazon at first consideration sounds like more of the shopping metaphor, but even in my short conversation with Katzmann, I suspect that what eventually comes about will be much more driven by that dating metaphor. And John represents one of more than a half-dozen significant entrepreneurs with whom I’ve spoken who are working on this problem (the higher education advertising wars) and opportunity (making better connections between students and colleges).

Transparency Matters

There are many important aspects and nuances to what I’ve written here about the metaphor shift, but for the sake of brevity, I offer what I deem one of the most important, especially in the early stages of this algorithmic revolution in college search and connections. Algorithms are created by people and they include the beliefs and biases of people. As such, an algorithm can mislead and do harm as well. It is for that reason that I argue for making these as open and transparent as is reasonable. In addition, when possible, why not give the learner some control over how the algorithms? In other words, instead of having a fixed system that matches students and colleges on the basis of what the platform owner/creator deems most important, why not leave some room for the end user to manipulate the algorithm, playing with prioritizing and re-prioritizing some aspects of the matching to see how that influences the recommendations? Better yet, why not create a system that shows people recommendations/rankings based upon four or five different configurations of the algorithm? This empowers the learners and invites them into being a co-creator of the system as opposed to someone who is just matched and sorted. Agency matters for people and society, and as this is in the early stages, this is the time to think about how we are going to create systems that are more open, humane, and nurturing of agency.

The Promise, Peril, and Possibility of Data, Analytics, and AI in Higher Education: A Framework (1 of 7)

Some people are more fluent in the humanities than in quantitative or computational discourses. No matter. The future of education will be re-created by the language of math and its many relatives. Data, analytics, and AI are not passing fads, nor are they simple augmentations to what will remain a largely unchanged and age-old educational enterprise. As such, whether you dream in narratives or numbers, whether your heart language is that of literature, history, and philosophy or theorems, derivatives, and algorithms; you need to venture into the world of quantifiable data and numbers to understand and be a co-creator of the future of education. That is not easy for me to write or admit. I have a deep and longstanding passion for the humanities, and if you stay with me throughout this series of articles, you will read what I hope to be a strong case for their role in this new world of numbers, but that does not change the fact that quantitative and computational thinking and the results of that thinking will be a powerful driver of fundamental change in education.

As a student of futures in education, I am not a fatalist. I see ample evidence that individuals and groups of individuals can influence what happens in the future. The future direction of data, analytics, and AI in education is something that you and I can influence. If I didn’t think this to be true, I wouldn’t bother to write this article. There are and will be strong corporate, government, political, social, and philanthropic influences in this future, but your voice and actions matter as well. It is with this confidence that I make this next statement. Whether data, analytics, and AI will empower or simply control and influence learners is partly up to the decisions that we collectively and individually make in the next 5-10 years.  I am here to advocate for an approach that strives to promote a better, more hopeful, more humane, and more empowering educational ecosystem, and it begins with a challenge for us to better understand the landscape. That is the goal of this first article, to offer a simple framework for beginning to recognize and understand the role of data, analytics and (in some cases) AI in education, now and in the emerging future.

In a recent presentation, I guided a group of higher education colleagues through five categories (I prefer to use the metaphor of “buckets” for some reason) for thinking about the role of data in modern higher education. Describing those five will give you some ways to make sense of what might otherwise look like an overwhelming pile of numbers, metrics, and statistics; but there is most certainly method and strategy in the numeric and data-immersed madness of our higher education age.

College Search and Connection Data

These data relate to the connection and relationship between higher education institutions and prospective learners. However, because I am thinking of this as a two-way connection, this might also be the data that learners review, collect, consume, and share about institutions. On the prospective student side, this might include data points like leads (people who have inquired or expressed interest), qualified leads (leads that have been vetted to some extent…people use this terms a bit differently from one context to the next), applications, completed applications, deposits, enrolled, and ultimately starts (How many and which people actually enrolled and started their first course?).

These data also include a collection of data points related to cost. These might include return on investment metrics for marketing and recruitment strategies; along with common online marketing measures like site traffic, source of that traffic, returning visitors to the site, cost per click and/or impression for online ads, conversion from visitor to qualified lead from various sources and marketing tactics.

However, since I also include University data in this bucket, at least to the extent that it plays a role in what school(s) a prospective student chooses (or rejects), we can think of data like location, price, features, graduation rate, demographics, selection of majors and programs, ratings and rankings, reviews (not numeric, but reviews are often turned into numeric rankings and ratings), and much more.

This is a simple introduction to the subject, but in my work in higher education and my interaction with various organizations involved with higher education, I can say that there are indeed several hundred relevant data points in this first bucket. There is also a dominant metaphor for thinking about and using these data today, a sales and shopping metaphor. It is not the only metaphor, however, and I see growing evidence of that metaphor changing, a topic that I will explore in a future article in this series where I focus exclusively on a few cautions, opportunities, and considerations about the role and use of college search and connection data.

Student Retention & Success Data

These data relate to features, behaviors, indicators, attributes, and metrics that are attached to whether students persist, stop out, drop out, or graduate. It might include data that allows one to track or identify risk factors like attendance, performance on tests/courses/assessments, financial status, high school or prior GPA and test scores, performance in what have been identified as “gateway courses”, measures of student interest/motivation/engagement, and much more. At the same time, this can include factors associated with retention and student success like faculty responsiveness to student questions; the quality, quantity, timing, tone, and nature of feedback on student work; the scheduling and timing of coursework and how that aligns with student life demands and needs; and again, so much more. If it is data that people use for insights or indicators related to student retention and/or success, then I put it in this bucket.

Given ongoing concerns about the cost of higher education, enrollment challenges for schools, questions about the relevance of the college degree, demands for more evidence about the return on the higher education investment of students and the public (especially where tax dollars contribute to the schools), and concerns about the disruption of higher education from various sources; this is one of the more highlighted areas for data and analytics today. In addition, while arguably in its infancy, the use of predictive analytics and rudimentary forms of what some today are defining as artificial intelligence, student retention and success is an area where many institutions are gaining the most traction around the use of data.

Yet, not everyone is comfortable reducing data about the student experience in college to measures about retention and graduation. IT feels a bit to corporate for some in education. There are still interest, however, in indicators that students are flourishing, engaged, experiencing a sense of belonging, or fully and actively engaged in the community.

In a future article, I will highlight some of the promising practices, but also some of the cautions and important considerations as this area develops.

Student Learning, Progress, and Mastery

While related to the last category, these data are more about what students learn, how well they learn; their motivation; interest; the conditions under which they learn best or worst; learning gaps and strength; data that compares performance across individuals/cohorts/populations; and their progress toward mastery, progress, proficiency, competency, or perhaps a more nuanced and sophisticated approach to a given discipline or subject. I offer the different terminology at the end of that last sentence to recognize that these data embody more than a single philosophy or approach to teaching and learning.

I’ve convinced that data/analytics/AI related to this bucket will be one of the two most significant in transforming or truly disrupting formal education as we know it. At a minimum, it will have a significant impact, even (or especially) if most of the research and applications for these data happen in learning environments outside of the hallowed halls of academia.

As with the other categories, I will dedicate a separate article in this series to data/analytics/AI and student learning. It may well be the most challenging area for some to openly consider, especially as a I begin to get into what will sound like far-fetched science fiction to some readers. I will make what I hope to be a convincing argument that it is neither far-fetched nor science fiction…at least not the type of science fiction that has no grounding in a current or emerging future reality.

Organizational Health

What does it mean to be a healthy and viable higher education institution? Ask any group of school leaders and, regardless of the mission, financial viability will be in the top three or four in the list, often in the top two. Of course, there will also be answers related to being faithful and committed to the distinct mission and core values of a given higher education institution. Put all of those answers in a list, find the data points that are solid indicators for each of those items, and you get a good understanding of what I’m referring to in this category. These data points are increasingly represented in scheduled (weekly, monthly, quarterly, annual, but sometimes real-time) reports and dashboards. Just like we use a dashboard when driving a car, people are increasingly using them to steer organizations, or at least to gauge how things are going, seeking actionable insights for next steps.

Learner Connections With People and Organizations

This is the most undeveloped category in higher education, but I contend that this, along with the category on student learning/mastery will become one of the most important in the future. Austin Kleon wrote a short, informal, and highly influential book about life and work in a digital age. In it, he argued that if you are not online, you don’t exist. He was referring to the means by which we are and our work is discovered, how we connect with other people and organizations, and how we tell meaningful stories and learn from the meaningful stories of others in a digital and connected age. Consider that each data point related to a person is a possible connecting point in a world of analytics and artificial intelligence, and that is already changing how we meet people, network, find jobs, shop, learn, volunteer, collaborate, solve problems, and create our public identity. Right now, most higher education institutions are hardly even thinking about this category of data, not beyond a few outliers, some workforce development efforts, internship programs, experimentation with micro-credentials and portfolios, and some promising 21st century caliber job board and networking technology. There is already a great deal of data in this area, but few have developed a deep understanding or clear vision for what it means for the organization and especially the learners (and graduates). Again, I will dedicate a separate article to unpack this, as I plan to do so with the others.

What Next?

As I’ve tested these five categories with colleagues and at least one group of people, I’m moderately confident that they represent a useful way to categorize the data that has relevance in current higher education conversations as well as ones that I’m confident will become increasingly important in the near future. The purpose of this first article was to introduce the categories and the topic, to build a basic vocabulary or framework. In the next five articles in this series, I will explore each category in greater depth. I have no intention of providing an exhaustive commentary for each of these five categories, but I will highlight the role of the category, offering a couple promising practices, some challenges, as well as suggesting some possibilities for the future. Then I plan to fill out the series with a seventh and final concluding article, summarizing and highlighting some of the insights in the series, and suggesting some next steps.

Solve the Higher Education Debt Problem by Making College Less Necessary

There is a grocery store nearby that I used to frequent. It provides quality products and I valued the brands and selection. Over the years the management or ownership clearly made a decision to establish the store as a premium spot, and one of the main ways that they did this was by significantly increasing the prices. I kept going there as did many others, but we complained about the prices to one another. So, why did we keep going there? When I finally came to my senses, I looked around for other options and found any number of great alternatives with far more competitive prices.

Sometimes it seems like there is a parallel in modern higher education. Consider the countless complaints in the form of articles lamenting the massive rise in tuition over the years, the growing college debt for graduates, as well as graduates failing to find jobs that they deem as a valid justification for such cost and debt. Yet, people keep going. In fact, even as we complain about the costs, we seem to be just as passionate about advocating for even more people going to college.

While some seek a solution to at least part of these concerns by lobbying for tax-funded tuition free college education opportunities or capping the cost of college, I offer an alternative that might be even more effective at addressing the concerns about cost, debt, and employment. What if we stopped going to college, or at least reduced the role of college as the required gateway to countless careers and opportunities? There are many ways to learn a given body of knowledge and skills, and if we are willing to let go of our attachment to college as “the way” and instead recognize it as what it as “a way”, then we open up an entirely new set of possibilities. What if we invested more time and resources in creating a learning ecosystem where college is one of many learning pathways to a growing number of careers?

This removes the debt and has potential to increase access and opportunity to living wages without higher education gatekeepers holding the keys to such work. If college is less necessary and less common for getting the jobs to which many aspire, we might actually make more progress in solving the debt problem than by reform efforts.

I’m not suggesting that we get rid of college, only that we stop looking at it as the only way to accomplish the goal of reach learning goals that lead to gainful employment and the associated access and opportunity. This calls for employers reconsidering the criteria that they put on job postings. It also requires professions focusing more upon verifying that people are qualified for the profession instead of dictating the ways in which one reaches that qualification. This goes back to what I’ve called The Lincoln Test (the title of a book that I’m writing). Lincoln didn’t go to law school but he passed the bar and become a lawyer. Why can’t we do more of that for a larger array of professions? Then we can add countless and various priced learning opportunities that can help people reach that qualification. We can create a competitive marketplace for such learning that ranges from simple self-study to tutors, short courses, online learning environments, computer-aided instruction, experiential learning opportunities, and anything else that we can think up. We just have to be sure not to make the same mistake of pouring tons of public funding into these training opportunities or over-regulating them. That will just drive the price up…just like college. Instead, let the ecosystem grow a wonderful and somewhat wild garden of learning.

This doesn’t require massive or overnight changes in the short-term. It can begin with individual professions as well as employers being open to alternative pathways…eventually not even calling them alternatives. They are just pathways. As more professions do this, ecosystem will grow and we may soon find that there are far fewer concerns about the cost or debt associated with college, because colleges will no longer serve as trolls under the bridge to career and related opportunities.

Some say that such a suggestion is an attack on higher education. While this would probably result in a decline in college enrollment, I contend that in the midterm, it might actually help colleges reconnect with the greatest value that they can offer society.

How AI Will Transform Education & Why Now is the Time to Start Preparing for It

Stay with me. I want to offer a few considerations about what I consider the inevitable transformation of education by artificial intelligence, but to do so, I’m going to first invite you into my childhood and early college years for a moment. It might not seem related to AI, but if you bear with me, I promise to offer you a few important and incredibly relevant considerations, as well as an important challenge and invitation.

Mr. Bently was an extraordinary teacher. Life wasn’t always easy in my elementary school years. Many others faced far greater challenges to be sure, but suffice it to say that when I went to school, it was not easy to set aside worries and concerns from outside of school enough to get the most out of what happened in most of my classes. Nonetheless, when I walked up to the room to enter Mr. Bentley’s class, he consistently greeted me and every other student at the door. As he wished us each a good morning, he also paid attention to the little things and deliberately said something that made each of us keenly aware that he cared about us and noticed us.

During class, he applied that same care and attention to each lesson. He seemed to notice small shifts in facial expressions that hinted at frustration, fear, or confusion. Not that he always came to the rescue, but he had a way of showing that he noticed while encouraging us to persist with a challenging problem. He listened to what we said, noticed what we didn’t say, keenly observed our nonverbal messages, and clearly worked to help cultivate a positive learning environment, most of the time without giving hardly any direct instructions.

I remember many caring teachers, but Mr. Bently stood apart from the rest as I think about teachers who listened, observed, and adjusted accordingly with such care and skill. How much did he care and pay attention? Twelve years after being his student, I was going into the summer after my freshman year of college. I had a summer job, but on a whim, while driving by an insurance company in my home town, I decided to stop in and ask if they had any summer openings. The next thing that I knew, I was in a beautiful office, speaking with the branch manager. Impressed with my initiative, he offered me a job on the spot, serving in their call center, working lists of prospective customers. Using a simple script, I spent evenings calling name after name, introducing myself by name and asking if they had interest in reviewing their insurance coverage. If so, my job was to schedule an appointment between that person and an available agent.

I didn’t enjoy the job. Making the calls and talking to people was enjoyable enough, but the list that I used included some problems. First, some of the people that I called were already customers, and they were often offended that I didn’t know as much. The worst calls were when I would ask for a given person, only to find out that this person passed away in the recent past. Imagine calling a person, asking for the spouse, only to discover that the spouse died in a car accident the day before, resulting in audible sobbing as you struggled for what to say. Out of a list of a couple thousand names, I remain amazed at how many deceased people were included on that list (Note that this was in the early 1990s, long before current methods for such work). One day, working through a new list, I reached the “B”s and found myself calling a number and asking for a “Mr. Bently.” A woman answered the phone and as I said, “Hi, my name is Bernard Bull…” the woman stopped me. “Bernard? Bernard Bull?” I confirmed. “Oh, my husband will be so delighted to speak with you.” This was the wife of my 4th grade teacher, Mr. Bently!

Did you catch that? Twelve years after being in this class, the wife of Mr. Bently recognized my name in an instant, and when I spoke to him, his memory of even the smallest details about me were fully intact. In an instant, it was like I was walking into Mr. Bently’s class all over again, experiencing that incredible felling of care, recognition, and belonging. I felt noticed and important to someone else, and it felt amazing. I don’t think that I went on with the rest of my script.

It would almost sound like blasphemy to think that artificial intelligence could ever replace a Mr. Bently. A good part of me continues to believe that no non-human system will ever serve as a substitute for the incredible and formative experience of being noticed and cared for by a teacher like Mr. Bently. Beyond that, he was a master of listening and observation, and he used that to help me and countless others learn. A great teacher like him is truly gifted at the art (and science?) of noticing nuances in learners and responding accordingly.

Perhaps that sort of a deeply human and meaningful interaction is only possible between two humans. Yet, we are on the verge of an age when artificial intelligence is inching, or sometimes leaping, toward noticing countless nuances. Consider what non-human systems can extract from a single still image of a person, breaking down facial expressions into the various combinations of muscles and movements in the face, even noticing the development of some muscles over others, potentially hinting at patterns of expression and emotion over time. These systems are emerging that promise to detect lies, fear and anxiety, interest, confusion, and more. Imagine a system that demonstrates the same capacity to notice nuances in our posture, tone of voice, choice of words (spoken and written), online habits and actions over time, in person action and habits over time, or reaction to various stimuli and contexts, and our response to any other sensory experience in the world. We are already partly there. Consider this enhanced by the ability to interpret what is happening in a person on the basis on heart rate, brain wave, and eye dilation, blood flow to various parts of the body, and other involuntary physical responses; comparing all of these “data points” to a massive database in order to diagnose and adapt.

Does this seem far-fetched? Scan the news and you will find articles about AI detecting skin cancer better than doctors, AI that can determine sexual orientation through still images, experiments with AI lie-detectors for border control, and AI behavioral systems being used in schools within China? We are talking about technology that is already getting heavy use in finance, healthcare, political strategy, security, social media, and yes, education. We might not have systems in education that are as advanced as I mentioned in the last paragraph, but we are well on our way.

Consider what happens as we reach a time when such technological observation is combined with the most current research on knowledge and skill acquisition.  An artificially intelligent cyber-tutor will constantly read, analyze, and adapt learning experiences to maximize learner interest and progress. As these systems advance, they will far exceed the capacity of any human to facilitate learning for large numbers of learners, even across time and place. This is the future of adaptive learning, personalized learning, as well as individualized instruction. It is, I contend, inevitable and irreversible.

Will these systems greet students at the door? Will there even be a door or a classroom? That is yet to be seen. Will they fill the deeply human need to be noticed and cared for by another human? Even if they can, I personally hope that we count the cost before going that direction. My life today richer because Mr. Bently noticed and cared, and I’m not ready to sacrifice that at the altar of artificial intelligence. At the same time, there is incredible promise and possibility with such technology, and I’m not ready to sacrifice that on the other altar of nostalgia and sentimentality. Rather, I like to think that we can join in co-creating a future of education where the best of these two worlds come together, creating deeply human and caring communities that are transformed and enhanced by carefully considered artificial intelligence systems.

In addition, there are many learning needs throughout life that are already less high-touch and we are fine with that. We turn to online video tutorials to learn a new skill, read books and online guides, opt for largely impersonal training, use educational apps, and blend our learning throughout life with a mix of learning environments and formats. Some are human-driven. Others are not. As such, those in the latter category as well as those areas where the human-driven learning is falling short are both prime candidates for disruption, or at least significant experimentation as we explore the possibilities, affordances, and limitations of artificial intelligence in education.

There is much that we don’t know about the future of education. There are countless trends and innovations that will come and go. Artificial intelligence is not one of them. It is here to stay. It will continue to grow. It will find its way into an increasing number of contexts, eventually transforming many of them. The question is whether we are going to do the good and important work of helping to shape that transformation in positive ways, or whether we will simply let AI take the lead through lazy thinking, naivety, technological fatalism, or something else.  Getting informed and involved in the conversation now is your chance to be a co-creator of that future. Now is the time for quick, low risk experimentation, careful consideration, wise thinking, and wide-spread discussion. I offer this article as one way to help spark that conversation.