Educometrika
PT|EN

Artificial intelligence applied to public education

Using artificial intelligence on concrete school system problems, with risk assessment and without adding complexity for schools.

Data science in educationEducation data analysisPolicy and program evaluationEducation public policy

01What problem this area addresses

Departments receive many proposals for AI tools, but it is not always clear which problem they solve, what data they use and what risks they bring for students and teachers.

Work in this area starts from the system's problem and assesses whether AI is the right answer, how to test it and how to track its effects.

02Questions education departments often face

  1. Q1In which tasks can AI reduce teams' workload without loss of quality?
  2. Q2How should a vendor's tool be assessed?
  3. Q3What care is needed with students' personal data?
  4. Q4How can a solution be tested at small scale before system-wide adoption?
  5. Q5What guidelines should the department set for AI use in schools?

03How Educometrika approaches the problem

  1. 01

    Use cases

    Identifying problems where AI can help, and those where it should not be used.

  2. 02

    Risk and compliance

    Assessing privacy, bias, transparency and data protection compliance.

  3. 03

    Pilot

    Controlled test with success criteria defined in advance.

  4. 04

    Evaluation and guidelines

    Analysis of pilot results and support for drafting usage rules.

04Data and methods

Data

  • Administrative and pedagogical data
  • System documents and texts
  • Student responses, when authorized
  • Tool usage logs

Methods

  • Language models
  • Classification and information extraction
  • Predictive models
  • Bias assessment
  • Pilot design and evaluation

05Possible deliverables

Use case mappingTechnical opinion on toolsRisk matrixPilot plan and reportGuidelines for AI use in the system

06When this kind of support makes sense

  • Before procuring an AI tool.
  • When the department wants to set rules for AI use in schools.
  • When repetitive tasks take up technical teams' time.
  • When evaluating a pilot already under way.

07Related topics

08

Is your system facing a similar problem?

Describe the context and the open questions. The team will assess how it can contribute.

Talk to the team

All areas

Consulting for education departmentsTechnical support for state and municipal education departments across the whole policy cycle: from diagnosing the system to evaluating results.Education public policyDesign and implementation of education policies and programs that account for the real conditions of execution in schools.Education data analysisOrganizing and analyzing enrollment, attendance, progression and learning data to answer concrete management questions.Educational assessmentPlanning and developing learning assessments that produce useful information for teachers, schools and management.Psychometrics and IRTMeasurement methods that ensure an assessment measures what it intends to and that results can be compared.Education indicatorsBuilding indicators that show the system's situation clearly and allow targets to be tracked over time.Policy and program evaluationEvaluations that check whether a policy was implemented as planned and whether it produced the expected results.Data science in educationData integration, statistical and predictive models and monitoring systems to support decisions in school systems.