Why Has Education Been So Slow to Digitally Transform?

Why has education struggled to achieve the kind of digital transformation seen in other industries, and what would it take for K-12 education to move forward in a more unified and sustainable way?

After more than a decade working in higher education, I came to believe there were several reasons education had not experienced the same level of digital transformation as other sectors. Some of those reasons were financial, while others involved workload, leadership, planning, and the difficulty of changing systems that appeared to work “well enough.”

Money drives technology decisions

The first reality was simple: money influenced nearly every decision involving technology implementation.

Institutions could identify useful tools, but purchasing the technology was only the beginning. They also had to account for licensing, training, integration, maintenance, accessibility, security, technical support, and eventual replacement.

Educational institutions often operated with limited budgets and competing priorities. Even when a technology had clear potential, leaders still had to decide whether the benefit justified the cost and whether the institution had enough staff to support it properly.

As a result, transformation could become a series of isolated purchases rather than a coordinated strategy.

Faculty and staff were already overwhelmed

Another major challenge was workload.

Many faculty and staff members already had an abundance of responsibilities related to teaching, grading, advising, course preparation, student communication, and institutional service. Asking them to learn another technology, especially when their existing process “worked well enough,” could feel like the proverbial straw that broke the camel’s back.

I witnessed this firsthand during a training session involving Microsoft Teams and classroom technology. Our lead information technology staff member invited employees who regularly taught or presented to students to learn how to use Teams alongside motion-sensing cameras, microphones, and virtual desktops.

The classroom setup was designed so that the standard Teams camera could be disabled and replaced by a camera feed developed by our information technology staff. The motion-sensing camera could follow the speaker, while different applications remained available across virtual desktops.

The goal was to make the experience smoother for students and easier for instructors. The training itself was excellent, and I left feeling confident about our use of the technology.

Then reality arrived.

When good training met the classroom

Only a short time after the training, I entered a classroom to speak with students about the Student Support Services program and quickly realized there were technical problems.

The motion-sensing camera was not sensing. The microphone was not capturing audio. Much of the training we had recently received had already been disregarded or forgotten.

As a result, I was relegated to sitting behind a computer and speaking into a quickly arranged microphone. After my presentation, the professor had to teach in the same uncomfortable manner.

The technology was available. The training had occurred. The intended process had been explained.

Yet the actual classroom experience did not match the plan.

This example demonstrated that digital transformation is not achieved by purchasing equipment or holding one training session. The technology must become part of a dependable process that users understand, practice, and receive support for over time.

A “clumsy” transition

In another class, I asked a professor whether he had received a Microsoft Teams message containing a link I had sent. He responded with a mixture of a shrug and a shake of the head.

I opened Teams on his computer to paste the link into the chat and noticed that he had several unanswered messages and emails that had not been checked.

Part of his expanded responsibility involved keeping up with these digital communication channels. However, when those responsibilities were added to teaching, grading, managing a classroom, and adapting course content, the technology became another source of pressure.

When I asked what he thought of the transition to online teaching, he gave one word:

“Clumsy.”

That description felt appropriate.

The problem was not that faculty members were unwilling or incapable. In many cases, the transition had added new expectations without removing older ones or providing enough time for people to develop confidence.

Why education had not transformed

I could only speak directly about the institution where I worked, but I felt confident that similar experiences were occurring elsewhere.

Several barriers appeared repeatedly:

  • Limited funding
  • Heavy faculty and staff workloads
  • Insufficient training and continued support
  • A lack of clear leadership
  • Poorly defined goals
  • Uncertainty about which tools would work together
  • Resistance to changing processes that appeared functional
  • Rapid technological change that made long-term planning difficult

Educational institutions were often expected to modernize while continuing every existing operation without interruption. That created a situation in which transformation became an additional project rather than a rethinking of how the institution functioned.

What would true digital transformation require?

Higher education institutions have been pressured to adapt to cultural and social changes while responding to the rapid development of digital technologies. The speed of that change can create a kind of institutional freeze.

Leaders may recognize that change is necessary but still struggle to determine which technologies to adopt, how much to invest, and what the next development will make obsolete.

The result is often a familiar argument:

“What we have has worked so far.”

That statement may reduce immediate risk, but it can also postpone necessary change.

The conditions for generating knowledge, processing information, and communicating have changed significantly. Education exists to help people understand the world and prepare for participation in it. If educational systems remain disconnected from the technologies shaping society, they risk preparing students for conditions that no longer exist.

Start with purpose, not products

True digital transformation should not begin with purchasing software or hardware. It should begin with the needs of students, faculty, staff, and the institution.

Leaders should first determine:

  • What problem are we trying to solve?
  • What should the student experience look like?
  • Which processes are unnecessarily complicated?
  • What work can technology improve or eliminate?
  • What training and support will users require?
  • How will success be measured?
  • How will the systems integrate?
  • What should be discontinued when the new process begins?

Without clear answers, institutions may purchase impressive tools that create more work without improving learning or performance.

Digital transformation requires changes in culture, workflow, expectations, and decision-making, not merely a collection of new technologies.

Should transformation be mandated?

At the time, I believed that meaningful digital transformation would likely require stronger direction and funding from state or federal authorities.

Local school districts, independent school districts, and higher education institutions could debate funding for years. Wealthier institutions would move ahead, while those with fewer resources would fall further behind.

A coordinated effort could establish common expectations for infrastructure, internet access, accessibility, cybersecurity, student data protection, faculty development, and technical support. Full or substantial public funding would also reduce the likelihood that digital opportunity depended almost entirely on local wealth.

However, a mandate alone would not guarantee success. Requirements would need to be accompanied by funding, realistic timelines, professional development, and enough flexibility for institutions to address local needs.

Otherwise, a top-down requirement could become another unfunded responsibility placed on already overwhelmed employees.

Ripping off the bandage

There would inevitably be discomfort.

Faculty, staff, students, and leaders would experience frustration. Some people would long for the “good old days.” New systems would fail. Training would need to be repeated. Processes would have to be revised.

Still, we had become too heavily invested in technology to retreat from it.

The more responsible choice was not to pretend that technology could be avoided. It was to manage the transition deliberately, provide people with support, and move forward with a shared direction.

In that sense, digital transformation resembled ripping off a bandage. The initial change could be uncomfortable, but delaying it indefinitely would not solve the underlying problem.

Final thought

Education had not digitally transformed at the same pace as other sectors because it was attempting to modernize within systems constrained by limited funding, heavy workloads, fragmented leadership, uneven infrastructure, and unclear goals.

The answer was not simply more technology.

It required purposeful leadership, coordinated investment, sustained training, integrated systems, measurable outcomes, and a willingness to reconsider processes that no longer served students or employees well.

Simple enough, right? 😊

What Made AlphaGo’s Victory Different from Watson’s?

What was different about DeepMind’s AlphaGo defeating a world-class Go player and IBM Watson defeating champions on Jeopardy!?

Both systems represented major advances in artificial intelligence, but they demonstrated different kinds of machine intelligence. AlphaGo was designed to make strategic decisions within a highly complex game, while Watson was designed to interpret language, search large bodies of information, and determine the most likely correct response.

AlphaGo and strategic decision-making

DeepMind developed AlphaGo to compete against professional Go players, eventually becoming the first computer system to defeat a world champion in the game.

Go presents a difficult challenge because the number of possible board positions and moves is enormous. A system cannot simply evaluate every possible outcome through brute force. AlphaGo instead relied on neural networks, extensive training, machine learning, and Monte Carlo tree search to evaluate possible moves and improve its decision-making.

Its neural networks learned from examples of human play and then strengthened their abilities through repeated games and self-play. Rather than following a fixed set of human-written instructions for every situation, the system learned which positions and moves were most likely to lead to victory.

Bory (2019) describes AlphaGo as a system without a physical body or traditional mechanical form. It exists primarily as a network of neural networks, which fits the broader digital metaphor of intelligence and data existing throughout the cloud rather than inside one visible machine.

What made AlphaGo especially significant was its ability to develop strategies that surprised even expert players. It did not simply imitate human decisions. In some cases, it identified moves that initially appeared unusual or incorrect but later proved strategically valuable.

Watson and natural language processing

IBM Watson approached intelligence differently.

Watson was designed as a question-and-answer system capable of competing on Jeopardy!. To succeed, it had to interpret clues that included wordplay, indirect references, multiple meanings, cultural knowledge, and unusual phrasing. It then needed to search its available information, compare possible answers, estimate confidence, and respond quickly.

Watson relied heavily on natural language processing, which involves programming computers to interpret and work with written or spoken language. In this context, “understanding” does not mean that Watson comprehended language in exactly the same way a person does. It means the system could process a clue well enough to solve the problem and produce an appropriate response.

Mehta and Devarakonda describe natural language processing as the use of computational methods to work with language in ways that support practical tasks. Search engines, translation systems, predictive text, and Watson’s performance on Jeopardy! all demonstrate this capability.

Watson’s challenge was therefore not primarily long-term strategy. It was language interpretation, information retrieval, probability, and response selection.

Two different forms of artificial intelligence

The distinction can be summarized this way:

  • AlphaGo learned how to make strategic decisions in a complex environment with an enormous number of possible actions.
  • Watson learned how to interpret language, search information, compare possible answers, and respond with confidence.

AlphaGo demonstrated the power of machine learning and pattern recognition within strategic decision-making. Watson demonstrated the ability of artificial intelligence to work with human language and large collections of knowledge.

Both systems defeated highly skilled people, but they did so through very different approaches.

Where else can pattern analysis contribute?

Artificial intelligence and machine learning already contribute to security, healthcare, finance, transportation, and other major industries. Their pattern-analysis capabilities may become especially valuable wherever large amounts of information must be interpreted quickly.

AI can recognize irregular activity in security logs, detect patterns in medical images, identify financial fraud, predict equipment failures, recommend resources, and uncover relationships that a person may not notice.

The larger question is whether these systems will replace human jobs or transform them.

More simply: When will an AI-powered system do my job, the jobs my parents performed, or the jobs for which today’s children are being prepared?

According to one automation assessment I reviewed, approximately 39 percent of my job was considered susceptible to automation.

But hey, looking on the sunny side, that leaves 61 percent for Mr. Future PhD.

The same assessment placed project managers, insurance agents, real estate agents, engineers, and information technology managers among occupations with a lower risk of full automation. That makes sense because these positions require judgment, communication, planning, relationship-building, and responses to situations that are not always predictable.

Jobs built around repetitive tasks and limited human interaction appear more vulnerable. If a process follows the same rules each time and can be measured consistently, it may be easier to automate.

Replacement or assistance?

The more hopeful possibility is that artificial intelligence will enhance many jobs rather than eliminate them.

Mike Thomas noted that the Bureau of Labor Statistics projected growth in several occupations expected to be influenced by artificial intelligence, including accountants, forensic scientists, geological technicians, technical writers, magnetic resonance imaging operators, dietitians, financial specialists, web developers, loan officers, medical secretaries, and customer service representatives.

If that prediction holds true, these occupations may grow not in spite of artificial intelligence, but partly because of it.

An accountant may use AI to identify unusual transactions. A technical writer may use it to organize information and accelerate drafting. A medical professional may use it to recognize patterns in images or patient data. A web developer may use it to test code, identify errors, or automate repetitive work.

In each case, artificial intelligence changes the nature of the work without necessarily removing the worker.

That distinction matters.

A system may perform part of a job without possessing the judgment, responsibility, creativity, empathy, or contextual understanding required to perform the entire role.

Preparing for work alongside artificial intelligence

The most likely future may not involve humans competing directly against machines. It may involve people learning to work effectively with them.

AlphaGo and Watson demonstrate that machines can outperform experts within carefully defined tasks. They do not demonstrate that machines can independently assume every responsibility connected to those tasks.

The workers who remain valuable will likely be those who can:

  • Interpret AI-generated results
  • Recognize when a system is wrong
  • Apply ethical and contextual judgment
  • Communicate findings to other people
  • Solve problems that fall outside established patterns
  • Use technology to improve their performance

Education should therefore prepare people not only to complete current job duties, but also to adapt as portions of those duties become automated.

Future Lance, this is why you get a good education, kid, and future-proof your money from robots.

That is future me talking to the past, kid-version of me.

Final thought

AlphaGo and Watson were important because they demonstrated two different ways artificial intelligence can outperform people.

AlphaGo used learning and pattern analysis to make complex strategic decisions. Watson used natural language processing and large-scale information retrieval to answer questions expressed through human language.

Their victories did not prove that machines had become generally intelligent. They showed that systems designed for specific tasks could perform those tasks at an extraordinary level.

The future of work may follow the same pattern. Artificial intelligence will increasingly handle portions of jobs that involve prediction, classification, retrieval, repetition, and pattern recognition.

The question is not simply whether a robot will take a job.

The better question is which parts of that job will be automated, which parts will remain distinctly human, and whether workers will be prepared to operate in the space between the two.

Designing Learning Technologies That Empower, Explain, and Earn Trust

Technology and the learning environments we create teach us to experience the world in new ways while sharing the resources that prepare us for future learning. Whether we are designing a game-based activity, developing an artificial intelligence system, or building an online learning environment, the choices we make influence how people think, behave, participate, and understand the world around them.

For that reason, the central question is not simply whether a technology works. We must also ask what it teaches, whose experiences it represents, how it affects the learner, and whether the people using it can understand and trust the decisions it produces.

Designing inclusive and empowering learning environments

Diversity, equity, and inclusion are concerned with the context, consequences, and culture represented within each learning environment. In gamification and game-based learning, many of the characteristics learners experience are representations of reality. The stories, rewards, characters, challenges, and rules built into a game communicate ideas about what is valued, who belongs, and how success should be achieved.

When we design games and other learning experiences, it is important to remain flexibly adaptive, work together, and not be afraid to make mistakes. Design is rarely a perfect, one-time process. It requires testing, reflection, feedback, and revision. The work we do in designing, learning, and seeking to create a better world for all can feel daunting when it is viewed as massive systemic change. Yet, when we focus on what each of us can accomplish through our own “little bit at a time,” the process becomes more attainable, and we can begin to shape educational mountains.

The story of Ota Benga, a Congolese man who was captured and displayed in a zoo exhibit, emphasizes why learning environments must be empowering not only in terms of learning, but also in terms of making the world a better place. Educational environments are never entirely neutral. They evolve through symbols, words, images, stories, and interactions that create multimodal spaces for meaning and knowledge.

These environments can be used for the betterment of the learner and society. They can also reinforce behaviors and assumptions that are not conducive to creating a better world. A game may encourage cooperation, persistence, and empathy, or it may normalize violence, stereotyping, exclusion, and domination. The instructional designer therefore has a responsibility to consider not only what learners are expected to know, but also what the experience may be teaching them indirectly.

Research, perception, and learner differences

At the time of this writing, I was conducting a longitudinal study at my institution examining students’ perceptions of their technological competence. The discussion of learning environments reminded me of several important considerations in that research.

First, a survey should be socially validated so that the questions are understood as intended by the people responding to them. A researcher may believe a question is clear, yet students with different backgrounds, experiences, and levels of technological familiarity may interpret it differently.

Second, students’ perceptions of their technological competence will vary based on their backgrounds. Access to devices, prior educational experiences, family expectations, employment, age, culture, and exposure to technology can all shape how capable a student feels.

Third, researchers must remain open to what the data reveal. Correlation and causation are different measures, and it is easy to interpret findings in a way that confirms what we already expect to see.

These considerations also apply to instructional design. We cannot assume that all learners enter an environment with the same knowledge, confidence, motivation, or access. A design that empowers one learner may frustrate or exclude another.

The zone of proximal development provides a useful way to think about this challenge. Instruction should place learners beyond what they can comfortably accomplish alone, but not so far beyond their current ability that the task becomes overwhelming. The teacher or instructional designer provides guidance, scaffolding, feedback, and resources that help the learner progress toward greater independence.

This raises two important questions:

  1. How can instructional designers use the zone of proximal development when creating objectives and lessons so that students are empowered to learn while the teacher acts as a facilitator?
  2. How can those same experiences encourage affinity groups or communities of practice in which learners with shared interests can grow, collaborate, and build knowledge together?

These questions become even more important as intelligent technologies take a larger role in educational environments.

Artificial intelligence and the black-box problem

One of the major weaknesses of artificial intelligence and machine learning systems is that they often cannot clearly explain why they reached a particular decision.

An artificial intelligence system might recommend a medication for a cancer treatment, identify a student as being at risk, flag a person through facial recognition, or recommend a particular course of action. The system may produce an answer that appears highly accurate, but it may not be able to provide an understandable explanation of how it arrived at that conclusion.

Most people would be reluctant to trust a physician who prescribed a treatment but could not explain the basis for the decision. Why, then, should we automatically trust an artificial intelligence system that operates in the same manner?

When we use the word think, it should not be confused with what an intelligent system is capable of doing. Thinking involves consciousness, self-awareness, intention, and reflection. To the best of my knowledge, we have not created a system that truly thinks in the human sense.

When an artificial intelligence system generates an output, response, or recommendation, its neural networks and algorithms process data to determine what the system calculates to be the most likely or useful answer.

Clear as mud?

That vague and generalized explanation is part of the problem. We often do not fully understand how the system’s internal processes produced a particular output. We have effectively created highly sophisticated systems that can generate a great deal of good, or harm, depending on how they are used, yet we cannot always explain their inner workings.

Hence, the black-box conundrum.

If we cannot explain how something works, how can we expect a system designed by humans to explain itself?

Why explainability matters

This becomes a significant problem because artificial intelligence, machine learning, and other intelligent systems are already part of our world. The toothpaste is out of the tube, and there is no putting it back in, my dear.

Knowing humans, because I are one, and yes, that was intentional, I suspect we became excited about what we were creating without fully considering the implications of our actions. We continued applying Moore’s Law, increasing computing power and capability, and then kicked many of the ethical questions down the road for future generations to address.

I am familiar with my own personality and how I tend to buck up when someone tells me what to do but cannot explain the reasoning behind the decision. “Because I said so” does not work particularly well when applied to complicated, real-world problems.

Yet that is often the same methodology produced by artificial intelligence.

The datasets may be enormous. The algorithms may be impressive. The output may even be correct. But how did the system form the response, and why should I believe it?

Show me the relevant data. Explain why one course of action is more prudent than another. Demonstrate that the system is not reproducing hidden bias or relying on incomplete information. Prove that we are not blindly following a creation we do not fully understand.

Then I may be willing to concede that our species has not opened Pandora’s box.

Explainability is important not only for data scientists and physicians. It matters to police officers using facial-recognition systems, teachers using analytics in their classrooms, students trying to understand their social-media feeds, employees affected by automated hiring systems, and passengers sitting in the back seat of a self-driving car.

People have always had a tendency to over-trust technology. The more a system is described as intelligent, the more likely users may be to assume that it is smarter, more accurate, and more objective than they are.

That assumption is dangerous.

The connection between learning design and artificial intelligence

Gamification, learning analytics, adaptive systems, and artificial intelligence may appear to be separate topics, but they share a common concern: the design of environments that influence human behavior.

A gamified lesson may determine what actions are rewarded. An adaptive system may decide what content a learner sees next. A learning analytics platform may classify a student as at risk. An artificial intelligence system may recommend a decision that affects someone’s health, education, employment, or freedom.

In each case, the technology reflects human choices.

Someone selected the data. Someone established the rules. Someone determined the goal. Someone decided what counted as success.

The responsibility of the designer is therefore not limited to making the system function. The designer must also consider whether the environment is inclusive, whether the outcomes are fair, whether the learner is empowered, and whether the decisions can be explained.

Technology should support human development rather than reducing people to data points, categories, or resources. It should encourage curiosity, collaboration, reflection, and agency. It should help learners move beyond what they can do alone while preserving the human relationships that make learning meaningful.

Building trust through thoughtful design

Trust cannot be demanded simply because a system is advanced. It must be earned through transparency, evidence, accountability, and responsible design.

For learning technologies, this means clearly communicating:

  • What the system is designed to accomplish
  • What data it collects
  • How that data are used
  • What limitations are known
  • How recommendations are generated
  • When human review is available
  • How learners can question or challenge a result

It also means testing systems with diverse users rather than assuming that one design will work equally well for everyone.

The same principle applies to games and simulations. Designers should examine whose experiences are represented, what behaviors are rewarded, what assumptions are embedded in the rules, and what lessons may be communicated beyond the stated objectives.

A learning environment should not merely be efficient or engaging. It should be intentional.

Conclusion

Technology has the ability to transform how we teach, learn, work, and interact with the world. Gamification can increase engagement and persistence. Artificial intelligence can identify patterns and support decisions. Adaptive systems can provide learners with targeted assistance.

However, every technological benefit brings responsibilities.

We must design learning environments that empower rather than exploit. We must recognize that learners enter with different experiences, abilities, and levels of access. We must create opportunities for collaboration and community. We must also demand that intelligent systems become more transparent and explainable, particularly when their decisions affect human lives.

The work may feel overwhelming when viewed as a single effort to transform education and society. But a little bit at a time builds mountains.

Each thoughtful objective, inclusive design choice, transparent algorithm, and opportunity for meaningful human connection becomes part of that mountain.

The goal is not simply to create smarter technology.

It is to create learning environments and intelligent systems that help humans become more capable, reflective, connected, and responsible.