Discovery
Did the learner ask the questions needed to understand the client's full situation before recommending anything?
Grading
How grading works on WealthSimAI
Every WealthSimAI session produces two separate grades: one for the advisory conversation and one for the portfolio built during it. Both are generated automatically, both are visible to the learner's professor, and a professor can review or override either grade at any time.
The conversation rubric
Every advisory conversation is evaluated on five factors.
Suitability
Does the recommendation actually fit the goals, constraints, and risk tolerance uncovered during discovery?
Clarity
Did the learner explain their reasoning and the recommendation in a way the client could understand?
Empathy
Did the learner acknowledge the client's concerns and emotional reactions, not just their financial facts?
Accuracy
Is the guidance the learner gave technically correct?
Portfolio grading is separate
Every simulated trade and portfolio is scored separately, from 0 to 100, against the client's mandate: risk alignment, mandate fit, diversification, and position size. Communication quality and investment decisions are always scored on their own tracks, never blended into one number.
Professor oversight
Review by exception
Professors can review and override any AI-generated grade, and a class can be set to require review before a grade releases. To keep review time manageable, only grades flagged as borderline or unusual land in the review-by-exception queue, so professors are not re-grading every conversation by hand.
Moment-tied coaching
Alongside the grade, learners receive coaching items tied to specific moments in the conversation, concrete next-time-try notes linked back to the relevant learning topic, rather than one generic comment.
Grading FAQ
No. Conversations are evaluated on a five-factor advisory rubric: discovery, suitability, clarity, empathy, and accuracy. Portfolios and trades are graded separately, 0 to 100, against the client's mandate for risk alignment, mandate fit, diversification, and position size.
Yes. Professors can override any AI-generated grade for a student in their class, and a class can be configured to require professor review before a grade is released to the student.
No. WealthSimAI surfaces a review-by-exception queue that flags only the grades that look borderline or unusual, so professors can focus their time there instead of re-checking every conversation.
Beyond the grade itself, students get coaching items tied to specific moments in the conversation, plus a progress report drafted by AI and approved by their professor before release.
See the rubric in practice
Request beta access to run a graded scenario yourself.