“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.