Learners180|
Schools3|
Assessments6|
Pass Rate57%
Driving education equity in Zimbabwe

Turn school data into governance intelligence.

HiveMind Intelligence collects, connects and analyses school data to produce practical decision signals for teachers, school leaders and education stakeholders.

Deterministic analysis first. AI advisory. Human decisions remain central.

Teacher-supportiveAssessment-orientedDeterministic firstAI advisorySchool-ready evidence

Evidence engine

Marks become support signals

Live demo data
HivemindPractical Readiness Checkgraded
HivemindTopic Test — Cells & Energygraded
PilotFractions & Ratios Quizgraded
AetherGrammar Checkgraded
AetherComprehension Diagnosticgraded
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tracked topics
0
support signals
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mark nodes

Actual product workflow

From scattered school data to governance evidence.

One clear workflow. Deterministic analysis first; AI supports the teacher, not the other way around.

Phase 1 — From school data to connected evidence

  1. 01

    Build the school profile

    Schools enter administrative, staffing, enrolment, class, subject and resource information.

  2. 02

    Record micro-assessment evidence

    Teachers upload spreadsheets or enter question-level assessment results.

  3. 03

    Map evidence to the curriculum

    Each assessment question is linked to the relevant topic, skill and curriculum objective.

  4. 04

    Connect performance with school context

    HMI analyses learning patterns alongside class size, staffing, resource availability, previous performance and curriculum coverage.

Phase 2 — From signals to governance decisions

  1. 05

    Generate intelligence signals

    The system identifies weak topics, difficult questions, affected learner groups, class-level performance differences, recurring misconceptions, possible contributing factors, and issues requiring leadership review.

  2. 06

    Plan teacher-led support

    HMI recommends possible support actions while keeping teacher judgement central.

  3. 07

    Implement and track change

    Teachers and school leaders record the action taken and compare later evidence with the original baseline.

  4. 08

    Produce governance evidence

    HMI creates outputs for teachers, heads of department, school leadership, district or policy stakeholders, and curriculum and examination planning where appropriate.

Intelligence levels

Intelligence at three levels.

One connected data model, three decision audiences.

Teacher intelligence

Question-level, topic-level and learner-group evidence that helps a teacher decide the next classroom action.

  • Weak-topic analysis
  • Difficult-question analysis
  • Learner-support signals
  • Misconception grouping
  • Reteaching recommendations
  • Follow-up checks
  • Evidence of change

School leadership intelligence

Class, stream and school patterns that help leaders see where support is working and where it is still needed.

  • Class and stream comparison
  • Learner-to-teacher ratios
  • Curriculum-coverage signals
  • Teacher-allocation patterns
  • Resource gaps
  • Intervention progress
  • Performance trends
  • Areas requiring leadership action

Stakeholder intelligence

Aggregated or anonymised evidence for district, policy and infrastructure planning — without exposing learner-sensitive data.

  • Resource-allocation planning
  • District and school trend analysis
  • Curriculum weakness detection
  • Examination-readiness signals
  • Infrastructure planning
  • Policy evaluation
  • Long-term education planning

Learner-sensitive data must not be exposed carelessly. Stakeholder outputs are aggregated or anonymised where necessary.

HiveMind Intelligence

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Demo data · Seeded multi-school demonstration data. No learner identities. Not verified pilot evidence.