Sort every task in your week into one of three — before you decide on any tool
AI drafts it fully. A human still reads it and still sends it. The judgment is in the review, not the drafting.
The human decides; AI assists. The decision never leaves you — AI shortens the distance to it.
Stays human by design. Not because AI can't — sometimes because it shouldn't, and sometimes because it costs more than it saves.
Six real tasks from one Economics teacher's week, after a year of building
A daily class pulse that surfaces the students who need attention today. The most useful thing on this list — and the one that carries the most data risk, which is why it leads straight into the compliance section.
Scoring is pure SQL against a stored answer key — the model never marks anything. Scan cells that can't be read are flagged for a human, never guessed. Verified at 59/60 accuracy. The lesson generalises: when an answer is objectively checkable, check it with code, not a model.
A year ago, the single biggest time sink of the term. Now a generated first draft in house style, which a teacher edits and sends. The clearest before-and-after on this list.
AI drafts the feedback; the teacher arbitrates the final mark. The draft saves the blank page, not the decision.
Still being built — deliberately included, because the framework has to hold for a half-finished tool as well as a polished one. If the sort only works in hindsight, it isn't a planning tool.
Tried it. Abandoned it. Editing the AI's output cost more time than making the deck from scratch. Worth saying out loud at a conference full of AI enthusiasm: a task landing in "keep" is a real result, not a failure to try hard enough.
By design, not by limitation
Ranked by lowest risk and highest time saved
Sort your week, then take the list away with you
The list you copy contains no student information, so it is safe to paste anywhere. The moment you go on to build one of these, that changes — see China & Compliance before you put real marks, comments or names through a foreign-hosted tool.
PIPL, data residency, and where student work is allowed to go
Marks, written comments and at-risk flags are personal information about minors. Putting them through a foreign-hosted AI tool is a PIPL question before it is a pedagogy question — and it is a question about your school's exposure, not only your own.
The practical rule that came out of a year of building: wherever real student data is involved, default to a domestic tool. Save the foreign models for work that contains no student information at all — planning, resource drafting, your own writing.
What the compliance thinking actually reduced to
MCQ scoring runs as a database query against a stored key. No model sees the answers, so no model can be wrong about them — and nothing identifiable leaves the machine.
A report generator can write in your house style without ever being told a student's name. Strip the identifiers first and most of the compliance problem disappears with them.
Anything the system can't read confidently gets flagged for a human rather than guessed. A blank you have to fill in costs a minute; a plausible wrong mark costs a great deal more.
Built for my own week, shared as they are
Nick Marsh teaches IB Diploma and IGCSE Economics at YCIS Puxi, Shanghai, where he also leads the Commerce department. Over the past year he has built a suite of teaching tools — including a ManageBac Chrome extension, a gradebook, reporting, tracking and lesson planning app, two China-based websites for IB and IGCSE Economics, and apps for essay and MCQ feedback. These have all been designed around a deliberate decision about what AI should automate, what it should assist with, and what must stay entirely human.
马喜任教于上海耀中国际学校（浦西校区），教授IB Diploma与IGCSE经济学课程，同时担任商科组负责人。过去一年，他开发了一系列教学工具——包括一款ManageBac浏览器插件、一套集成绩册、报告撰写、学情追踪与课程设计于一体的应用系统、两个分别服务于IB与IGCSE经济学、面向中国本土师生的网站，以及用于论文与客观题反馈批改的应用。这些工具的设计始终遵循一个明确原则：清楚界定哪些工作应由AI自动完成、哪些应由AI辅助、哪些必须完全保留人工。