The Importance of Real-Time Data for English Learners

Every year, school systems invest significant time and resources into summative assessments to evaluate how English learners (ELs) are performing. But if you’re leading a district multilingual program, you already know the truth:

By the time the data arrives, it’s too late.

The Problem: You’re Flying Blind for Most of the Year

Here’s how the cycle typically unfolds:

• Students take a state language assessment (like TELPAS or ACCESS) in February or March.

• Scores are released in May or June.

• Analysis occurs in July or August, just before the next school year begins.

• Major programmatic decisions—scheduling, staffing, intervention models—are made without this data in mind. Any decisions we do make are based on data from 6 months prior.

  
  

That’s not just inefficient; it’s dangerous. We are making pedagogical decisions based on outdated data from a single test. It’s like steering a ship based on last season’s weather report.

The Consequences of Delayed Data

What happens in between?

• Months of instruction without clear visibility.

• Students who plateau or regress.

• Teachers left guessing.

  
  

This is the blind spot between benchmarks, and it’s time we address it.

Common Fixes (and Why They Fall Short)

Some districts have tried to fill this gap using the tools they already have:

1. Classroom-Based Observations

These are valuable, but they’re not scalable. Observations vary widely in quality and frequency. They rarely get digitized for analysis.

2. Periodic Benchmarks or Unit Tests

To gather more insight between summative assessments, many districts turn to tools like MAP Growth, AVANT, or Flashlight 360. These can provide valuable snapshots, but they come with trade-offs.

• Above Functional Language Level: They are often given above a student’s functional language level, especially in speaking and listening. This makes it difficult to gauge true growth.

• Instructional Time Lost: They require pulling students out of class, eating into precious instructional time.

• Delayed Results: They don’t deliver real-time data. Results are delayed, and by the time they’re analyzed, the instructional moment has passed.

  
  

Even when useful, these periodic assessments tend to focus on surface-level outcomes. They rarely capture ongoing growth across all four domains—especially the kind of academic vocabulary usage and oral fluency development that matters most for long-term success.

3. Teacher-Collected Work Samples

While useful, this method is time-consuming. Teachers are already stretched thin. District leaders can't build a system-wide strategy from a Google Drive folder of student journals.

Each of these offers a slice of the puzzle, but none provides the consistent, real-time formative data needed to drive instruction at scale.

The Emerging Opportunity: Real-Time Formative Data, Powered by AI

The truth is, we now live in an age where every student has access to a device. AI can analyze language growth in ways no human ever could at scale.

That’s a game-changer.

What’s Possible Today

Here’s what’s possible with modern technology:

• Automated formative assessments embedded into instruction three times a week.

• Real-time data on decoding, comprehension, vocabulary, and oral fluency.

• Progress monitoring without adding hours of grading or prep time.

• Skill-specific insights—not just “reading level” but exactly which skills are improving or stalling.

  
  

This isn’t science fiction. This is the promise of tools like AIR Language, which puts AI to work gathering and analyzing data as students read and speak.

A Better Path Forward

Imagine this scenario:

• Students read AIR Language for 20 minutes, three times a week.

• They read aloud to Ari, AIR’s AI reading tutor. Ari listens, prompts questions, and engages them in short academic conversations.

• Ari captures data—not just “can this student read?” but “do they understand?” and “how well are they using academic language?”

• That formative data is automatically delivered to teachers and district dashboards.

• You see what’s happening right now—by classroom, campus, and skill.

  
  

No spreadsheets. No guesswork. No six-month delays.

The Bottom Line: Precision, Not Prediction

Summative assessments will always have a role, but they shouldn’t be your only lens into student learning. What we need now is precision, not prediction.

With platforms like AIR Language, you gain visibility between the benchmarks—when it matters most.

If we want to accelerate English learner growth, we need to stop steering blind. Let’s start seeing clearly.

The Future of English Learner Assessment

As we move forward, it’s essential to embrace innovative solutions. The integration of real-time formative data can transform how we support English learners.

By leveraging AI and technology, we can ensure that every student receives the guidance they need, exactly when they need it. This approach not only enhances learning outcomes but also empowers educators to make informed decisions.

In conclusion, the future of education lies in our ability to adapt and utilize the tools available to us. Let’s commit to a data-driven approach that prioritizes the needs of our students and fosters their growth.

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