Underfitting
In artificial intelligence and machine learning, not all mistakes come from overcomplicating things—sometimes, the problem is making systems too simple. This issue, known as underfitting, occurs when a model fails to learn enough from its training data, leading to poor performance across both familiar and new tasks. In education, underfitting can mean AI tools that provide generic, unhelpful feedback or fail to adapt to students' real needs. Understanding underfitting helps educators critically evaluate the technology they use, ensuring it genuinely supports diverse learning styles and promotes meaningful, personalized instruction. This guide explores what underfitting is, why it matters in education, and how to spot and address it.
What is Underfitting?
In machine learning (ML) and artificial intelligence (AI), underfitting is when a model is too simple to capture the underlying patterns in the data. It fails to learn enough from the training data and, as a result, performs poorly on both the training set and any new data. If overfitting is like memorizing instead of understanding, underfitting is like not learning enough in the first place.
Underfitting happens when a model lacks the complexity needed to model the relationship between inputs and outputs effectively. This can lead to poor predictions, generic outputs, or an inability to distinguish important differences in the data.
How to Explain to Students
Elementary (K–5): "Underfitting is like trying to solve a big puzzle with just one small piece. The computer doesn’t learn enough to be helpful."
Middle School (6–8): "Imagine your teacher gives a one-size-fits-all answer to every question, even if the questions are totally different. That’s underfitting. The answer is too simple."
High School (9–12): "Underfitting happens when a machine learning model is too basic to find useful patterns. It ignores details that matter, so it makes weak or incorrect predictions."
How Underfitting Appears
Underfitting can be seen in a wide range of applications. For example, a basic AI tutor that gives the same generic advice regardless of a student's input is likely underfitting. Other examples include:
A math recommendation app that always suggests the same practice problems regardless of a student's performance.
An essay grading tool that gives similar scores to all submissions.
An early warning system that fails to flag students needing support because it overlooks nuanced behavior patterns.
These tools may look like they work but are often ineffective in practice, especially when students have diverse needs and learning styles.
Key Aspects of Underfitting
How does underfitting happen? It's due to four major concepts:
Simplicity of the model
Poor performance on training and testing data
Inadequate training or features
Diagnosing and addressing underfitting
Simplicity of the Model
Underfitting usually results from using a model that is too simple for the complexity of the task. For example, a linear model might try to fit a straight line to data that clearly follows a curve. No matter how much training it receives, it won’t perform well because it cannot capture the true relationship.
Poor Performance on Training and Testing Data
A telltale sign of underfitting is low accuracy on both training and test data. Unlike overfitting, which does well in training but poorly in testing, underfitting does poorly in both. This suggests the model hasn't learned the important patterns from the data at all.
Inadequate Training or Features
Underfitting can also happen when the training process is rushed or the data provided is missing important information. If a model only receives simple or low-quality inputs, it can't produce high-quality outputs. Similarly, if the data lacks variety, the model might generalize too much and fail to notice important distinctions.
Diagnosing and Addressing Underfitting
Educators and developers can recognize underfitting through evaluation scores and visual tools like loss curves. Solutions to underfitting include:
Increasing model complexity
Extending training time
Providing richer, more detailed input data
Adding more relevant features
Why is Underfitting Relevant in Education?
For educators, underfitting matters because it can make AI tools appear functional but actually limit their usefulness in the classroom. A generic recommendation tool might not respond to student needs. An AI writing assistant might give vague, unhelpful suggestions. Without enough learning, these tools fail to support personalized or meaningful instruction.
Understanding underfitting empowers educators to select better tools and ask vendors the right questions. It also helps teachers explain to students why some technologies need improvement or tuning.
Underfitting in Education Examples
Underfitting can have an impact in:
Educational assessment tools
Student monitoring and intervention
Content recommendation systems
Educational Assessment Tools
AI tools used for grading or feedback can underfit if they're not trained on diverse writing samples. This may lead to overly safe scoring or general comments that don’t reflect student effort or creativity.
Student Monitoring and Intervention
If a predictive model is underfitted, it may miss early signs of academic struggle. For instance, it might overlook attendance trends, behavior patterns, or subtle shifts in performance. This leads to missed opportunities for early intervention.
Content Recommendation Systems
Underfitted models might suggest the same materials to all students, failing to adapt to individual needs. This undermines personalized learning and can lower student engagement.
To avoid these issues, schools can:
Choose AI tools with evidence of high training performance
Ensure data used in training represents all student groups
Support ongoing evaluation and feedback mechanisms
Learn more with Flint
Underfitting is a hidden but significant challenge when using AI tools in education. Models that underfit fail to capture the rich complexity of real-world learning, offering shallow predictions, generic feedback, and missed opportunities for meaningful support. By understanding underfitting, educators are better equipped to choose smarter, more effective technologies and to advocate for AI tools that truly reflect the diverse experiences and needs of their students.
If this guide excites you and you want to apply your AI knowledge to your classroom, you can try out Flint for free, try out our templates, or book a demo if you want to see Flint in action.
If you’re interested in seeing our resources, you can check out our PD materials, AI policy library, case studies, and tools library to learn more. Finally, if you want to see Flint’s impact, you can see testimonials from fellow teachers.
