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Use batch submission for classroom image jobs that can finish asynchronously, such as preparing a collection of image-generation, image-editing, or moderation requests for later review. Use a synchronous request when a student or teacher needs a result during a live lesson. Moderation should inform your workflow, not replace its safety rules or human review.

Choose based on when the result is needed

OpenAI’s Batch API processes individual requests asynchronously. Its documentation describes a completion window of up to 24 hours, often faster; that is not a promise of an immediate response or a classroom-specific turnaround guarantee. The API supports image generation, image editing, and moderation in batch jobs. OpenAI Batch API documentation

Classroom situation Suggested path Reason
A teacher queues prepared media jobs for review later Batch The results do not need to be available during the interaction.
A student is waiting for an image or moderation result in an active lesson Synchronous request A batch may take up to 24 hours to complete, so it cannot promise a live response.
A school needs to classify a collection of existing images Batch moderation may fit Moderation is a supported batch endpoint, and the Moderation API accepts image inputs.
A workflow involves known or suspected child sexual abuse material (CSAM) Do not send it to the OpenAI Moderation API OpenAI says the API is not designed for CSAM detection or handling; use dedicated child-safety safeguards.

Batch limits are service constraints, not classroom performance benchmarks. OpenAI’s current documentation lists a maximum of 50,000 requests per batch and a 200 MB input file. Each request goes on its own JSONL line, needs a unique custom_id, and one input file can target only one model. Check the current documentation before deployment because capabilities and limits may change. OpenAI Batch API documentation

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Prepare a batch that can be reconciled

  1. Group only jobs that can wait. Keep work needed for an active classroom interaction on a synchronous path.
  2. Create one JSONL line per request. Assign each request a unique, stable custom_id, and make sure the input file targets one model. OpenAI Batch API documentation
  3. Keep an application-side job map. Record which classroom task, user-facing item, and review state correspond to each custom_id. The identifier is part of the documented format; maintaining the mapping is an application-design measure for matching returned results to their origin.
  4. Show that work is pending. Design for asynchronous completion with a visible pending state and a defined route for errors, delays, or exceptions. Do not imply that submission means the result is ready.
  5. Decide what happens next. Specify whether a result can be used, needs review, or must be escalated before the job enters the queue.

Use moderation results as signals, not final policy

The OpenAI Moderation API accepts text and image inputs and returns category flags and scores. OpenAI describes these as signals for enforcing an application’s own policy: its guide gives examples such as filtering content, routing a request for review, or intervening with accounts that submit flagged content. A flag or score is not, by itself, a complete safety decision for a school. OpenAI Moderation API guide

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Define local thresholds and escalation

Decide which results can proceed automatically, which need a person to review them, and what happens when the signal is uncertain or conflicts with school rules. Moderation can produce false positives or fail to capture a school’s specific requirements. OpenAI also notes that moderation models may be upgraded; score-based custom policies may need recalibration when models or categories change. OpenAI Moderation API guide

Keep a human review path where required

Plan review staffing around the expected queue and the consequences of a decision. The API does not supply a school’s review process. OpenAI’s usage policies require human review for covered high-stakes decisions in education and restrict certain privacy-invasive uses. Those provider policies do not settle every school’s legal obligations. OpenAI Usage Policies

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Do not use this endpoint for suspected CSAM

OpenAI explicitly says its Moderation API is not designed for CSAM detection or handling and is not a substitute for dedicated child-safety safeguards. Do not route known or suspected CSAM to this endpoint. OpenAI Moderation API guide

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Set school-specific privacy and operating rules

Before launch, establish who may submit and view classroom media, what consent is required, how long inputs and results are retained, and how concerns are escalated. The applicable requirements depend on the school’s legal and contractual context; the provider policies alone do not establish a universal rule for every school or jurisdiction. Confirm the current provider documentation and applicable institutional requirements for the deployment.

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