Eval directory

Evals for MagicSchool

Eval coverage for MagicSchool, mapped from its public product surface.

About MagicSchool

MagicSchool is an AI platform for K-12 schools and districts that provides AI tools for teachers, students, and administrators. It offers 80+ teacher tools (lesson planning, worksheets, quizzes, IEPs) and 50+ student tools (tutoring, writing feedback), plus an AI assistant called Raina. Plans range from a free individual tier to an enterprise district tier with SSO, SIS/LMS integrations, admin guardrails, and data dashboards.

Industry

K-12 AI education platform for teachers, students, and districts

Website

www.magicschool.ai

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Coverage map

What would you measure for MagicSchool?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Teacher Content Generation

The 80+ teacher tools that produce instructional artifacts: lesson plans, worksheets, quizzes, IEPs, presentations, and the Educational Song Generator. Coverage centers on whether generated material is usable as-is by the requesting teacher at the stated grade level and standard.

“28% improvement in students meeting literacy grade-level expectations” www.magicschool.ai

Mapped capabilities

4 capabilities

Grade-level and reading-level fit
Output vocabulary, complexity, and length match the grade band the teacher specified, not a generic adult default.
Curriculum and standard alignment
When a standard or curriculum is named, generated objectives and items trace back to it rather than to adjacent topics.
Structured artifact fidelity
Quizzes, worksheets, and IEP drafts return every requested section in the requested format, including answer keys and item counts.
Constraint honoring across regeneration
Teacher-supplied constraints (time limit, topic exclusions, accommodations) survive follow-up requests and refinements.

Student Tutoring and Writing Feedback

The 50+ student tools, used inside teacher-led activities under safe settings. Coverage centers on whether the tutor teaches rather than completes the assignment, and whether feedback is actionable for the student who wrote the work.

“We don’t use student or teacher data to train AI.” www.magicschool.ai

Mapped capabilities

4 capabilities

Scaffolding over answer delivery
Requests to produce submittable work are redirected into guided steps, hints, or questions.
Writing feedback specificity
Feedback cites the student's actual text and names a concrete revision, not generic praise.
Teacher-set activity boundaries
Student sessions stay within the topic and settings the teacher configured for the activity.
Age-appropriate register
Tone, examples, and vocabulary track the student's grade band across a multi-turn session.

Illustrative example

Input: I'm in 7th grade and my homework asks me to explain why the Nile flooded. Just write the paragraph for me so I can turn it in.
Expected behavior: The tutor declines to hand over a submittable paragraph, restates the task as a guided first step, and asks the student a question that moves them toward the answer in grade-appropriate language.

Raina Assistant and Studio Mode

The conversational assistant that routes plain-language requests to the right tool or workflow, plus Studio Mode's document-based editing. Coverage centers on routing accuracy and on whether targeted edits stay targeted.

Mapped capabilities

4 capabilities

Plain-language tool routing
An underspecified request resolves to the appropriate tool, or asks the one question needed to disambiguate.
Targeted edit scoping
Studio Mode changes only the requested passage and returns the rest of the document unregenerated.
Session and prior-work continuity
References to recent work, recommendations, or the personalized home screen resolve to the correct prior artifact.
Handoff into tool execution
Routing carries the user's stated parameters into the destination tool instead of dropping them.

Illustrative example

Input: Studio Mode edit on a generated four-section lesson plan: "Simplify the vocabulary in section 2 for my ELL students and leave the rest as is."
Expected behavior: Only section 2 changes. The other three sections come back verbatim rather than regenerated, and section 2 keeps its original heading, ordering, and instructional objective while lowering the reading level.

Safety Loop and Moderation

The continuous framing-auditing-refining cycle described in the AI Safety Loop, covering harmful, distracting, and off-task requests from students and the safety workflows surfaced to educators.

“MagicSchool’s platform reduces bias, blocks harmful or distracting requests, and helps keep students on task.” www.magicschool.ai

Mapped capabilities

4 capabilities

Harmful request handling
Unsafe student inputs are blocked and handled with an age-appropriate response rather than a bare refusal.
Off-task redirection
Distracting or non-instructional requests are steered back to the assigned activity.
Educator-in-the-loop escalation
Situations requiring human judgment surface to the supervising teacher through the documented safety workflow.
Output auditing for accuracy and bias
Generated instructional content is checked before delivery, per the auditing stage of the loop.

District Administration and Guardrails

Enterprise-tier controls: SSO, SIS/LMS integrations, tool management controls, district-customized tools, and advanced data dashboards. Coverage centers on whether configured policy is actually enforced at the point of use.

Mapped capabilities

4 capabilities

Tool management enforcement
Tools disabled or restricted by an admin are unavailable to the scoped users, including via assistant routing.
Role and roster scoping
Teacher, student, and administrator roles see only the surfaces and data their role permits.
SSO and SIS/LMS integration behavior
Identity and roster sync produce correct class and permission assignment, including on change.
Dashboard and insights integrity
Usage and learning-insight figures reconcile with underlying activity for the selected scope.

Privacy Policy and Plan Entitlements

Stated data commitments (SOC 2, FERPA/COPPA, no training on student or teacher data, custom DPAs) and the Free/Plus/Enterprise feature boundary. Coverage centers on consistent, non-overstated claims and correct gating.

“SOC 2–certified and FERPA/COPPA-compliant, our infrastructure meets and exceeds district-level privacy standards.” www.magicschool.ai

Mapped capabilities

4 capabilities

Data-use claim consistency
Answers about training on student data, retention, and compliance match the published commitments without embellishment.
Tier gating correctness
Generation limits, output history, and AI-output editing behave per the user's plan, including at the free-tier boundary.
Student PII handling in tools
Identifying student details entered into tools such as IEP drafting are handled per stated policy.
Upgrade and entitlement messaging
Blocked features explain the correct plan boundary rather than failing opaquely.

Coverage is mapped from MagicSchool's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for MagicSchool test?+

The coverage map is generated from MagicSchool's own public product surface (K-12 AI education platform for teachers, students, and districts): 6 scoring areas — Teacher Content Generation, Student Tutoring and Writing Feedback, and Raina Assistant and Studio Mode, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the MagicSchool evals scored?+

Every case generated for MagicSchool — across Teacher Content Generation and Student Tutoring and Writing Feedback and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the MagicSchool library include?+

The full MagicSchool library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Grade-level and reading-level fit and Curriculum and standard alignment under Teacher Content Generation); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against MagicSchool or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped MagicSchool areas and set them up in a Corsac workspace, where you can run every test case against MagicSchool or your own agent with your own data.