How AI Is Helping Education Companies in Brazil Cut Costs and Improve Efficiency

Across Brazil, education companies are piloting generative AI to shave costs and speed delivery - think automated grading that returns faster feedback, AI agents that handle scheduling and attendance, and always‑on tutors that answer questions at 3 a.m.

to keep learning moving. Global research on generative AI trends in education highlights how content generation, intelligent tutoring and 24/7 chatbots free teachers to coach higher‑value skills (2024 report on generative AI trends in education), while regional briefings underline AI's potential to personalize instruction for Brazilian students (2025 AI landscape in Brazil regional briefing).

Practical tools like automated grading can cut feedback cycles, and workforce upskilling - for example through an AI Essentials for Work bootcamp - practical AI skills for the workplace (15 Weeks) that teaches prompt writing and tool use - helps schools deploy AI safely at scale, turning efficiency gains into wider access rather than lost jobs.

Bootcamp	Length	Early bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work (15 Weeks)

Education companies in Brazil are adopting AI because the economics and evidence both push in the same direction: a fast-growing AI market and heavy public and private investment create incentives to cut costs and scale services, while field experiments show concrete learning gains and time savings in schools.

National statistics point to a booming ecosystem - millions of monthly EdTech users and strong investment flows - so platforms can reach large, mobile-first classrooms quickly (Brazil EdTech market statistics and trends); government commitments and procurement also lower rollout barriers, from teacher training funds to state contracts (Brazil AI landscape growth and regulations statistics).

Pilots back this up: a randomized evaluation in Espírito Santo found AI grading increased ENEM essay practice, raised full-essay scores by about 0.09 standard deviations, and let teachers spend more time on higher‑value feedback - evidence that AI can both shorten feedback cycles and expand access when policy and infrastructure align (randomized evaluation of automated writing evaluation (AWE) in Espírito Santo).

In short, Brazil's adoption drives come from a mix of market opportunity, fiscal support, and pragmatic evidence that AI can free teacher time while improving student outcomes.

Metric	Value / Finding
AI market revenue (2023)	USD 11,089.1 million
EdTech monthly users	Over 12 million students
AWE RCT result	ENEM essay score +0.09 SD; program scaled to ~30,000 students/year

The Espírito Santo randomized evaluation of automated writing evaluation (AWE) shows how practical AI tools can both speed feedback and scale without breaking the budget: across 178 public schools (~19,000 students) the program used laptops and a platform that gave instantaneous AI feedback on ENEM‑style essays, and students in AWE schools wrote about 1.4–1.6 more training essays each and received roughly 1.3 more essays with feedback - real, measurable practice rather than one‑off tests.

Both the Pure AWE (AI scoring only) and the Enhanced AWE (AI plus human grading) arms raised full ENEM essay scores by ~0.09 standard deviations while increasing one‑on‑one essay discussions and reducing teachers' feelings of time insufficiency (enhanced AWE saw a drop from 23% to 9%).

The study's finding that Pure AWE performed similarly to the enhanced model helped Espírito Santo scale the lower‑cost option into state policy, potentially reaching ~30,000 students per year; the evaluation details and implications for classroom rollout are described in the randomized evaluation report (Espírito Santo AWE randomized evaluation report (Poverty Action Lab)), and practical automated grading tools such as automated grading with Gradescope illustrate how faster cycles of feedback can become routine classroom practice.

Metric	Value / Finding
Schools / Students	178 public schools; ~19,000 senior high students
Laptops provided	8,000 (Feb–Apr 2019)
Increase in training essays	Enhanced: +1.4 (29%); Pure: +1.6 (32%) per student
Increase in essays with feedback	≈+1.3 per student (≈40%)
ENEM essay score effect	+0.09 standard deviations (both interventions)
Policy uptake	Pure AWE procured by Espírito Santo; ~30,000 students/year

SOMOS Educação's Plu assistant - developed on AWS's GenAI stack (Amazon Bedrock plus CloudFront, RDS, EKS, S3, OpenSearch, SQS and SNS) and unveiled at Bett Brasil 2024 - is designed to shrink the two hours a day teachers spend on lesson prep by generating a complete 50‑minute lesson script in seconds, complete with illustrations, activities and tailored exam questions; piloted with ~3,400 students by July 2024, Plu aims to scale to 5,000–7,000 schools and leans on SOMOS's huge content repository and a RAG (retrieve‑augment‑generate) approach to keep outputs relevant.

The partnership with AWS (and support from Accenture on model architecture) highlights a pragmatic route to scale: fast lesson planning that feeds adaptive teaching tools, 1.4 million AI objects already created on Plurall, and a roadmap toward individualized educational plans and inclusive features by 2026 - all detailed in coverage of the Plu pilot and in Vasta's platform results that note Plurall AI activity and growth in complementary solutions.

For Brazilian schools this isn't just tech for tech's sake: when routine prep is trimmed, conservative estimates point to nearly 24 extra teacher working days a year from a modest 10% productivity gain, freeing educators to focus on coaching and student support rather than paperwork (SOMOS Plu pilot and rollout goals, Vasta first-quarter 2025 results and Plurall AI metrics).

Metric	Value
Pilot students (July 2024)	~3,400
Target schools (2025 / broader)	5,000 by 2025; 7,000+ target
Plurall reach since 2014	7 million students; 120,000 teachers
Plurall AI objects created	1.4 million

“We believe this technology can be incredibly valuable in freeing them from time-consuming tasks… nearly 24 additional days per year” - Rafael Augusto Teixeira, Senior Computing Manager at SOMOS Educação.

NovaEscola's WhatsApp Planner (ANE) turns a teacher's most tedious chore - lesson planning - into a seconds‑long WhatsApp exchange by drawing on a trusted 6,000‑plan library aligned with Brazil's national curriculum, a design meant to work even with sporadic internet in the countryside; developed through the 2024 GenAI Accelerator with Turn.io, OpenAI and WhatsApp, the tool shipped from pilot to national scale and, since February 2025, more than 15,000 users have generated over 63,000 customized plans, with early impact showing 34% of teachers saving 30–60 minutes per class and another 34% saving 10–30 minutes.

The service complements NovaEscola's high‑quality repository (almost 1 million monthly accesses) and strong teacher feedback - pedagogical relevance 8.9/10, readability 9.1/10 - while real classroom stories (like a multigrade teacher in the Northeast adapting one plan for three grades) make clear how simple WhatsApp access can widen equitable, curriculum‑aligned practice across Brazil; try the lesson library or add ANE on WhatsApp to see how it works in practice via NovaEscola's plan hub and HundrED profile.

Metric	Value
Core lesson library	6,000 curriculum‑aligned plans
Monthly platform access	Almost 1,000,000 users
WhatsApp Planner scale (since Feb 2025)	>15,000 users; >63,000 plans created
Teacher time saved	34% saved 30–60 min/class; 34% saved 10–30 min
Quality scores	NPS 88+; pedagogical relevance 8.9/10; readability 9.1/10

“The idea is so simple and elegant. This could and should be implemented globally; this should be the norm and already in use!”

AI tools have trimmed routine costs and unlocked faster, cheaper scaling for Brazilian edtechs by automating time‑consuming tasks like essay scoring and lesson prep: in Espírito Santo a randomized evaluation found that AI feedback led students to submit roughly 1.4–1.6 more practice essays and increased essays receiving feedback by about 1.3 per student, while teachers' use of the platform exceeded 95% - results that translated into a modest but meaningful ENEM essay gain of ~0.09 standard deviations (about 17 points) and made the lower‑cost AI‑only model attractive for state procurement (see the J‑PAL randomized evaluation of AI feedback in Brazil's schools).

Practical design choices - offline‑friendly platforms, 8,000 laptops provided during rollout, and AI that replicates useful human feedback - let programs scale widely at lower marginal cost; Espírito Santo moved to procure the Pure AWE tool (reaching an estimated ~30,000 students/year) and, across later rollouts, over 100,000 seniors have used the platform.

For product teams, pairing pragmatic automation with selective human review (or Gradescope‑style workflows) is a clear path to faster feedback cycles, lower per‑student costs, and more teacher time for high‑value coaching (Gradescope‑style automated grading workflows for faster feedback).

Metric	Value / Finding
Schools / Students in RCT	178 public schools; ~19,000 students
Laptops provided	8,000
Increase in training essays	Enhanced: +1.4; Pure: +1.6 per student
ENEM essay score effect	+0.09 SD (both interventions)
Teacher take-up	>95% used the ed techs
Policy uptake / scale	Pure AWE procured by Espírito Santo; program reached 30k/yr and 100k+ seniors enrolled since 2020

“The implementation of a writing platform for all students enrolled in their senior year of high school in our public education network reaffirms the commitment of the Government of the State of Espírito Santo, through its State Department of Education - Sedu - to continuously invest in innovative actions that positively impact the learning and future of these young people. Since 2019, this initiative has already benefited more than 60,000 students, proving to be a strong ally in promoting the development of writing and reading skills of the network's students.” - Statement from Sedu

Student and teacher voices in Brazil show a pragmatic embrace of AI tempered by real worries about access and ethics: over 60% of Brazilian students aged 15–24 used at least one AI‑powered educational tool in the past year, so AI is already part of many study routines and homework workflows, while more than 12 million students engage with EdTech platforms monthly - a scale that helps explain why teachers encounter AI in their daily practice (BytePlus analysis of AI use among Brazilian students, DigitalDefynd Brazil EdTech market statistics).

Educators are cautiously optimistic - about 62% expect AI to improve educational quality - and many report time savings from automation, yet persistent gaps remain (roughly 30% of rural students lack consistent high‑speed internet and nearly 40% of households still face connectivity challenges), so tools that save teachers time can unintentionally widen divides unless paired with targeted infrastructure and training.

The takeaway is concrete: when AI returns faster, curriculum‑aligned feedback and lesson plans quickly reach classrooms, but policy and teacher upskilling must follow to turn those efficiency gains into fairer learning opportunities for all.

Metric	Value / Source
Students (15–24) using AI tools	>60% - BytePlus
Monthly EdTech users in Brazil	>12 million - DigitalDefynd
Educators expecting quality gains	≈62% - Magma translation
Rural connectivity gap	≈30% lack consistent high‑speed internet - BytePlus

Implementation in Brazil hinges as much on governance as on models: the LGPD applies broadly to any processing of Brazilian residents' data and the ANPD enforces tight rules - including breach notifications within three working days and penalties up to 2% of local revenue (capped at BRL 50 million) - so edtechs must bake privacy into product design rather than treat it as an afterthought.

Practical steps include automated data mapping and DSR workflows, vendor risk checks, documented DPIAs for high‑risk uses, and clear explainability and human‑oversight protocols for automated decisions as the pending Brazilian AI Bill adds a risk‑based overlay to LGPD obligations (see Securiti LGPD compliance tools for automated discovery and DSR handling and DLA Piper summary of Brazil data protection law for legal scope and enforcement details).

Technical safeguards (encryption, access controls), cross‑border transfer clauses or approved safeguards, and an incident playbook are nonnegotiable - remember that a single, well‑publicized breach can trigger both heavy fines and lasting trust damage, so treat privacy as a product feature from day one.

Risk / Consideration	Required Action (based on LGPD & AI guidance)
Regulatory enforcement & fines	Maintain records of processing, appoint responsible contact, and monitor ANPD guidance
Data subject rights (DSRs)	Automate intake, verification and fulfillment to meet statutory timelines
Breach notification	Incident response plan to notify ANPD and affected subjects within required windows
Cross‑border transfers	Use adequate safeguards (SCCs, binding rules) and document transfers
Automated decisions / AI risk	Conduct DPIAs, ensure explainability, human oversight and documentation

“An Ideal Solution for Mastering Privacy Compliance”

Brazilian education startups should follow a pragmatic, locally grounded roadmap: first, design products that map to national priorities and funding channels - align offerings with the PNE and the Connected Education Innovation Policy to tap procurement and broadband goals (Brazil technology in education policy and connectivity); second, pilot fast and iterate with teachers at the center, using rapid-cycle experiments and stakeholder workshops like the PDIA pilots at UNIVESP to build authority, acceptance and measurable learning workflows (PDIA pilots for teacher training in Brazil); third, design for uneven connectivity and device gaps by supporting offline modes, low‑bandwidth delivery and device programs (PROUCA/Connected Education guidance); and fourth, measure scale with hard adoption and impact metrics - Brazil already has a $3.5B EdTech market and 12M+ monthly users, so product teams should benchmark reach, teacher takeup and time‑saved outcomes early (Brazil EdTech market statistics and user metrics).

Roadmap Step	Key Action (research-backed)
Policy alignment	Map product to PNE & Connected Education priorities for procurement and broadband support
Pilot & iterate	Run rapid PDIA-style pilots with UNIVESP/teacher workshops and clear metrics
Connectivity-first design	Support offline/low-bandwidth modes and device strategies (PROUCA)
Measure & scale	Track adoption, teacher time saved, and reach vs. national EdTech benchmarks

“Design is about ideas, implementation is about people”

Start small in classrooms (even spaces where students work from beanbags) to prove learning gains, then use validated pilots and teacher training to turn efficiency into equitable scale rather than tech-for-tech's-sake.

The bottom line for Brazil: randomized evidence from Espírito Santo shows AI can be a pragmatic lever - automated essay scoring and feedback raised full ENEM essay scores by about 0.09 standard deviations, increased student practice and teacher‑student discussions, and (helpfully) scaled after a rollout that included 8,000 laptops and very high teacher take‑up, leading the state to procure the lower‑cost Pure AWE model for roughly 30,000 students a year (Espírito Santo randomized evaluation (J‑PAL)).

Scaling those modest but real gains across Brazil will require three policy pillars: invest in connectivity and devices so rural students aren't left behind; lock in privacy, procurement and compliance measures to navigate LGPD and emerging AI rules; and fund teacher upskilling so automation augments pedagogy rather than replaces it - practical workforce training like the AI Essentials for Work bootcamp (15 Weeks) can help build prompt and tool fluency at scale.

Taken together with national AI trends and regulatory guidance, a deliberate mix of infrastructure, governance, and human capital investment can turn cost savings into broader, equitable learning gains rather than deeper divides (Brazil AI landscape and regulatory risks).

Key indicator	Value / Finding
ENEM essay effect	+0.09 SD (both AWE interventions)
Teacher take‑up	>95% used the ed tech
Laptops provided in rollout	8,000
Policy scale (Espírito Santo)	Pure AWE procured; ~30,000 students/year

“These are very suitable use cases for artificial intelligence. With a well-designed project, it is possible to do not only more, but also better, with fewer resources.” - Gabriel Casara, CGO, BlueMetrics