Jeddle vs ChatGPT for marking: which one should you use for a class set?

Details about other products were checked on 28 July 2026. Other providers change their features and pricing, so check their site before deciding.

What is a general assistant like ChatGPT genuinely good at?

Start with what it does well, because plenty of the online comparisons skip that part. ChatGPT is quick, flexible and always awake. It will draft a task sheet, rewrite an explanation three ways for three reading levels, turn a set of marking notes into revision notes or study guides, pull comprehension questions out of a stimulus, and knock out a parent email in the tone you ask for. For a head of department building a unit on a Sunday night, that is real value and it is worth paying for.

It is also better at holding context than it used to be. OpenAI’s Projects keep chats, files and instructions together in one workspace. Custom instructions let you set standing guidance about how you want responses written. Memory can carry details between conversations. So a general assistant, set up carefully, will happily mark a handful of pieces to a standard you have described to it, and do a passable job.

Does ChatGPT still make you paste the rubric in every time?

No, not if you set it up properly. A Project can hold your rubric, your success criteria and two or three exemplar responses as uploaded files, along with written instructions about band descriptors and the tone you want. Custom instructions apply across chats. Any comparison still claiming a chatbot cannot store your criteria is describing a product from a couple of years ago, and you should discount the rest of what it says.

The honest limitation is a different one, and it is about scope rather than storage. A Project stores documents for you, the teacher. It does not know that 9 English has twenty-eight students, that eleven of them submitted late, or that this is the second draft of the same task. You are still the index. Every submission you want marked has to be carried in by hand, and every response has to be carried back out and filed somewhere else.

Where does a chat window run out of road on a class set?

At about the fifth script. Marking a class is not one task repeated thirty times. It is one judgement applied thirty times, and the second half is what a chat thread handles badly. There is no class list, no per-student record, no view of where the cohort sits against a single criterion, and no trail showing what you changed before the mark went out.

Consistency is the sharper problem. These models sample from a probability distribution, so the same prompt can come back worded differently on a different run. A published study of ChatGPT on code generation found identical prompts frequently produced different outputs, and that dropping the temperature to zero did not guarantee determinism. Marking is not code, but the mechanism is the same one, and it works against you when the whole point is treating thirty students the same way.

That matters because consistency is not a preference in Australian schools, it is part of the job. AITSL’s material on assessment describes teachers making consistent and comparable judgements about student achievement, with moderation as the collaborative practice that keeps standards aligned between colleagues. If you cannot reconstruct how a mark was reached, you cannot moderate it, and a scattered chat history is a poor evidence base at a faculty meeting.

What do Australian schools have to get right whichever tool they use?

The Australian Framework for Generative Artificial Intelligence in Schools sets out six principles and twenty-five guiding statements. It was published in 2023, and Education Ministers endorsed a review of it in June 2025. Two of those guiding statements settle more of this argument than any feature list. Guiding statement 5.1, human responsibility, keeps teachers and school leaders in control of decision making and accountable for decisions that are supported by the use of generative AI tools. Guiding statement 6.3 asks students, teachers and staff to take appropriate care when entering information into these tools, because it may compromise an individual’s data privacy.

Read together, those put the weight on you rather than the vendor. The mark is yours to defend. The student work you upload is yours to be careful with. So check which plan you are actually on before anything else. OpenAI’s consumer data controls include a setting called Improve the model for everyone, while its education and business products state that content is not used to train models by default. Those are different defaults, and a personal login is not a school data agreement.

Two practical points catch people out. OpenAI’s terms require users to be at least 13, and anyone under 18 to have a parent or guardian’s permission, so a chatbot account is not something you can simply hand to a Year 8 class. And ChatGPT for Teachers, the free education workspace OpenAI has built, is currently limited to verified educators at accredited United States K-12 schools and districts. Australian teachers are not eligible for it, which means the protections it carries are not the ones you are working under.

So which one should you reach for, and when?

Reach for the general assistant when the task is one-off, exploratory and low-stakes. Planning a unit, writing three versions of an explanation, sketching a rubric before you refine it, building a quiz, turning your marking themes into revision material for the class. It is fast, it is cheap, and it is good at every one of those.

Reach for a purpose-built marking platform when the task is repetitive, criteria-driven and has to survive being questioned. A class set of essays against an achievement standard is the clearest case. The Australian Curriculum says teachers use achievement standards to understand the expected standard of learning required, and to make on-balance judgements about the quality of learning a student has demonstrated. Making that judgement thirty times, consistently, with a record of it, is a workflow problem rather than a writing problem.

One quick test before you commit an afternoon to either. Count the manual steps between a student pressing submit and feedback landing in that student’s hands. If the count is small and identical for every student, the tool fits the job. If it climbs with class size, you have bought yourself a second marking task.

Where does a purpose-built marking platform actually fit?

This is the part of the comparison that is no longer neutral, so read it that way. Jeddle is built around the marking job rather than around the conversation. Classes, tasks and submissions are first-class things in the product. Your rubric and comment banks sit against the task instead of in a document you have to remember to attach. Every drafted comment and mark is put in front of you to review and edit before a student sees any of it.

That is the trade. You give up the open-ended flexibility of a chat window, and you get structure, a per-student record and a review step that does not depend on your discipline at 10pm on a Thursday. If what you want is expert essay marking that arrives as a draft rather than as a decision, that is the shape of tool to look at. If what you want is a thinking partner for planning next term, the chatbot is still the better answer, and Jeddle is not trying to be it.