Custom AI Models for Personalized Learning at Scale
Ertas Studio empowers universities, school districts, and edtech teams to fine-tune AI models tailored to their curricula, student populations, and pedagogical approaches — at a fraction of the cost of commercial AI APIs.
The Challenges You Face
Commercial AI APIs Blow Through Education Budgets
Educational institutions operate on tight budgets with long procurement cycles. Per-token API pricing makes AI costs unpredictable, and a single semester of AI-assisted tutoring for thousands of students can generate a bill that exceeds the entire technology budget.
Generic Models Do Not Align with Curricula
Off-the-shelf LLMs do not understand your institution's specific learning objectives, grading rubrics, or pedagogical standards. They give technically correct but pedagogically inappropriate answers — solving problems for students instead of guiding them toward understanding.
Student Data Privacy Is Non-Negotiable
FERPA, COPPA, and state-level student privacy laws restrict how student data can be processed and shared. Many AI services cannot satisfy these requirements, and the compliance review process for new vendors can take longer than the academic year.
Faculty Lack ML Engineering Skills
The people who understand pedagogy and curriculum design are educators, not ML engineers. If AI tools require Python scripting and GPU management, adoption will be limited to the computer science department.
How Ertas Solves This
Ertas Studio provides a visual fine-tuning platform that educators and instructional designers can use directly. Upload examples of ideal tutoring interactions, curriculum-aligned explanations, or grading rubrics in simple JSONL format. Select a base model, click train, and Studio handles the cloud GPU orchestration, LoRA configuration, and checkpoint management.
The resulting model exports as a GGUF file that runs on university-owned servers — so student interactions never leave campus infrastructure. Per-query costs drop to the cost of electricity, making AI-assisted learning economically sustainable even at scale.
For educational institutions, this means custom AI tutors that follow your pedagogical principles, understand your curriculum, and respect student privacy — deployed on infrastructure you control with costs that fit within education budgets.
Key Features for Educational Institutions
Educator-Friendly Interface
The visual training workflow is designed for instructional designers and faculty, not ML researchers. If you can create a spreadsheet of example interactions, you can fine-tune a model in Studio.
Curriculum-Aligned Training
Fine-tune models on your institution's actual course materials, learning objectives, and grading criteria. The model learns to explain concepts the way your best instructors do, aligned with your specific pedagogical approach.
On-Campus Deployment
GGUF models run on university-owned hardware — a department server, a campus data center, or even a faculty laptop. Student data stays on campus, satisfying FERPA requirements without complex data processing agreements.
Cost-Predictable AI
Replace per-token API costs with a fixed Studio subscription for training and a fixed server cost for inference. Budget for AI like you budget for any other campus service — with known, predictable costs.
Why It Works
- Self-hosted GGUF models eliminate per-query costs, making AI tutoring economically viable for institutions serving thousands of students per semester.
- Curriculum-aligned fine-tuned models produce pedagogically appropriate responses that guide students toward understanding rather than simply providing answers.
- On-campus deployment satisfies FERPA's 'school official' exception requirements, avoiding the need for the parental consent processes that third-party AI services trigger.
- Faculty with no ML background have successfully fine-tuned discipline-specific tutoring models using Studio's visual workflow within a single professional development session.
- Fine-tuned models trained on institution-specific rubrics produce grading feedback that aligns with faculty standards more closely than generic LLM outputs.
Example Workflow
A university's chemistry department wants to offer AI-assisted tutoring for introductory courses. An instructional designer compiles 500 examples of ideal tutoring interactions — student questions paired with Socratic-method responses that guide students to the answer rather than providing it directly. These are formatted as a JSONL file.
The instructional designer opens Ertas Studio, uploads the dataset, selects a 7B instruction-tuned model, and starts training. Thirty minutes later, the model is ready. Testing in the playground confirms it responds in the Socratic style the department prefers. The GGUF is exported and deployed on a department server.
Students access the tutor through the university's LMS. Every interaction stays on campus infrastructure. The department pays nothing per query. When the next semester's curriculum is updated, the instructional designer adds new examples and retrains — the model evolves with the curriculum.
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