Learning Is a Struggle AI Must Not Skip (Non-NotebookLM Version)

Welcome to the Cultural Context of Knowledge podcast. I am your host, Donald Easton-Brooks. This podcast invites learners to pause and reflect on how their own cultural experience shape the way they learn, interpret information, and define what counts as knowledge. Each episode will focus on how cultural context impact the way in which we understand the world around us.

Whether it's the classroom, work, environment, or relationships. In this episode of the cultural context of knowledge, I present the writings used to feed NotebookLM. In the prior episode, the AI program did a good job of interpreting the notes. Yet this episode offers a different, more humanistic take on learning.

As a struggle AI must not miss, including examples for both educators and learners. Let's get started. The argument presented in this episode is that genuine learning is fundamentally a struggle, moving from an unclear state of knowledge to one of understanding and equilibrium. This struggle is sequential.

Aligning with processes like Bloom's Taxonomy, which require building knowledge from basic definitions before moving to complex abstract thinking. The critical danger of incorporating AI into learning. Is that users often bypass these foundational steps, attempting to start at the highest level of inquiry without the necessary conceptual understanding of the task.

Using tools like chat, GPT, or Gemini without a clear objective or a basic frame of reference results in inefficient processes, irrelevant or generalized output. And an increased risk of errors because the user cannot effectively guide the AI's predictive models. Therefore, the text cautions that Genuine learning requires respecting this sequential process, ensuring AI enhances rather than replaces the essential foundation of knowledge.

Again, at its core, learning is a struggle. That statement is not meant to romanticize frustration or normalize suffering. It is meant to name what is actually happening when learning is real, the learner moves from what is unclear, unfamiliar, or unstable, toward clearer understanding. The struggle can be simple or complex.

A simple struggle occurs when the task is doable, but new to you. You may have seen it before, but you have never done it yourself. A complex struggle arises when you lack the frame of reference. When the task requires prior concepts, language, or experience, you have not yet built. The difference is not intelligence.

The difference is what you have to work with as a learner. This matters for educators because struggle is often misread. We sometimes treat struggle as evidence that the learner is not ready when it is often evidence that the learner's actively constructing readiness. Learning is not simply the receipt of information, it is the reorganization of understanding.

So the practical question is not how do we remove struggle. Instead, the practical question is. How do we scaffold struggle so that the learner stays engaged long enough for understanding the form? Let's take a concrete example that illustrates how learning works in sequence learning to write letters.

As a kindergartner. Imagine the task of writing the letter a. Here the child is asked to draw a diagonal line from a center point, pulling the pencil slightly to the left, depending on the child's fine motor skills and understanding of the movement. That first line may be possible but not automatic. The child may have drawn circles and lines before, even if it was random chicken scratch, but may never have been asked to draw with precision and intention.

So the child draws the line once, then repeats it. Rehearsal repetition is not busy work. It is the process by which a movement becomes controllable. Next, the child is asked to draw that, the diagonal line from the top center point in the opposite direction. Again, the same ability, but with increased complexity.

The child's mind and hand now have to coordinate a mirrored movement, something that feels surprisingly difficult to an inexperienced writer. After repetition, the child is asked to connect the two diagonal lines at the top midpoint, drawing them as symmetrically as possible. Notice what is happening here.

The struggle is both mental and physical. The child. Is learning the concept, shape, directionality, symmetry, and control at the same time. Then the child adds the horizontal line across the middle again with rehearsal after A, the child moves to B. Now working with curves and proportion. The point is not that this is a dramatic struggle, the point is that even simple learning involves struggle, and the level of simplicity varies among learners.

This is why one child picks up writing quickly while another needs more time. The learning sequence is the same, but the learner's readiness within that sequence differs. Now scale up riding a bike, reading algebra. These require foundational skills before the learner can even meaningfully attempt the task.

In those contexts, skipping foundations does not just slow down learning. It can prevent learning. Learning is struggle and a process. One useful way to describe that process is Bloom's taxonomy and the framing used here. The sequence is understood through three stages. The basic level, the compare and contrast level, and the abstract level.

First the basic level. This is where definitions, purposes, and functions are built. It is difficult, often impossible to move beyond this stage if it is incomplete or only partially understood. In other words, foundations are not optional. They are load bearing. Go back to the child writing letters for writing to make sense, the child must understand what a letter is, why letters matter.

And what the goal of the task is. The child needs language and purpose. This is also where motivation enters. Motivation is not a separate soft factor. It is often the fuel that keeps the learner engaged through the struggle. Second, the compare and contrast level. This is where the learner clarifies meaning by differentiating.

The child compares the diagonal lines to make the letter A symmetrical. The child compares A and B. Not only their appearance, but also their sound. Both are letters, yet they function differently. Comparison is not just an academic strategy. It is a cognitive mechanism for sharpening understanding. Third, the abstract level.

This is where learning becomes more creative. Complex and generative letters become words. Words become sentences. Sentences become style and voice. At this level, the learner is not simply reproducing knowledge. They are using knowledge to construct something new. There are critiques of bloom, including arguments that learning is not linear.

That critique can be fair if we confuse linear with sequential. Learning does not have to be a single straight line. Learners can loop back. They can move at different speeds. They can approach a concept from multiple angles, but learning is still scaffolded. The sequence matters because foundations shape what is possible at higher levels.

For educators, bloom is not just a planning tool. It is a diagnostic tool. When learners stall at analysis or creation, the problem may not be effort. The problem may be that the basic level definitions, functions purposes, was never fully built. Now we arrive at the central question for educators and learners.

Where does ai. And specifically tools like Chat, GPT, and Gemini fit into this learning process. AI can be valuable. It can accelerate access to explanations, it can generate examples, it can provide feedback loops, it can help learners rehearse, compare, and clarify. It can reduce the time it takes to find resources.

It can support writing, planning, and revision. But the key risk is that people often start AI at the third level of blooms, the abstract level, without building the first and second levels. They ask for high level products, polished answers, finished essays, lesson plans, complex solutions before they have a conceptual understanding of the problem.

Yes. In some cases, people can learn backward, beginning with a complex product and working backward to the foundations, but without conceptual understanding. The backward approach often lengthens the learning process rather than shortening it. Why? 'cause the learner lacks criteria without criteria. The learner ends up refining prompts through trial and error, uncertain about what to ask, what to trust, and what to revise.

This is where it is essential to understand what AI is doing in this framing. Systems like Chat, GPT, and Gemini function as predictive models. They learn patterns from very large data sets and generate responses that are statistically likely given the prompt. That can be powerful, but it also means the quality of the output depends heavily on the user's ability to guide the tool.

If the learner does not understand the task, they cannot guide the tool effectively and cannot reliably detect errors. So the failure mode is predictable, irrelevant, or overly general output. Inaccurate claims that sound confident, and a learning process that appears efficient on the surface, but is unstable beneath the surface for educator.

The implication is not avoid ai. The implication says design AI use to honor the learning sequence. Use AI to strengthen the basic and compare and contrast levels before asking it to support the abstract level. Here are examples of sequence respecting prompts educators can teach learners to use. Define these key terms in plain language, then give one example and one non example.

Ask me five questions to check whether I understand the basics. Before we move on, show me two common misconceptions and how to test for them. Generate a short practice set that starts simple and increases in complexity. Gimme a checklist of what I should know before attempting this task. In this approach, AI serves as a scaffold.

It supports the struggle. It does not erase it. Now we connect the learning process and AI use to the broader cultural context of knowledge. In the hard sciences, physics, chemistry, biology knowledge is often grounded in discipline based laws that behave like absolutes under consistent conditions. An example here is gravity.

If you drop an object without restrictions, it will fall. The reliability of such principles allows for relatively stable predictions. In social life and social science, knowledge behaves differently. We can expect human responses, people will react, but we cannot expect the same response with the same precision across time, context, tone, identity, power, and experience.Human responses are lawful in the sense that they occur. They're not absolute in the sense that they repeat identically, and here is the crucial point. What people know and how they interpret it is deeply shaped by cultural and historical experience. Simply all human experience has context. There is always a history.

This means that knowledge is not simply information. Knowledge is information interpreted through lived experience, shaped by cultural narratives and influenced by power, especially whose interpretation is treated as standard or true. In this approach, master narratives or grand narratives can dominate a system's shared sense of reality.

While many narratives, the lived truths of non-dominant communities are ignored or suppressed. When that happens, the system's knowledge base becomes biased. It categorizes communities through dichotomies order versus disorder, civilized versus non-civil, and then treats those categories as neutral. The result is systematic error, and in many cases, dehumanization.

So what does this have to do with AI and learning? It has everything to do with it. AI systems are trained on human language at scale. That language includes dominant narratives, institutional assumptions, and the patterns of acceptable knowledge as represented in the data. If educators and learners treat AI output as neutral, they risk importing master narratives into classrooms and calling it objectivity.

In culturally complex domains, educational policy, discipline, curriculum, identity, community history, AI can produce responses that sound coherent while reproducing bias. And if the learner has skipped foundational understanding, they are less equipped to notice what is missing, whose voice is absent, or which assumptions are being smuggled in.

This is why the cultural context of knowledge such not an add-on to AI literacy, it is central to it. A culturally responsible approach to AI enhanced learning should require at least three commitments. First, contextualizing knowledge by teaching learners to ask, what is the historical and cultural setting of this claim?

What experiences shape, how different communities interpret this issue? Second Pluralizing knowledge by teaching learners to look for many narratives, community perspectives, lived accounts, and historically marginalized interpretations, especially when the topic affects real lives and real opportunity structures.

Third, validation through negotiation and evidence means that shared knowledge is built through interaction and negotiation testing. Confirming, challenging and revising understanding with others in educational terms, discussion, critique, sources, lived experience. An empirical checking all matter. So here's the bottom line for today's episode.

Ai, C-H-A-T-G-P-T and Gemini are high value technologies. They can accelerate learning when used as scaffolds, helping learners build definitions, practice skills, compare concepts, and gradually move toward abstraction. But they can undermine learning when they become a mechanism for skipping the sequence.

Jumping to the product while bypassing the struggle that builds understanding and beyond sequence. Educators and learners must recognize that knowledge is culturally situated. What counts as good knowledge is not only about correctness, it is also about context, perspective, power, and whose narratives are included when AI is used without cultural responsibility.

It can reproduce master narratives and present them as a neutral truth. If you are an educator, the instructional move is straightforward. Design AI use that reinforces foundational learning and requires cultural context checks, definitions, evidence, multiple perspectives, and many narratives alongside dominant accounts.

If you are a learner, the strategy is equally clear. Use AI to strengthen your foundation. First, define the terms, build the frame, practice, compare, then and only then use AI to support higher level creation and synthesis. AI should compress time, not compress development. Thank you for listening to the Cultural Context of Knowledge.

This transcript was generated from the episode recording and may contain small transcription errors.