For business professors
Your questions. Your courses. Your way of working. Advanced AI and practical guidance, shaped around the context that matters to you.
The starting point
You choose what to share, what to change and what to carry forward.
Capable models. Directed workflows. Hands-on guidance.
Research
From an analytical model to an empirical study or a funding proposal, start with the question you want to answer. Test the argument and the evidence against your target's criteria, and plan the next revision, which you write.
A table entry disagrees with the stated expression.
Correct the entry, regenerate the table and check the remaining rows against the same expression.
For analytical work, examine assumptions, proofs, comparative statics and numerical results. For empirical work, examine research design, identification, measurement, robustness and interpretation.
Every project starts as a pilot. For analytical papers in LaTeX, the pilot uses our Readiness Review engine, which runs end to end today. For empirical papers, proposals and other projects, we agree on the approach with you first.
Get feedback informed by the published criteria of your target journal and department, and a revision plan you carry out. You keep the contribution and the intellectual choices.
Management Science · M&SOM · Operations Research · POM · ISR · MIS Quarterly (MISQ)
Bring a funding proposal into focus: question, significance, work plan and presentation, read against the funder's stated criteria. We check the funder's rules on AI use with you before starting, and the proposal stays yours to write.
A prioritized report, evidence for each finding and a revision plan you can review. The review works from your LaTeX source and any figure code you include.
For example, a Management Science submission is read against the department you name. A clean audit does not certify correctness or predict acceptance.
Teaching
Your students already use AI. We bring course-design skills and agentic workflows that help you decide what students should practice themselves, where AI belongs and how to build the course around that decision. You review everything before it reaches students.
Decide what students should practice on their own and where AI belongs. Our redesign method builds on the two-lane approach from published assessment guidance: a few secured assessments show what each student can do alone, and the rest are open to AI and designed for learning. Activities, assessments and rubrics then ask students to explain and defend their reasoning.
Develop lectures, slides, worked examples, assignments and rubrics around a clear learning goal. Agentic workflows draft; you review, test and decide what students see.
Shape scattered notes into a coherent course package, with a clear sequence, consistent terminology and an instructor guide.
Create a custom classroom simulation with roles, decision rounds and instructor controls. Use the debrief to connect what happened in the room to the ideas in the course.
Teaching pilots work from your course materials only: no student work, grades or rosters. We do not use AI-detector scores, and we do not promise that an assignment cannot be done with AI or that learning will improve.
Request a teaching pilotAn illustrative module
“Students can ask AI to solve the spreadsheet. Can they explain and defend the decision?”
A short explanation and a worked example.
A supply-chain simulation with competing objectives.
An assignment and rubric that ask students to explain the tradeoffs.
A debrief and an instructor guide with solution checks.
Workflows
Practical lessons and hands-on guidance with the skills and agentic workflows we develop, applied to your own research or teaching. We help you choose the tools, give them useful context and check what comes back.
Define the question, audience and constraints. Decide what a useful result would look like.
Choose relevant materials, methods and preferences. Inspect and correct the context before reusing it.
Give agents defined tasks. Examine evidence, test outputs and adjust the workflow as you learn.
You own the questions, research taste and learning goals. Verify the work and choose what to use.
As a pilot: one-to-one sessions on a project of yours, or a short session for a small group.
Request workflow guidanceYou may already have a campus AI license, an AI review tool, an editor you trust or a teaching center. Each is useful. Here is what Co-Professor adds.
Good for many questions, and you may already have one. Co-Professor adds structure. For analytical papers: fact-checked profiles of journals and departments built from their published statements, citations looked up in bibliographic records, and a separate pass that tries to refute each finding before you see it. For courses: a staged redesign method in place of a blank prompt. Workflow guidance also helps you get more from the license you already have.
Several check a manuscript line by line, and some do it well. We see them as a complement. For analytical papers, Co-Professor works from your LaTeX source and figure code, reads the draft against the criteria of the journal or department you are targeting, and returns a revision plan you carry out. It also reaches beyond the paper, to proposals, courses and the way you work.
A skilled human reader is worth having, and we do not replace one. Generic editing services rarely specialize in analytical OM, IS and OR models. Co-Professor is built by a business-school professor for business-school research and teaching and, where you want it, leaves you with workflows you can run yourself. Each pilot is scoped with you before it starts, at the prices below.
Workshops teach principles, and built-in tools draft materials. Co-Professor works through your own course with you: which assessments show what students can do alone, where AI belongs, and the activities, rubrics and AI policy that follow.
Founding prices for our first pilot clients, in US dollars. Nothing is charged until we have agreed on the scope with you.
Free
One per paper
A first look at your title, abstract, introduction and contribution: fit with your target journal or department, and the main risks to address before you submit.
$69
Per paper, founding price
A full review of one paper against the published criteria of your target journal, with a prioritized revision plan you carry out. Money-back guarantee if the review is not useful to you.
From $149
Per course
A redesign of one course for students who use AI: which assessments show what students can do on their own, where AI belongs, and the activities, rubrics and AI policy that follow. Department programs are quoted separately.
Quoted
After a short scoping call
One-to-one sessions on a project of yours, or a short session for a small group, with the skills and agentic workflows we develop.
Once the scope is agreed, we send a secure payment link or an invoice. There is no checkout on this page. Request a pilot
We are taking a small number of pilot projects in research, teaching and AI workflows. Tell us about yours in a few lines. We reply by email to agree on the scope, the materials, who sees them, the timing and the price. Nothing starts until you agree.
Request a pilot at https://www.fashengxu.com/co-professor/
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Build a project brief: choose a starting point, add your context and leave with an editable brief. It stays in this browser tab until you copy it, download it or add it to your request. Nothing is sent on its own.
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You can switch starting points without losing what you have written.
Your work, your context and your decisions stay central.
Two things. You can request a pilot with the form above, and you can build a project brief to send with it. The brief tool runs in your browser and sends nothing on its own; the form sends only what you type into it. There are no accounts, uploads or payments on this site. Every project starts as a pilot. For analytical papers in LaTeX, the pilot uses our Readiness Review engine, which runs end to end today. For empirical papers, proposals, teaching and workflow projects, we agree on the approach with you first.
You describe the project in a few lines. We reply by email to agree on the scope: what you want to resolve, the materials, who can see them and for how long, which AI services are used, the timing, what you receive and the price. Work starts only after you agree. A pilot is one defined project, such as one paper, one proposal, one course redesign or a short series of workflow sessions. At the end we ask what was useful and what was not.
The Intro Check is free, one per paper. A paper review pilot is $69 per paper (founding price, with a money-back guarantee if the review is not useful to you). A course redesign pilot starts at $149 per course, and department programs are quoted separately. Agentic workflow coaching is quoted after a short scoping call. Nothing is charged until we have agreed on the scope; we then send a secure payment link or an invoice. There is no checkout on this page. Some projects depend on AI services that are offered only in the regions their providers support.
Describe your materials in the request; do not paste or attach them. If a pilot needs files, we agree on how they are shared, who can see them, which AI services process them and when they are deleted, before you send anything. Research help is for authors and authorized collaborators; manuscripts received in confidence for peer review are not accepted. Teaching pilots work from course materials, never student work, grades or rosters.
You do. Check proposed revisions, citations, solutions and teaching materials before you use them. Feedback carries uncertainty and does not certify correctness or predict acceptance, and a report can say “not yet”. Co-Professor does not write your paper or proposal; you keep the contribution and the intellectual choices. Follow the AI-use and disclosure rules of your journal, funder and institution.
No. Co-Professor is independent and is not affiliated with or endorsed by any journal, publisher, society or university. Journal names on this page describe the targets our profiles cover, and feedback draws on what journals publish about their own criteria, not on inside knowledge.