Learning Intelligence, Neuroscience, Digital Twins and Analytics
A framework for institutions that need to make technology decisions they can defend to parents, to boards, to regulators, and to themselves.L.I.N.D.A. was developed and field-tested at municipal public-network scale [VERIFY: confirm the exact scope you are authorised to claim and the attribution wording agreed with the network]. It is built on four service lines that can be engaged separately or as a sequence.
Line 1 — Baseline: measure before you decide
You cannot manage what you have never measured, and almost no institution has a real baseline for digital maturity or AI use.
What you get
Validated diagnostic instrument covering three dimensions: infrastructure, educator competency, and pedagogical AI integrationFull-institution baseline measurement with psychometric validation reportSegmented analysis by stage, subject area and staff tenureBenchmarking against national and international reference frameworks.
Built on:
DigCompEdu (European Commission, 2022); UNESCO AI Competency Framework for Teachers (2024), including the Acquire / Deepen / Create progression; sampling methodology from TIC Educação (Cetic.br/NIC.br).
Timeline: 8–12 weeks
Best for: any institution about to invest, restrict, or write policy
Line 2 — Policy: write rules that survive contact with realityMost school technology policies are unenforceable, legally exposed, or both. The failure mode is always the same: written by committee, without evidence, and without a plan for what happens when someone breaks them.
What you get
Device and AI use policy, drafted for your specific legal and cultural contextAcademic integrity framework redesigned around assessment, not surveillanceData protection impact assessment for every system holding student dataFamily and student communication protocolStaff enforcement guidance — the part almost every policy omits
Built on:
UNESCO Recommendation on the Ethics of Artificial Intelligence (2023); applicable data protection law [VERIFY: LGPD for Brazil, GDPR for EU, adapt per market]. The 2023 GEM Report found that only 16% of countries explicitly guarantee data privacy in education by law, and that 89% of 163 edtech products recommended during the pandemic were capable of surveilling children. Your policy has to do the work your national law isn't doing.
Timeline: 6–10 weeks
Best for: institutions facing parent pressure, an integrity crisis, or a regulatory deadline
Line 3 — Early Warning: find the students who are driftingInterpretable predictive models that identify students at educational risk early enough to act — built to a standard that survives audit.
What you getRisk model trained on your institution's data, with full documentationPublic model card and subgroup equity auditAlerts integrated into the workflow your staff already useIntervention protocol tied to each risk tierHandover training so the model outlives our engagement
Our commitment on accuracy — read this before comparing us to competitors.Vendors routinely advertise 93–98% accuracy for dropout prediction. Those figures come from cross-sectional, single-institution, higher-education studies using synthetic oversampling. The 2026 review by Duro and colleagues in Computers, covering 61 studies, concluded that the field must move away from accuracy-focused evaluation toward transparent and accountable systems, and found that most studies rely on single-institution data with only a minority addressing fairness or real-world integration.In the setting that actually resembles a school longitudinal, population-level, K-12 Psyridou and colleagues (2024, Scientific Reports) achieved AUC between 0.61 and 0.65 using thirteen years of data including cognitive skills, motivation, behaviour and wellbeing.
We quote 0.65, not 0.98. We report precision-recall AUC, calibration, and error parity across student subgroups. If a competing proposal promises you 95% accuracy, ask which population it was measured on and whether they will report performance for your lowest-performing subgroup. The answer will tell you what you need to know.
Timeline: 5–8 months Best for: institutions with at least three years of usable historical data.
Line 4 — Simulation: test the policy before you impose it
A computational model of your institution that lets you simulate a policy change before it touches a real student.Timetable restructuring. Device restrictions. Intervention targeting. Resource reallocation. Run it, see the projected effect and its uncertainty, then decide.
What you get
Simulation model of your institution, validated by backtesting against your own historical dataThree policy scenarios modelled and documentedModel documentation sufficient for independent replication
On terminology — because precision here protects you. Tao and colleagues (2019, IEEE Transactions on Industrial Informatics) established that a true Digital Twin requires bidirectional real-time communication between physical and virtual entities. A system that only ingests real-world data without acting back on it is a Digital Shadow.
No education system in the world currently operates a bidirectional Digital Twin. We deliver a validated Digital Shadow and an honest roadmap toward the bidirectional stage. Any provider offering you a full educational Digital Twin has either redefined the term or not read the literature.
Timeline: 6–12 months Best for: networks and universities planning structural change.
