Understanding the "Artificial" in Artificial Intelligence Education Tools for Teachers

In the excitement over what AI (artificial intelligence) education tools for teachers can do, it’s easy to forget what it is: a powerful but artificial system shaped by human design and data. Understanding its “artificial” nature and limitations is important—especially for teachers. That’s why it’s critical to ask the right questions when selecting AI tools for your classroom.

What Is AI, Really?

AI refers to technologies designed to replicate certain aspects of human intelligence. Examples range from email spam filters to advanced models like ChatGPT, which can generate human-like text or create images from scratch. AI also powers tools that recognize student speech or analyze writing. But AI doesn’t have all the features of human intelligence—it’s simply a tool built to perform specific tasks efficiently.

I like to compare AI to artificial flowers, which have several benefits over real flowers if your goal is longevity or a low-maintenance way to brighten a room. But regardless of how real they might look, they were designed to mimic real flowers, not produce pollen. So, while they’re a good option for decoration, they’re useless to honeybees.

Similarly, many AI tools can be incredibly useful for specific tasks, and some are very convincing in their human-likeness. However, they were designed for a purpose and lack the insight and creativity to be considered truly intelligent. Keeping this distinction in mind helps set realistic expectations of what AI can do—and what it can’t.

Ultimately, an AI system is only as good as the data it’s trained on and the algorithms that power it. In classrooms where fairness and accuracy for all students matter deeply, this consideration is crucial.

Common Blind Spots in AI Education Tools

Because AI’s intelligence is artificial, understanding where its blind spots come from is important to using it wisely and fairly. Two main factors shape how AI performs: the data it learns from and the human errors made during its development.

Challenge 1: Representation in Training Data

AI systems learn from examples—sometimes billions of them. But if those examples aren’t diverse, the AI will have gaps in its knowledge. For instance, if a speech recognition model is trained mostly on adult English speakers from one region—say, the Midwest—what happens when a first grader in Texas uses it? Or when a student whose first language is Spanish tries? The AI may struggle to understand them because it hasn’t “heard” enough of those voices or accents. In other words, it doesn’t “speak that dialect.”

This issue isn’t limited to speech AI. A writing-feedback tool might struggle with specific cultural references or patterns if it hasn’t encountered them during training. The result: a system that works well for some students but doesn’t respond as effectively to others.

As someone who builds speech recognition systems, I follow a simple rule: aim for as much breadth and diversity of training data as possible and continuously test the system to discover any areas of underrepresentation.

Challenge 2: Human Errors in AI Development

Even with plenty of data, how we design and train AI also matters. Humans decide what data to include, how this data is categorized and labeled, and how it will be used to train and fine-tune a model. Human error can become internalized in an AI system, impacting its performance for different groups of students.

Take an AI system designed to score essays. It’s typically trained on human-graded samples, treating those scores as the standard for evaluation. But human graders may penalize unfamiliar content or writing styles. If certain language varieties receive lower scores from humans, the AI will likely learn and replicate this scoring—reflecting patterns we never intended to encode.

It’s also important to remember that AI has no lived experience or ethical judgment. It only knows what we teach it. Generative AI models are often trained on data from across the internet, encountering information that’s helpful, unhelpful, factual, and subjective. Without careful oversight, it can absorb or repeat those errors in its responses.

I’ll admit, seeing the number of AI products on the market that don’t adequately address these challenges has made me a self-confessed “AI skeptic,” though I work in AI daily. But that skepticism drives responsible development. In our work on speech technology, we rigorously test before any system reaches students. We look for cases where the AI might fall short, testing for different accents, regions, classroom noise, and unexpected child speech patterns. When it struggles, we retrain the model and gather more data to bolster any weaknesses. In a sense, we try to “break” the AI in the lab so it won’t break in your classroom. That kind of continuous, fairness-focused testing is essential—and one every education technology company should adopt.

AI in Education: Key Questions to Ask

Understanding AI’s limitations is empowering. It’s not magic; it mirrors the data and people behind it. Educators don’t need to be AI experts, but you should feel confident asking tough questions about any AI-powered tool you’re considering using in your classroom. High-quality educational AI products should have clear answers to questions like these:

1. What data was this AI trained on?
2. How does it support different learners?
3. What steps are taken to ensure fair performance across groups?
4. What are the tool’s known limitations?
5. How much control do teachers have over the use and outputs of this tool?
6. Has it been externally reviewed or validated?

How Teachers Can Evaluate AI Education Tools Responsibly

AI in the classroom holds real promise. It can save time, personalize practice, and create new ways to engage students. But AI is not the same as human insight or understanding. As we introduce these tools, we must do so thoughtfully and with awareness of where they excel and where they need oversight.

My hope in sharing this is to help you feel more confident approaching AI with interest and care. When a new tool promises to transform learning, ask how and why. In doing so, you’ll help ensure that technology benefits—and does not harm—students.

Discover more about our approach to responsible AI innovation tools for the classroom.

Other Recommended Resources for You:
Safeguarding Students—Why Responsible AI in Education is Essential
Guest Post: A Decade of Building Responsible Voice AI for Children—Digital Promise