Ensuring integrity: Hong Kong ICAC releases a comprehensive corruption prevention guide for student admissions

This investment comes with equally high public expectations regarding institutional governance, particularly in critical processes like student admissions. Any instance of corruption or malpractice could severely undermine public trust in TEIs and damage Hong Kong’s hard-earned reputation as a premier international education hub. It is therefore imperative that TEIs implement robust safeguards to guarantee the integrity and fairness of the student admission process.

This brings us to a crucial question: How can TEIs ensure integrity and fairness in the student admission process? The answer pops out with the name of a renowned organisation in Hong Kong: The Independent Commission Against Corruption (ICAC).

The Corruption Prevention Department (CPD) of the ICAC is statutorily mandated to carry out corruption prevention work across all sectors in Hong Kong. This includes reviews of operational systems and procedures of government departments and public bodies, with the authority to recommend procedural revisions to reduce potential corruption risks. 

Corruption Prevention Guide on Admission of Students for Tertiary Education Institutions

CPD has developed a comprehensive Corruption Prevention Guide on Admission of Students for Tertiary Education Institutions (the Guide). While tailored to Hong Kong’s context, the Guide also addresses universal challenges in maintaining integrity and offers best practices applicable to TEIs worldwide. 

Key features

The Guide covers relevant anti-corruption legislation and includes case studies which illustrate major corruption risks, common inadequacies, and corruption prevention red flags in the admission processes of both undergraduate and postgraduate programmes.

The Guide also provides practical guidance on the preventive measures for each key stage, including: application handling, applicant shortlisting, assessment protocols, qualification verification, and an oversight mechanism.

Leveraging technology such as Artificial Intelligence (AI) and big data to detect fake academic credentials

The Guide promotes various technology-driven recommendations to enhance the detection of fake academic credentials and corruption prevention during the key stages in the student admission process, such as:

Receipt of applications and communications with applicants

• Enable receipt of applications in the computer system or web-based platform with automatic controls built into the process, e.g. disallowing any alteration of application information or receipt of any new application after the application deadline, maintaining audit trails throughout the receipt process, and analysing Internet Protocol (IP) addresses of applicants to detect irregularities (e.g. more than one application submitted from the same IP address).

Shortlisting and assessment of applications

• Uphold accountability and facilitate objective assessment, capture in the computer system all the scoring information throughout the shortlisting and assessment process, including the scores in the written tests or interviews by individual interview panel members and any alteration to the scores with justifications provided;
• Adopt technologies (e.g. AI) to facilitate online interviews with applicants and analyse applicants’ performance; and
• Enable automatic calculation of the overall combined scores and determination of the ranking order list.

Verification of qualifications

Use technologies to assist the checks conducted by TEI staff. For example: 

• Use facial recognition technology to compare the portraits of the applicant appearing on the application form, identity documents, academic credentials and online interviews (if applicable) to detect any impersonation or other malpractices;
• Use AI or big data technologies to detect any irregularities in the information provided in the applications and online interviews, such as resemblance of scores/grades attained by different applicants, email domain of the referees on reference letters, inclusion of AI-generated answers, and similar/same typos or other mistakes made in the same batch of applications.

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