Day 10 of 20 · AI for Teachers
Data & Tracking Made Easy
⏱ 5 min
📊 Beginner
Every half term, someone sends you a spreadsheet. Maybe it's assessment scores. Maybe it's attendance data. Maybe it's a tracking grid with 150 students and 14 columns of numbers that are supposed to tell you something meaningful.
You stare at the numbers. You sort by column. You highlight a few cells. You know there are patterns in there — students who are slipping, groups who need intervention, trends that should inform your teaching. But extracting those insights from a wall of numbers feels like a second job. One you didn't train for.
AI changes this completely. You paste in the data, describe what you want to know, and get clear, actionable insights in seconds. No formulas. No pivot tables. No data analysis degree required.
What AI can do with your assessment data
When you feed assessment data into AI, you can ask questions you've always wanted answered but never had time to investigate:
Gap analysis: "Here are my Year 8 Science scores across 5 topics. Which topics show the biggest gaps between expected and actual performance?"
Group comparisons: "Compare the average scores of boys vs girls, pupil premium vs non-pupil premium, and SEN vs non-SEN students. Flag any groups that are significantly underperforming."
Trend spotting: "These are the last three assessment scores for each student. Which students are improving? Which are declining? Which have been consistently at or below target?"
Intervention lists: "Identify students scoring below 60% in two or more topics. Group them by the specific topics they're weakest in, so I can plan targeted intervention groups."
Cohort overview: "Give me a summary paragraph I can use in a department meeting that describes overall performance, key strengths, areas of concern, and recommended next steps."
The data you already have contains answers to all of these questions. AI just makes them accessible without requiring you to become a data analyst.
Knowledge Check
What's the main barrier that stops teachers from getting useful insights from assessment data?
A
Assessment data isn't useful for teaching
B
Only senior leaders should analyse data
C
Schools don't collect enough data
D
Extracting meaningful patterns from spreadsheets is time-consuming and requires skills many teachers weren't trained in
The data is there — schools collect more of it than ever. The problem is turning numbers into actionable insight. Most teachers are trained to teach, not to wrangle spreadsheets. AI bridges that gap by letting you ask questions in plain English and get clear answers.
Here are my Year 9 Maths end-of-term scores (out of 60) across three topics:
Student, Number, Algebra, Geometry
Aisha M, 48, 52, 41
Ben T, 32, 28, 35
Chloe R, 55, 51, 53
Daniel K, 44, 38, 22
Ella W, 29, 31, 27
Finn J, 51, 49, 47
Grace P, 38, 42, 36
Harry S, 25, 19, 30
Isla N, 46, 50, 44
Jack L, 33, 27, 31
Pupil premium students: Ben T, Ella W, Harry S, Jack L
Analyse this data. Tell me: overall class performance, which topic is weakest, which students need intervention, and whether there's a pupil premium gap. Give me specific, actionable recommendations.
**Year 9 Maths — Data Analysis**
**Overall Performance:**
Class average: 38.4/60 (64%). This is below the typical target of 70% — the class as a whole needs support, but the picture is uneven. Your top performers (Chloe, Finn, Isla) are comfortably above target. Your concern group is clustered at the bottom.
**Weakest Topic: Algebra**
Class average by topic:
- Number: 40.1/60 (67%)
- Algebra: 38.7/60 (65%)
- Geometry: 36.6/60 (61%)
Geometry is actually the weakest overall, but the more concerning pattern is **Algebra for lower-attaining students**. Ben, Harry, and Jack all scored below 30 in Algebra — that's a significant gap that will compound if not addressed before you move on.
**Students Needing Intervention:**
Priority 1 (below 50% in 2+ topics):
- Harry S (25, 19, 30) — struggling across all three areas, Algebra critically weak
- Ella W (29, 31, 27) — consistently below 50%, no strong topic
- Ben T (32, 28, 35) — Algebra is a significant gap
- Jack L (33, 27, 31) — similar profile to Ben
Priority 2 (specific topic gap):
- Daniel K (44, 38, 22) — Geometry score is an outlier. He's capable in Number but something went wrong with Geometry. Worth investigating — was he absent? Is it a specific misconception?
**Pupil Premium Gap:**
Pupil premium average: 29.8/60 (50%)
Non-PP average: 44.8/60 (75%)
Gap: **25 percentage points** — this is significant and needs to be flagged.
All four PP students are in your intervention group. This isn't a coincidence.
**Recommendations:**
1. Run a targeted Algebra intervention for Harry, Ben, Jack, and Ella — focus on the foundational skills they're missing
2. Investigate Daniel's Geometry score individually — his other scores suggest this is a specific gap, not a general ability issue
3. Flag the PP gap with your line manager — 25 points is large enough to warrant additional support or resources
4. Consider a diagnostic quiz on Algebra prerequisites before your next Algebra unit to pinpoint exactly which skills are missing
↻ Replay conversation
That analysis would take 45 minutes with a spreadsheet
And let's be honest — most teachers wouldn't do it at all. Not because they don't care, but because who has 45 minutes spare to run pupil premium gap analyses when there are books to mark and lessons to plan?
AI makes this kind of analysis routine instead of rare. You can run it after every assessment. You can check in on specific groups whenever you want. You can walk into a data meeting with specific insights instead of vague impressions.
The data doesn't change. What changes is your ability to actually use it.
Generating intervention lists and action plans
Once AI has analysed your data, you can go further:
"Based on this analysis, create an intervention timetable. I have 2 slots per week for small group intervention (Tuesday P5 and Thursday P3). Group students by their weakest topic. Suggest a focus for each session over the next 4 weeks."
"Write a brief summary of each priority intervention student — their strengths, gaps, and recommended focus — that I can share with my teaching assistant before the next lesson."
"Draft an email to parents of the four pupil premium students, explaining that we're running additional support sessions and inviting them to a brief check-in meeting. Tone: positive and supportive, not alarming."
AI takes you from data to action in minutes. Instead of stopping at "these students need help," you get a specific plan for how to help them.
Knowledge Check
After AI identifies students needing intervention, what's the most useful next step to ask for?
A
A specific intervention plan with grouped students, session focus areas, and a timetable
C
A report to send to the headteacher
D
A graph showing the data visually
Identifying the problem is step one. The real value is moving from analysis to action — grouping students by need, planning focused sessions, and having a clear structure for intervention. AI can generate all of this from the same data set, turning numbers into a concrete plan.
From raw scores to clear action — AI turns data into decisions.
Patterns across cohorts and over time
If you teach the same subject across multiple year groups or have data from previous years, AI can spot bigger patterns:
"Here are the Year 10 Chemistry results from this year and last year. Are there topics that students consistently struggle with? Has the gap closed or widened?"
"Compare the performance of my three Year 8 classes on the same assessment. Are there significant differences between groups? What might explain them?"
"I've taught this unit three times now. Here are the results each time. Has my teaching of this topic improved over time?"
These questions are the kind that heads of department and curriculum leads wrestle with during data analysis meetings. AI doesn't replace that professional discussion — but it gives you a head start. You arrive at the meeting with insights already identified, not squinting at a colour-coded spreadsheet trying to work out what the red cells mean.
Final Check
How does AI change the role of data in your teaching?
A
It eliminates the need for assessments
B
It automatically improves student results
C
It replaces the need for teachers to understand their students
D
It makes data analysis routine and accessible, so you can use assessment insights to inform planning after every assessment — not just at data drop points
Data has always been valuable — teachers just haven't had time to analyse it properly between everything else. AI makes analysis fast enough to be genuinely routine. That means you can respond to data in real time, adjusting your teaching and interventions after every assessment, not just at formal data collection points.
📚
Day 10 Complete
"Your data already contains the answers. AI just makes them visible — so you can spend your time acting on insights, not hunting for them."
Tomorrow — Day 11
Report Writing Revolution
Tomorrow you'll tackle the most dreaded task in teaching — report writing. AI won't write your reports for you, but it will make the process dramatically faster.