Rao, S1
1Corresponding Author: Sohail Rao, MD, MA, DPhil. HBond Foundation, 6819 Camp Bullis Road, San
Antonio, Texas 78256, USA. E-mail: [email protected]
ABSTRACT:
Artificial Intelligence (AI) is transforming the landscape of admissions into professional schools, including medicine, dentistry, pharmacy, law, and engineering, by offering data-driven, unbiased, and efficient selection models. The integration of AI into admissions processes has the potential to eliminate manual inefficiencies, reduce bias, improve test security, and enhance candidate evaluation. AI-driven solutions such as automated application screening, predictive analytics, and remote proctoring are already being implemented in leading universities and professional schools across North America, Europe, and Asia, significantly improving transparency, accessibility, and fairness in competitive admissions (AAMC, 2022; Harvard Medical School, 2023; CaRMS, 2022; ADEA, 2023).
In Pakistan, the Medical and Dental College Admission Test (MDCAT) serves as the primary standardized exam for students aspiring to enter medical and dental schools. However, MDCAT and other entrance exams for professional schools face serious challenges, including test security breaches, inconsistent evaluation criteria, biases in candidate selection, and disparities in preparation resources (PMDC, 2023; Dawn, 2024; Ramesh et al., 2020). Reports from MDCAT 2023 highlighted paper leaks, irregularities in scoring, and widespread protests, further underscoring the need for technological intervention to restore credibility in the admissions process (Dawn, 2024). AI-powered adaptive learning platforms can help level the playing field by offering personalized test preparation, while automated candidate screening and AI-driven scoring models can improve fairness and efficiency in admissions decisions (Ahmed et al., 2021; Gupta et al., 2022).
Beyond Pakistan, AI is revolutionizing medical, dental, and other professional school admissions worldwide. Leading institutions such as Harvard Medical School (HMS), Association of American Medical Colleges (AAMC), Canadian Resident Matching Service (CaRMS), and ADEA have successfully integrated AI-powered applicant ranking systems, holistic candidate evaluations, and fraud detection technologies to ensure merit-based and objective selection criteria (AAMC, 2022; HMS, 2023; CaRMS, 2022; ADEA, 2023). AI is also being used to enhance security in standardized exams such as the MCAT, DAT, LSAT, and GPAT, where AI-driven proctoring tools ensure that test integrity is maintained through facial recognition, behavior analysis, and keystroke monitoring (Gupta et al., 2022; European Commission, 2023).
This article explores the role of AI in optimizing MDCAT admissions, with an emphasis on improving test security, enhancing candidate evaluation, and reducing human bias. Additionally, it examines how AI-powered models are transforming admissions across all professional schools, providing actionable insights for Pakistan and other regions seeking to adopt AI-driven admissions frameworks. The findings highlight best practices from global institutions, offering a roadmap for policymakers, educators, and AI developers to enhance equity, accessibility, and transparency in high-stakes professional school admissions.
INTRODUCTION:
Admissions into professional programs such as medicine, dentistry, pharmacy, law, and engineering are highly competitive worldwide, requiring rigorous candidate evaluations to select the most capable and deserving students (Chen et al., 2021; Ahmed et al., 2021). The admissions process for these fields is traditionally based on standardized entrance examinations, academic performance, and interviews, with the goal of ensuring that only the most qualified candidates enter these critical professions. However, legacy admissions models face growing criticism due to bias, inefficiencies, and accessibility disparities, which prevent a truly meritocratic selection system.
In Pakistan, MDCAT serves as the primary standardized exam for students aspiring to enter medical and dental schools (PMDC, 2023). However, numerous controversies and inefficiencies have raised serious concerns about the credibility of MDCAT, including:
- Widespread test leaks and fraud in MDCAT 2023, which led to student protests, legal challenges, and calls for reform (Dawn, 2024).
- Bias in selection processes, disproportionately affecting students from rural areas and lower-income backgrounds who lack access to private coaching, preparatory materials, and digital resources (WHO, 2023).
- Inconsistencies in manual scoring and subjective evaluation, increasing the risk of human error, favoritism, and unfair admissions decisions (Ramesh et al., 2020).
These challenges are not unique to MDCAT but extend to admissions in other professional fields. Entrance exams such as the National Dental Admission Test (DAT) for dental schools, the Law Admission Test (LAT) for law schools, and the Graduate Pharmacy Admission Test (GPAT) for pharmacy schools also suffer from bias, inefficiencies, and disparities in access to preparation resources, making it harder for underprivileged students to compete on an equal footing (ADEA, 2023; U.S. Department of Education, 2023). These systemic issues highlight the urgent need for AI-driven reforms that can standardize admissions, eliminate human bias, enhance test security, and improve candidate evaluation methodologies.
The Global Shift Toward AI in Admissions:
In response to these challenges and inefficiencies, leading universities and professional schools worldwide are adopting AI-powered admissions models to increase fairness, efficiency, and transparency in candidate selection. Institutions in North America, Europe, and Asia are using machine learning algorithms, automated screening tools, and predictive analytics to:
- Reduce bias by using AI-powered candidate evaluations that focus on objective performance metrics rather than socio-economic backgrounds (AAMC, 2022; Ramesh et al., 2020).
- Improve accuracy and efficiency by automating the application review process, eliminating human errors in scoring, and ensuring merit-based selection (HMS, 2023; CaRMS, 2022).
- Enhance test security by implementing AI-driven remote proctoring systems that detect fraud, impersonation, and suspicious behavior during exams (Gupta et al., 2022; European Commission, 2023).
For example, HMS, AAMC, CaRMS, and ADEA have successfully integrated AI-driven holistic applicant evaluations, fraud detection tools, and automated ranking systems to ensure unbiased, merit-based admissions processes (AAMC, 2022; HMS, 2023; CaRMS, 2022; ADEA, 2023). Furthermore, AI is already being used to enhance security in standardized exams such as the Medical College Admission Test (MCAT), DAT, the Law School Admission Test (LSAT), and GPAT, where AI-powered test proctoring tools leverage facial recognition, behavior analysis, and keystroke monitoring to maintain test integrity (Gupta et al., 2022; European Commission, 2023).
AI in Pakistan’s Professional School Admissions:
While developed nations have begun embracing AI-driven admissions frameworks, Pakistan has yet to fully integrate AI into its medical, dental, and professional school admissions processes. The MDCAT 2023 controversy underscores the urgent need for AI-driven reforms to:
- Automate candidate screening, ensuring merit-based admissions without human bias.
- Enhance test security, eliminating cheating and fraud in MDCAT and other professional exams.
- Expand accessibility to AI-powered learning tools, allowing students from all backgrounds to compete on an equal footing.
This article evaluates how AI-powered admissions models can be applied to Pakistan’s MDCAT, dental school admissions, and other professional programs, while also analyzing global AI-driven selection frameworks for potential adoption. By examining successful AI implementations in North America, Europe, and Asia, this study provides actionable recommendations for policymakers, educators, and technology developers to enhance equity, transparency, and efficiency in professional school admissions in Pakistan and beyond.
METHODS:
This study employs a systematic literature review to examine the role of AI in professional school admissions, focusing on medical, dental, pharmacy, law, and engineering programs. The research methodology consists of three main components: analyzing MDCAT’s existing challenges, reviewing AI applications in professional school admissions, and assessing AI-driven exam security and fraud prevention measures.
The first component involves an analysis of MDCAT’s current challenges and regulatory framework. Official guidelines from the Pakistan Medical and Dental Council (PMDC) (2023) were reviewed to understand MDCAT eligibility criteria, syllabus structure, and scoring methodologies. Additionally, reports on MDCAT 2023 irregularities, including paper leaks, security breaches, scoring inconsistencies, and student protests, were examined to highlight systemic flaws in the current admissions process (Dawn, 2024). These reports provide insight into how weaknesses in MDCAT administration have affected fairness, transparency, and merit-based selection, strengthening the argument for AI-driven interventions.
The second component of this study focuses on AI applications in medical, dental, and other professional school admissions. A review of peer-reviewed studies, institutional reports, and industry white papers was conducted to evaluate the use of predictive analytics, automated grading, and AI-driven bias detection models in candidate selection (Ahmed et al., 2021; AAMC, 2022; ADEA, 2023). AI-powered application screening tools were also analyzed, particularly those used by HMS, ADEA, and CaRMS to streamline application review processes and reduce human bias (HMS, 2023; CaRMS, 2022). These AI systems have demonstrated significant improvements in efficiency, fairness, and candidate evaluation accuracy, providing a potential roadmap for integrating similar models into MDCAT and other entrance exams in Pakistan.
The final component of the study examines AI-driven exam security and fraud prevention measures, which have become increasingly relevant in standardized testing environments. AI-powered remote proctoring tools, including facial recognition, keystroke analysis, and behavioral monitoring, are now widely used in high-stakes exams such as the MCAT, DAT, LSAT, and GPAT to detect cheating, impersonation, and security breaches (Gupta et al., 2022; European Commission, 2023). This study reviews how AI-enhanced security systems have improved test integrity in these standardized exams and assesses their applicability in securing MDCAT and other professional school entrance tests.
By systematically reviewing current AI applications in admissions and standardized testing, this study aims to identify best practices and recommend AI-driven solutions that can enhance transparency, efficiency, and fairness in Pakistan’s MDCAT and other professional school admissions. The findings will help policymakers, educators, and AI developers understand how advanced AI models can be leveraged to address longstanding challenges in medical, dental, and professional school admissions both in Pakistan and globally.
RESULTS:
The findings of this study highlight the transformative role of AI in professional school admissions, particularly in test preparation, exam security, and candidate evaluation. AI-driven technologies are enhancing accessibility, improving fairness, and ensuring data-driven decision-making in medical, dental, pharmacy, law, and engineering school admissions worldwide.
AI-Enhanced Test Preparation for MDCAT, DAT, and Other Entrance Exams:
AI-powered adaptive learning platforms are revolutionizing test preparation by providing personalized study plans, real-time feedback, and intelligent tutoring tailored to each student’s strengths and weaknesses. Unlike traditional study methods, which rely on generic content and standardized curriculums, AI-based test prep software dynamically adjusts difficulty levels, recommends study resources, and predicts student performance based on historical data and real-time engagement (Hussain et al., 2023). This level of personalization significantly improves student performance and retention rates, especially for high-stakes entrance exams such as MDCAT, DAT, and other professional school exams.
In North America, AI-driven MCAT and DAT preparation programs have demonstrated higher success rates by utilizing predictive performance analytics and customized learning pathways (HMS, 2023; ADEA, 2023). These systems analyze student progress, identify knowledge gaps, and adjust study materials accordingly, ensuring that students are well-prepared for their exams. Implementing AI-enhanced learning platforms in Pakistan’s MDCAT and other entrance exams could help bridge the resource gap, allowing students from diverse socio-economic backgrounds to access high-quality, personalized test preparation tools.
AI-Driven Remote Proctoring for Test Security:
The integrity of standardized entrance exams is a growing concern, with frequent reports of cheating, impersonation, and security breaches undermining public trust in admissions processes. AI-powered remote proctoring solutions provide a highly effective means of monitoring exams, ensuring fairness and credibility. These systems utilize real-time facial recognition, anomaly detection, and keystroke pattern analysis to identify suspicious behavior and prevent cheating and identity fraud during exams (Gupta et al., 2022).
Many professional entrance exams in North America and Europe, including the MCAT, LSAT, and DAT, have already integrated AI-based fraud detection technologies to enhance test security (AAMC, 2022; ADEA, 2023). These systems automatically flag irregular behaviors, such as eye movement deviations, unusual keyboard strokes, and unauthorized background noises, allowing proctors to intervene in real time. By incorporating AI-driven test security into MDCAT and other entrance exams in Pakistan, regulatory bodies such as PMDC can prevent exam fraud, enhance transparency, and restore public confidence in the admissions process.
AI for Candidate Screening in Medical, Dental, and Other Professional Schools:
The traditional admissions process for professional schools relies on manual application reviews, subjective assessments, and interviewer bias, often leading to inconsistent and inequitable candidate selection. AI-powered application screening and ranking models provide a data-driven alternative, ensuring merit-based, objective admissions decisions (Chen et al., 2021). These systems evaluate applicant records, test scores, personal statements, and interview responses, using machine learning algorithms to rank candidates based on predicted success rates.
In Canada and the United States, institutions such as CaRMS and AAMC use AI-driven predictive analytics to identify long-term student success indicators, ensuring that admissions decisions are based on data rather than subjective human judgment (CaRMS, 2022; Ramesh et al., 2020). By adopting AI-powered candidate evaluation models, medical, dental, and other professional schools in Pakistan can create a fairer, more objective admissions framework, reducing bias in selection criteria and improving diversity in professional programs.
The results of this study demonstrate that AI-driven solutions have the potential to revolutionize professional school admissions by:
- Enhancing accessibility to test preparation through AI-powered adaptive learning systems, ensuring personalized, high-quality education for students regardless of socio-economic background.
- Improving test security using AI-driven remote proctoring and fraud detection technologies, which can significantly reduce cheating and exam irregularities.
- Automating and optimizing candidate screening, making admissions processes more data-driven, objective, and fair while reducing human bias in selection.
By integrating AI into MDCAT, DAT, and other entrance exams, Pakistan and other developing nations can modernize their admissions processes, ensuring greater transparency, fairness, and efficiency in professional school selections.
CONCLUSION:
The integration of AI-driven admissions models has the potential to transform MDCAT, dental school admissions, and other professional school selection processes by ensuring merit-based, unbiased, and data-driven candidate evaluations. Traditional admissions systems, which often rely on manual application reviews, subjective assessments, and outdated selection methodologies, can be replaced with AI-powered evaluation tools that provide greater accuracy, efficiency, and fairness. As seen in North America and Europe, institutions such as HMS, AAMC, ADEA, and CaRMS have successfully implemented AI-based selection models to improve transparency, security, and long-term student success (HMS, 2023; AAMC, 2022; ADEA, 2023). These case studies provide valuable insights into best practices for AI adoption, which can serve as a roadmap for integrating AI into MDCAT and other professional school admissions in Pakistan.
One of the key advantages of AI in admissions is its ability to reduce human bias and improve accessibility. Socioeconomic disparities often hinder access to quality test preparation resources, leading to unequal opportunities for students from underprivileged backgrounds. AI-powered adaptive learning platforms can bridge this gap by providing personalized study materials, real-time feedback, and intelligent tutoring systems, ensuring that all students have access to high-quality exam preparation regardless of their financial background (Hussain et al., 2023). Additionally, AI-driven application screening models ensure objective candidate selection, eliminating biases related to gender, socioeconomic status, or geographic location (Chen et al., 2021). By adopting these technologies, Pakistan can create a more equitable admissions system, allowing students to be evaluated solely on their merit and potential rather than external socio-economic factors.
Furthermore, AI can play a critical role in addressing test security challenges, which have been a recurring issue in MDCAT and other entrance exams. The MDCAT 2023 controversy, where paper leaks and exam irregularities led to student protests and re-evaluations, highlights the urgent need for a robust, AI-driven security framework (Dawn, 2024). AI-powered remote proctoring systems using facial recognition, keystroke analysis, and behavioral monitoring have already been deployed in MCAT, LSAT, and DAT to prevent cheating, impersonation, and test fraud (Gupta et al., 2022; AAMC, 2022). Implementing similar AI-driven exam security measures for MDCAT and other Pakistani professional entrance exams can help restore credibility to the admissions process, ensuring fairness and integrity in candidate evaluations.
However, successful AI adoption in Pakistan’s MDCAT and professional admissions system will require collaboration between multiple stakeholders, including PMDC, universities, policymakers, and AI technology developers. Policymakers must develop regulatory frameworks that govern the ethical use of AI in admissions, ensuring data privacy, fairness, and transparency in AI-driven candidate evaluations (European Commission, 2023). Universities and educational institutions must invest in AI-powered learning and testing infrastructure, while AI developers must focus on building culturally and contextually relevant AI models that align with Pakistan’s specific educational challenges.
Ultimately, the adoption of AI-driven admissions models can lead to a more transparent, efficient, and meritocratic professional school selection process. By leveraging global best practices and adapting them to Pakistan’s unique challenges, AI has the potential to revolutionize MDCAT and other entrance exams, ensuring that the next generation of professionals is selected through a system that prioritizes fairness, academic merit, and long-term success. The findings of this study underscore the need for immediate action in incorporating AI-driven technologies to enhance equity, security, and efficiency in medical, dental, and other professional school admissions.
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