For decades, educators have used Bloom’s Taxonomy to describe how humans learn—from simple memorization to critical thinking and creativity. Interestingly, AI is following a similar trajectory, progressing through different levels of intelligence. However, while AI may mimic human learning, it does not think the way we do.
Let’s explore AI’s development through the lens of Bloom’s Taxonomy.
Stage 1: Remembering – The Data Collector
What Humans Do: Memorize facts, such as historical dates or
multiplication tables.
What AI Does: Stores and retrieves vast amounts of information but lacks
understanding.
Example: Early spam filters could detect emails containing “FREE” and classify them as spam. But as soon as spammers changed “FREE” to “FR33,” the filters failed. The system was only memorizing patterns, not understanding them.
Why It Matters:
- AI at this stage can retrieve information quickly but cannot adapt to new contexts.
- Best suited for organizing large datasets and retrieving stored knowledge.
Try it yourself: Google Quick Draw – See how AI recognizes doodles based on past data.
Stage 2: Understanding – The Recognizer
What Humans Do: Recognize context, like interpreting sarcasm or
distinguishing different meanings of a word.
What AI Does: Recognizes words, images, or speech in context but still
lacks comprehension.
Example: Voice assistants like Siri and Google Assistant can respond to basic questions but often struggle with abstract or nuanced requests. Asking, “What’s the weather like?” works fine, but saying, “Tell me something to cheer me up” may return an unrelated fact.
Why It Matters:
- AI at this stage can categorize and respond to inputs but does not truly grasp meaning.
- Useful for tasks like speech-to-text, chatbots, and virtual assistants.
Try it yourself: Google Teachable Machine – Train AI to recognize patterns.
Stage 3: Applying – The Problem Solver
What Humans do: Use knowledge in new situations, like applying math
to real-world problems.
What AI Does: Makes predictions and decisions based on learned data but
lacks reasoning.
Example: Streaming services like Netflix suggest movies based on viewing history. But if you watch one documentary about space, you may suddenly get flooded with space-related content, even if you have no interest in astronomy.
Why It Matters:
- AI at this stage can automate decisions and enhance personalization.
- Best for recommendation systems, fraud detection, and predictive analytics.
Try it yourself: ChatGPT – See how AI generates responses based on learned data.
Stage 4: Analyzing – The Pattern Finder
What Humans Do: Break down information, identify trends, and draw
conclusions.
What AI Does: Detects hidden patterns in large datasets but lacks
interpretation.
Example: In manufacturing, AI can detect slight changes in machine vibrations that indicate potential failures. However, it cannot explain why the issue is happening—it only flags the anomaly.
Why It Matters:
- AI at this stage can assist in fields like healthcare (early disease detection) and finance (market trend analysis).
- Still requires human oversight to interpret results.
Try it yourself: IBM Watson AI – Explore AI-driven insights in business and healthcare.
Stage 5: Evaluating – The Decision Maker (Not Fully Here Yet)
What Humans Do: Make judgments based on logic, ethics, and reasoning.
What AI Does: Can make decisions based on data but struggles with
fairness and context.
Example: Some hiring AI systems have been found to favor certain applicants over others because they were trained on biased historical data. Without human intervention, AI may reinforce past discrimination rather than correct it.
Why It Matters:
- AI is already being used in hiring, law enforcement, and medical diagnostics.
- Human oversight is essential to ensure fairness and ethical decision-making.
Stage 6: Creating – The Innovator (Still Science Fiction)
What Humans Do: Invent new ideas, art, or scientific theories.
What AI Does: Generates new content based on existing data but does not
create independently.
Example: AI can compose music in the style of Beethoven or paint in the style of Van Gogh, but it is not truly inventing something original—it is recombining what already exists.
Why It Matters:
- AI is becoming a tool for human creativity, enhancing areas like art, music, and design.
- True autonomous innovation is still a long way off.
AI Is Not Thinking—It’s Processing
AI may follow a path similar to human learning, but it operates fundamentally differently:
- AI doesn’t understand—it predicts.
- AI doesn’t think—it calculates.
- AI doesn’t create—it generates.
AI is not replacing human intelligence. Instead, it is reshaping how we interact with technology and challenging us to think more critically about our role in its development.
The real question is: are we using AI wisely,
or are we letting it shape our decisions without question?
