Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a DSPy `Signature` for detecting AI-generated text. This signature should take student essay content as input and produce a boolean indicating if it's AI-generated, along with a confidence score and a brief justification. Consider key linguistic features that distinguish human from AI writing for your signature's fields.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
AI-Powered Exam Proctoring: MCP-Enabled Cheat Detection with Gemini 2.5 Pro and DSPy
The recent news from ACCA highlights the growing challenge of AI-assisted cheating in remote exams. This challenge tasks developers with building a cutting-edge, MCP-enabled agent system designed to detect and deter such academic misconduct. Leveraging the advanced reasoning capabilities of Gemini 2.5 Pro's 'Deep Think' mode and the programmatic control of DSPy, participants will create an intelligent proctoring agent capable of analyzing student behavior, textual responses, and potentially code submissions for patterns indicative of AI usage. The system will employ extended thinking techniques with adaptive reasoning budgets to conduct deep analysis of suspicious activities. MCP tool integration will be crucial for securely accessing exam platform APIs, student interaction logs, and academic integrity policy documents. This challenge emphasizes the development of robust, ethical AI systems for maintaining academic integrity in a rapidly evolving digital assessment landscape.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.