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Ceibal's AI-Assisted Feedback Tool for Online Teacher-Training Tutors (Uruguay)

Uruguay · Montevideo · See the Uruguay profile

Since mid-2024, Uruguay's Ceibal has given tutors on its online teacher-training courses an AI tool that drafts grading and detailed feedback for review before sending — cutting turnaround time and replacing generic remarks with personalised comments.

Ceibal's AI-Assisted Feedback Tool for Online Teacher-Training Tutors (Uruguay)

Details

Maturity
Scaling
Promoter
Fundación Ceibal / Administración Nacional de Educación Pública (ANEP)
Period
Since mid-2024; expanding through 2026 with the Acredita platform
Keywords
teacher training, online tutoring, AI-assisted feedback, formative assessment

Context

Fundación Ceibal, Uruguay's public digital-education agency, working with the national education authority ANEP, built an AI-assisted correction tool for tutors on its online courses for teachers in training and practising educators, using OpenAI models as the underlying AI service within Ceibal's own workflow and code.

Objectives

The tool aims to speed up feedback turnaround for tutors and shift written feedback away from generic remarks toward specific, personalised comments delivered close to submission time, while keeping a human tutor in control of the final grade and comments.

Activities

When a student submits a task, the AI drafts a correction with detailed comments and a suggested grade; the tutor then reviews it, can edit both the grade and the written comments, and only then sends it to the student, a human-in-the-loop design rather than an autograder. Ceibal is extending the same model to other assessment contexts, including a planned 2026 platform, Acredita, for adults seeking secondary-school equivalency certification, and a separate OEI-funded AI writing assistant for ninth-graders' essays.

Results

Ceibal reports the tool sped up turnaround time and moved feedback from generic remarks ("very good, continue") to specific, personalised comments. The project was recognised as best academic work at a regional educational-technology conference and was a finalist for a PwC innovation award; Stanford University researchers are separately studying its outcomes, though no results had been published as of reporting.

Implementation

Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.

Data sources

Where this practice's information was retrieved from, and when.

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