Curriculum Intelligence
AI signals across standards, pacing, and assessed evidence
Aggregation layer
Curriculum signals across your stack
Pathway pulls pacing, assessment, and standards alignment from Illustrative Mathematics, Canvas, and your SIS — then uses AI to surface where instruction and learning diverge and recommend adjustments.
Standards Taught
78%
Of CCSS grade-level standards
Standards Assessed
65%
≥ 1 measurable evidence
Mastery Rate
43%
Across all assessed standards
Pacing Variance
±2.4w
Across sections
Standards at risk
Taught but under-mastered — candidate for re-teach
5.NF.B.7
Divide unit fractions by whole numbers
Mastery
22%
Taught
Mastered
4.OA.A.3
Multi-step word problems with whole numbers
Mastery
31%
Taught
Mastered
3.MD.C.7
Area & multiplication / addition
Mastery
18%
Taught
Mastered
6.RP.A.3
Use ratio reasoning to solve real-world problems
Mastery
44%
Taught
Mastered
Section pacing drift — Grade 4 Math
Units completed vs scope & sequence
AI recommendations
Generated from pacing + mastery + assessment evidence
Re-sequence Unit 4 — Ratios before Proportions
Mastery on 6.RP.A.3 lags by 27 pts versus pacing.
+8 pts projected mastery
Insert formative check for 5.NF.B.7
Standard is taught but mastery is half the cohort average.
Catch misconception 2 weeks earlier
Reduce Unit 6 by 1 week — Section C
Section C is 5 weeks behind A on pacing.
Recover EOY coverage