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business@cangra.com

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+91 92368 16360
+91 73077 76934
+91 74899 42288

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CANGRA Talents Pvt. Ltd.
D-1/17 vardan khand, Sector-1,
Gomti Nagar Vistar, Lucknow,
U.P., India - 226010
Ph: +91 73077 76934

GSTIN
09AAHCC0345A1Z7

Regd. Address

CANGRA Talents Pvt. Ltd.
1, Basant Vihar, Indira Nagar,
Sec-14, Lucknow,
U.P., India - 226001
Ph: +91 522 2714999

Company CIN
U74999UP2017PTC093778

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AI Against Online Exam Cheating

  1. The prevalence of cheating

    Online tests are also part of recruitment nowadays. The factor has streamlined the testing of various types of skills on applicants in one go but has also created issues with fraudulent cases. It is found that about 50% of applicants try to cheat when online assessments are conducted.

  2. The aftermath of cheating

    With cheating in online assessments, severe effects result. The candidates shall be disqualified and undergo reputational dragging. To the employers, it may lead to the employment of less qualified candidates; thus wasting valuable time and resources.

  3. The role of AI in cheating detection

    This cheating in the assessment would now be very much detected through AI assistance. AI checks on behavior, test results, and other external factors to profile suspicious activities likely to manifest as cheating.

  4. Proctoring tools powered by AI

    Currently, a significant number of online tests use AI-based tools for assessment, particularly proctoring, which can catch most cheating behaviors in the form of unauthorized materials, peer answer sharing, and impersonation.

  5. AI-powered anomaly detection

    AI algorithms can even identify behavioral anomalies that indicate cheating. For example, if the performance of a candidate improves drastically, then his cheating might have been done with unauthorized help at the time of test.

  6. Future of AI in online assessments

    To make the detection of cheating increasingly sophisticated, only with the advancement of AI technology will the integrity of recruitment be safeguarded, and the element of fairness ensured both for the employer and the candidate.