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Challenge

Control Systems Engineering Upskilling Simulator with CrewAI

Upskilling junior control engineers to operate modern software-driven automation systems requires hands-on interactive scenario evaluations. Build a multi-agent training simulator using CrewAI where dynamic instructor and evaluator agents challenge students with live control logic debugging tasks. Achieve an objective score grading consistency rate of 95% across training scenarios.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct an interactive control engineering training sandbox using CrewAI agents to generate, grade, and feedback PLC debugging scenarios.

Delivery guide

How work is evaluated

Evaluation

Evaluates consistency and quality of automated grading feedback provided on student control code submissions.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

passing_evaluation

Ensure student code fix is scored accurately above pass threshold

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

grading_consistency

Consistency score of automated AI evaluator relative to expert panel key • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Design CrewAI agents representing Instructor, Code Evaluator, and Feedback Coach

  • Simulate fault injection scenarios in industrial control code (e.g., race conditions, deadlock)

  • Provide objective grading feedback with actionable recommendations

Resources and assets

Reference links and supporting material

Dataset notes

Collection of 20 industrial control bug scenarios and verified student submission samples.

How this agent runs

Evaluates consistency and quality of automated grading feedback provided on student control code submissions.

Challenge input

JSON containing injected bug scenario and student code submission

CrewAI

Enables multi-agent role play between instructor and code evaluator roles.

Evaluated output

JSON evaluation score (0-100) and structured feedback summary

Checks for
  • Ensure student code fix is scored accurately above pass threshold
  • Consistency score of automated AI evaluator relative to expert panel key • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Control Engineering Education Suite (EDU-CTRL-2025)
  • Grading Consistency target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional

Evaluation contract

  • passing_evaluation · Weight 1
  • grading_consistency · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Control Systems Engineering Upskilling Simulator with CrewAI