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.

Can Machine Learning Personalize Education Without Isolating the Learner?

“Personalized learning” seems to be having an identity crisis of sorts. One group defines it one way, while another group of educators, schools, and technology developers uses the term to describe something different.

That lack of agreement appears to be one of the main problems surrounding personalized learning. We do not have a communal definition on which to base a shared set of standards. If we cannot agree on what personalized learning is, how can we expect machine learning to advance the experience we are trying to create?

It begins to sound like a comedy sketch built around a failure to communicate:

“Who’s the first baseman?” asks Costello.

“Who is the first baseman,” Abbott replies.

“That’s what I asked. Who is the first baseman?”

“Who’s on first base!” Abbott exclaims.

“I don’t know. What are you asking me for?” Costello shouts.

It would be quite farcical if it were not also true.

Before discussing how machine learning can make a new form of personalized learning possible, we first need to define what we mean by personalized learning.

  • Do we mean individualized or customized learning paths?
  • Do we mean using technology to support instruction?
  • Do we mean one-on-one learning?
  • Do we mean student-led experiences in which learners choose the topics they are interested in?

Researchers have pointed out that personalized learning varies widely in both definition and implementation. Teachers, schools, districts, states, and technology developers may all use the term, yet they may be describing very different practices. That variability makes it difficult to study personalized learning consistently or determine which approaches reliably improve student outcomes.

This matters because machine learning can only support the goals we give it. If the educational purpose remains unclear, the technology may simply automate the confusion.

Machine learning can certainly support personalized learning. It can analyze patterns in student performance, identify areas of difficulty, recommend resources, adjust the sequence or difficulty of content, and provide feedback based on a learner’s progress. It may also help instructors recognize when a student is struggling before the problem becomes obvious.

The technology, however, is only as useful as the educational goal guiding it.

Instead of tossing money at educational projects because they use the right buzzwords or promise to “revolutionize the school district,” we should first determine what we want to accomplish and then decide whether machine learning is the appropriate way to support that goal.

The question should not be, “How do we use machine learning because it is available?”

The better question is, “What learning problem are we trying to solve, and can machine learning help us solve it responsibly?”

Should Personalized Learning Be Applied to Every Subject?

No.

Life is a communal gathering, and school teaches us more than academic content. It teaches us how to live in society, communicate with others, accept different beliefs, benefit from other people’s input, and listen to opinions with which we may not agree.

If we create the expectation that students will always receive information specifically tailored to their preferences, how will they respond when they encounter obstacles or when their cupcake frosting is vanilla instead of chocolate?

For certain subjects and situations, personalization makes sense. A student who needs additional support in mathematics may benefit from an adaptive system that identifies specific gaps and provides targeted practice. Specialized programs connected to high-demand careers should be designed to resemble the actual career field as closely as possible. Students may also benefit from options that allow them to pursue interests, work at an appropriate pace, or receive resources in formats that better support their learning.

Other subjects and experiences should remain communal.

Students need the traditional classroom in which one child is chewing on a pencil, another is pretending to read while turning the pages backward, and the teacher is doing her best not to have an in-class breakdown.

That atmosphere is a microcosm of what awaits students once they enter the real world.

They will work with people who think differently, move at different speeds, communicate poorly, interrupt, misunderstand directions, or require patience. Not every project, workplace, relationship, or responsibility will be tailored to their individual preferences.

So, let us prepare students for that reality rather than convince them that life will always be customized around their needs and wants.

Finding the Right Balance

Personalized learning should not mean isolating students inside individual digital pathways.

It should mean using available information to provide the right support while preserving opportunities for collaboration, challenge, discussion, and shared experience.

Machine learning could help create that balance. It might quietly adjust practice activities, identify misconceptions, recommend additional support, or give teachers a clearer view of student progress. The student could still participate in a common course, work with peers, and encounter ideas that were not selected solely because they matched an existing interest.

The teacher should remain central to this process. An algorithm may identify a pattern, but it cannot fully understand the family pressures, emotional concerns, cultural experiences, motivations, or relationships affecting the learner. Machine learning should inform instructional judgment, not replace it.

There is also a danger in allowing the system to become too prescriptive. If a platform constantly recommends what a student is most likely to succeed at, it may unintentionally narrow that student’s opportunities. A learner should not be prevented from attempting something difficult simply because an algorithm predicts a lower probability of success.

Personalization should expand options, not quietly restrict them.

Final Thought

Machine learning makes personalized learning possible by allowing educational systems to respond to patterns in student data at a scale that would be difficult for one instructor to manage alone.

But possibility does not equal purpose.

Before transforming educational culture, we need a clearer definition of personalized learning and a shared understanding of when it is appropriate. Some learning experiences should be individualized. Others should require students to participate in a community, encounter frustration, consider unfamiliar ideas, and learn alongside people who are different from them.

The goal should not be to create an educational experience in which every condition is perfectly tailored to the individual.

The goal should be to provide enough personalization to support the learner without removing the shared challenges that help prepare that learner for life.

How Can Learning Analytics Better Support Community College Students?

What learning analytics would be most useful in a community college, and how could those data help students make more informed decisions throughout their educational journey?

At the institution where I worked, we served as an open-enrollment college that accepted students with a wide range of academic backgrounds and life experiences. Placement measures such as the Texas Success Initiative assessment helped determine where students should begin, but those scores only told part of the story.

A large percentage of the student population was first-generation, nontraditional, or both. Many had never attended college, did not have family members who could guide them through the process, or were balancing school with employment, family responsibilities, and financial pressures.

The challenge was not always that students could not understand the course material. Often, they simply did not know the many small but important details involved in navigating higher education. Registration deadlines, degree requirements, grade point average expectations, financial aid rules, scholarship conditions, syllabus responsibilities, and program prerequisites can become overwhelming when no one has explained how the system works.

For that reason, the most useful learning analytics would be those that help identify risk early while also making the student’s academic pathway more visible.

Early-alert data

Attendance, grades, missing assignments, course participation, and sudden changes in performance could all contribute to an early-alert system.

If a student stops attending class, misses several assignments, or experiences a significant decline in grades, advisors and faculty could intervene before the situation becomes difficult to recover from. The purpose should not be to label the student as unsuccessful. It should be to identify when support may be needed.

These data become especially useful when combined with human judgment. A dashboard may show that a student has missed two classes, but a conversation may reveal transportation problems, work conflicts, childcare issues, confusion about the course, or a misunderstanding of institutional procedures.

Analytics can identify the signal. People still need to understand the cause.

Greater transparency for students

Treca Stark of Prince George’s Community College has noted that community colleges operate with limited financial and human resources while carrying multiple responsibilities related to academic preparation, workforce development, and open access. These constraints make it understandable that institutional leaders would explore how analytics could help staff use their time more effectively.

One of the best uses of analytics would be to provide students with greater transparency throughout their college journey.

In previous roles, I worked with many students who did not understand the requirements of their degree plan, how many credit hours they had completed, when they might graduate, or what prerequisites were required for a program they hoped to enter. This confusion often created frustration for the student and additional work for advisors and support staff.

A well-designed dashboard could make this information visible in one place. It might show:

  • Completed and remaining degree requirements
  • Current grade point average
  • Credits earned and credits still needed
  • Progress toward graduation
  • Financial aid status
  • Registration holds
  • Upcoming deadlines
  • Program prerequisites
  • Recommended next steps

This would not remove the need for advising. It would allow the student and advisor to begin the conversation with the same information.

Course recommendations

Another useful approach would be a recommendation system similar to the model associated with Austin Peay State University. Such a system could examine a student’s academic profile, intended program, completed coursework, and the prior success of students with similar backgrounds.

The system could then recommend courses in which the student is more likely to succeed and that keep the student moving efficiently toward completion.

This could be particularly valuable for students who are unsure which course to take next or who unknowingly register for courses that do not apply to their degree.

However, recommendations should remain advisory rather than deterministic. A student should not be restricted because an algorithm predicts a lower probability of success. The system should inform a decision, not make the decision on the student’s behalf.

Why these analytics matter

The greatest value of learning analytics is not simply that they produce more data. Their value lies in whether they make the educational process easier to understand and support timely action.

For community college students, useful analytics could help answer practical questions:

  • Am I currently on track?
  • What do I need to complete next?
  • Am I at risk of losing financial aid?
  • Which course should I take?
  • How close am I to graduation?
  • Who should I contact when I need help?

When students can see their progress clearly, they are better positioned to take responsibility for it. At the same time, faculty and staff can focus their limited time on students who need direct support.

The burden of responsibility becomes more evenly shared. The institution provides clear, timely information, while the student gains a better opportunity to understand and manage the path ahead.

Final thought

The most useful learning analytics for an open-enrollment community college would combine early-alert indicators, degree-progress information, and carefully designed course recommendations.

These tools would not replace advisors, faculty members, or student support staff. They would strengthen their work by making important information easier to see and act upon.

The central question is not whether a college can collect more data. It is whether that data can help students feel less confused, more informed, and better supported as they move toward completing their education.

What Skills Will Tomorrow’s Workers Need Most?

If you were responsible for preparing tomorrow’s workers for a retraining program, which skills would you prioritize?

The possible answers are extensive: emotional intelligence, creativity, cultural flexibility, technological adaptability, storytelling, design thinking, data analysis, financial literacy, mindfulness, entrepreneurship, and many others. My own five choices come from a mixture of what is needed to become a mature, well-rounded individual and what is required to perform effectively across a wide range of workplace situations.

The five skills I would emphasize are:

  1. Self-awareness and self-assessment
  2. Empathy and active listening
  3. Patience and perseverance
  4. Problem-solving
  5. Data analysis

Self-awareness and self-assessment

The ability to assess yourself honestly means that when you look in the mirror each morning, you tell yourself the truth.

You recognize your strengths and weaknesses, your inhibitions and impulsive moments, and the experiences that helped shape the person you are today. When improvement is needed, and there is always room for improvement, self-awareness allows those truths to become visible so that meaningful changes can be made.

This kind of truth-telling begins at the individual level and then flows outward into how we work with others. Employees who understand themselves are more likely to recognize when they need help, receive feedback without becoming defensive, and take responsibility for their decisions.

Empathy and active listening

The ability to listen attentively to another person is becoming increasingly difficult because of the amount of noise surrounding us.

It is easy to pick up a phone, put in an earbud, and tune other people out. Yet human beings are social creatures. We need others with whom we can exchange ideas and share our thoughts, concerns, fears, and anxieties.

Empathy requires us to consider what another person may be experiencing. That begins with listening closely enough to understand their position rather than simply waiting for our opportunity to respond.

In the workplace, active listening supports collaboration, strengthens trust, reduces misunderstandings, and helps teams recognize concerns before they become larger problems.

Patience and perseverance

“Patience is a virtue” may be an old expression, but it remains just as relevant today, perhaps even more so.

It is easy to pursue a skill, project, or goal and then give up when the work becomes difficult or the effort required feels too great. A workforce that understands the patience needed to persevere through challenges develops a mindset that is steady and less likely to be defeated by obstacles.

Retraining itself requires perseverance. Workers may be asked to learn unfamiliar systems, change established habits, or begin again in areas where they were once confident. The ability to tolerate discomfort and continue moving forward may matter as much as any specific technical skill.

Problem-solving

Problem-solving may seem like an obvious choice, but it deserves to be stated directly.

When you see a problem, do not ignore it.

This skill connects back to self-awareness and self-assessment. Rather than avoiding responsibility or kicking the can down the road for someone else to handle, employees should develop the will, patience, and judgment needed to address the issue.

Effective problem-solving requires more than reacting quickly. It involves defining the problem, gathering information, considering possible causes, evaluating alternatives, and determining whether the solution actually worked.

As technology and workplace demands continue to change, workers will increasingly encounter situations for which no established procedure exists. Their value will depend partly on their ability to reason through uncertainty.

Data analysis

If the workforce is expected to progress, compete, and make responsible decisions in a global market, the ability to interpret data will be essential.

Data alone is not knowledge. It must be examined, placed in context, and translated into useful information. Workers need to recognize patterns, question assumptions, distinguish meaningful evidence from noise, and explain what the findings mean for the organization.

The previous four skills connect directly to this ability.

If you are honest about who you are, you will recognize when you need assistance. If you actively listen to the people whose help you seek, you will benefit from their knowledge. If you remain patient while applying that advice, you will continue to grow. As your ability to solve problems develops, you will become better prepared to analyze data and transform it into a valuable resource: information.

And to think, the entire process began with looking in the mirror.

How I Would Implement the Training Program

The training program should combine subject-matter expertise with a deliberate instructional design process.

One approach would be to bring in a freelancer or external specialist to serve as the subject-matter expert. That expert could work with the hiring team, organizational leaders, and current employees to identify the skills and performance gaps that need to be addressed.

From there, the organization could follow an instructional design process:

  • Analyze workforce needs and current performance.
  • Define measurable learning and performance objectives.
  • Design training activities around real workplace situations.
  • Develop resources, simulations, and opportunities for practice.
  • Implement the program in manageable stages.
  • Evaluate whether employees are applying the skills effectively.

However, the program should not focus only on technical proficiency. It should also examine the human qualities that determine how someone responds to change, pressure, and other people.

Questions should explore areas such as:

  • How has the employee responded to difficult situations?
  • What mistakes have they made, and what changed afterward?
  • How do they react when receiving feedback?
  • How do they treat coworkers whose roles carry less authority?
  • Do they listen before responding?
  • Do they take responsibility for shared spaces and outcomes?
  • Do they contribute to trust or participate in gossip and unnecessary conflict?

These questions provide a fuller picture of the individual and how that person is likely to interact with others.

The purpose is not to divide employees into those who are valuable and those who are not. Instead, the assessment can identify who is ready to model certain behaviors, who may need additional support, and which learning experiences will be most useful for each person.

A strong retraining program should develop both technical competence and human judgment. Technology, job requirements, and organizational structures will continue to change. The workers best prepared for that future will be those who understand themselves, listen to others, persist through difficulty, solve problems, and turn data into responsible action.