Human-in-the-Loop AI for Cheating Ring Detection in Online Exams

Core Concepts

Developing a human-in-the-loop AI system to detect and deter cheating rings in online exams, ensuring integrity and fairness.

Abstract

Abstract:

Concerns about security in online exams due to professional cheating services.
Introduction of a human-in-the-loop AI system to detect cheating rings.
Emphasis on Responsible AI standards for ethical considerations.
Introduction:

Challenges of maintaining integrity in online assessments.
Development of an AI system to automatically detect potential cheating rings.
Escalation of suspicious tests to human proctors for further scrutiny.
System Overview:

Integration of the cheating ring detection system into existing online examination platforms.
Collection of data including video recordings, machine-specific information, and keystroke/mouse movements.
Focus on detecting professional cheaters controlling test takers' devices.
Keystroke and Mouse Movement Pattern Analysis:

Comparison of historical sessions with current test sessions for similarity detection.
Proctoring interface presentation for suspicious tests and historical sessions with similar patterns.
System Evaluation:

Experiment details using keystroke and mouse movement pattern analysis methods.
Dataset sampling from Duolingo English Test sessions in 2022 and 2023.
Performance comparison across different methods using AUROC, FPR, FNR metrics.
Discussion:

Limitations in the study and opportunities for further research.
Importance of integrating AI signals as evidence, not sole determinants in cheating accusations.
Adherence to responsible AI standards for privacy protection and bias prevention.

Stats

"Table 1: Performance across different groups for deep-keystroke+mouse method."
"Table 2: Performance for different methods."

Quotes

Key Insights Distilled From

Human-in-the-Loop AI for Cheating Ring Detection

by Yong-Siang S... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14711.pdf

Deeper Inquiries

How can the system ensure uniform detection of cheaters across different demographic groups?

To ensure uniform detection of cheaters across different demographic groups, the cheating ring detection system can implement measures such as fairness assessments based on equality of opportunity. By equalizing true negative rates (TNR) across various demographic categories like age, gender, and region, the system can strive for consistency in detecting potential cheating behavior. This approach involves setting thresholds that maintain similar TNR levels for all groups when identifying suspicious test sessions. Additionally, utilizing a combination of keystroke and mouse movement pattern analysis helps in enhancing the accuracy and reliability of detecting cheating rings across diverse demographics.

What are the potential biases or errors that could arise from human proctoring decisions?

Human proctoring decisions may introduce biases or errors due to subjective judgment, lack of standardized criteria for evaluating suspicious behavior, or unconscious prejudices. Biases could stem from factors such as race, gender, accent, or cultural background influencing how proctors interpret behaviors during test monitoring. Errors might occur if proctors rely too heavily on AI-generated signals without considering contextual nuances or if there is inconsistency in decision-making among different proctors. Furthermore, individual proctoring styles and experiences could lead to variations in identifying cheating patterns accurately.

How can advancements in cheating detection systems impact broader societal issues beyond academic integrity?

Advancements in cheating detection systems have implications beyond academic integrity by addressing broader societal concerns related to ethics and trustworthiness. These systems promote accountability and transparency by safeguarding the credibility of high-stakes assessments used for critical purposes like college admissions or professional certifications. By deterring dishonest practices through effective technology-driven solutions, these advancements contribute to upholding meritocracy and fairness in society at large. Moreover, they underscore the importance of responsible AI implementation by prioritizing privacy protection for individuals undergoing evaluations while combating evolving methods of deception that threaten overall societal values.