• Core Structural Shift:Shift from post-hoc synthetic text/code detection to continuous authentic process verification.
• Recommended Execution:Two-stage Submission: Automated baseline test + live in-lab whiteboard/oral invariant defense.
• Detection Fragility Warning:Commercial statistical AI text/code detectors exhibit a 34% false-positive rate on non-native English/divergent syntax. Cease punitive sanctions based solely on probabilistic classifier metrics.
Generated Academic Integrity & Assessment Brief
Intervention arithmetic, workload, and heuristic limits
Read the explanation
This saved academic matrix is an unvalidated assessment-design heuristic, not evidence of cheating by an individual student or measured learning outcomes. Its computer science fixture starts at vulnerability eighty six. Take-home code adds eight, giving ninety four with no interventions. Git chronology subtracts sixteen and the counterfactual challenge subtracts eighteen, yielding sixty. The assumed mastery score starts at thirty five, take-home code subtracts ten, then those interventions add eighteen and twenty two, giving sixty five. Fresh empty browser storage starts with both interventions checked. The paired bars show vulnerability falling from ninety four to sixty while assumed mastery rises from twenty five to sixty five. These fixed source coefficients are not causal estimates. The workload estimate scales with cohort size. Git adds zero point one five hours per student and counterfactual work adds zero point two. Subtracting the baseline half hour leaves zero point three five incremental hours. The source multiplies cohort size by that increment and by one point five, then rounds. At one hundred eighty students it gives ninety five hours; at six hundred students it gives three hundred fifteen. Vulnerability sixty and assumed mastery sixty five do not change with cohort size, because that input only affects labor. The bar uses two pixels per reported hour. This is the saved workload formula, not a validated staffing forecast; the extra multiplier is a source assumption. Intervention effects are added and then clamped. For in-class exams with all four interventions, the source vulnerability arithmetic is eighty six minus twenty five minus twenty four minus sixteen minus eighteen minus twelve, or minus nine. The clamp raises that to twelve. Assumed mastery sums to one hundred fifty three, but its cap displays ninety eight. Labor for one hundred eighty students becomes two hundred seventy hours. The institution-policy dropdown is recorded in the export but is absent from the metric formula, so changing it alone leaves these numbers unchanged. JSON and Markdown exports preserve current parameters. Reset restores the fresh computer science preset, one hundred eighty students, and prohibition metadata. The labels are editable planning metadata, not proof of compliance or student performance.
Academic AI Integrity Matrix: Mechanics & Redesign Limits
How does the simulator calculate assessment vulnerability, mastery assurance, and grading labor across disciplines?
This simulator uses heuristic scoring rather than empirical classroom metrics to evaluate academic integrity risk. For Computer Science, it starts with an 86-point base vulnerability, adds 8 points for take-home code, and subtracts 16 points for Git audits and 18 for counterfactual edge-case tasks, capping the result at 60 / 100 once loaded. Mastery assurance starts from a baseline of 35%, subtracts 10% for take-home code, and gains 18% and 22% from the two interventions to display 65%. Additional grading labor scales with cohort size according to the formula Math.round(cohortSize * (laborHoursPerStudent - 0.5) * 1.5).
Preset vulnerability scores, synthetic propensity percentages, and radar chart shapes reflect hard-coded editorial assumptions rather than peer-reviewed departmental datasets. The simulated grading labor formula assumes fixed marginal increments per intervention regardless of grading automation, teaching assistant ratios, or rubric complexity.
Try a worked example
On page load for a Computer Science cohort of 180 students with Git Trajectory Audit and Counterfactual Edge-Case Mutation checked, the tool computes labor hours per student as 0.5 + 0.15 + 0.20 = 0.85 hrs, displaying a Grading Labor Load of +95 hrs (Math.round(180 * 0.35 * 1.5)). If you uncheck '2. Granular Git Commit & Trajectory Audit', the labor overhead drops to 0.70 hrs, decreasing the displayed Grading Labor Load to +54 hrs (Math.round(180 * 0.20 * 1.5)), while Vulnerability rises from 60 / 100 to 76 / 100 and Mastery Assurance drops from 65% to 47%.
Mathematical Modeling of Assessment Vulnerability
The tool initializes each discipline with a static vulnerability index: Computer Science (86), Humanities (79), Business (72), Law (68), and STEM (62). Format modifiers adjust this base: take-home code (+8), essay synthesis (+6), problem sets (+4), case studies (+2), or proctored in-class exams (-25). Interventions reduce vulnerability by fixed amounts: oral defense (-24), counterfactual mutation (-18), Git trajectory audit (-16), and defect critique workshops (-12), clamped between 12 and 95.
Empirical evaluations of AI detection tools demonstrate significant fragility, particularly regarding false positives on non-native English writing and divergent syntax. Systematic benchmarking has shown that automated statistical detectors frequently misclassify non-native writing samples as AI-generated due to lower linguistic perplexity, underscoring the risks of using automated classifiers as tribunal evidence. GPT detectors are biased against non-native English writers
Mastery Assurance and Grading Labor Formulations
Mastery assurance is calculated starting from a baseline of 35 points, modified by assessment formats (-10 for take-home code to +35 for in-class exams) and additive intervention bonuses (+28 for viva voce, +22 for counterfactual mutations, +18 for Git audits, +15 for critique workshops), bounded between 25% and 98%.
Grading labor overhead calculates net term hours from per-student task burdens (oral: +0.35 hr, counterfactual: +0.20 hr, git: +0.15 hr, critique: +0.10 hr, in-class exam: +0.20 hr). The resulting hourly delta scales as Math.round(cohortSize * (laborHoursPerStudent - 0.5) * 1.5), which models idealized grading workflows rather than actual faculty time audits.