'... nothing we do is more important than hiring and developing people.

Letters of recommendation (LORs) provide valuable insights into candidates' capabilities and experiences beyond standardized test scores. However, reviewing these text-heavy materials is time-consuming and labor-intensive. To address this challenge and support the admission committee in providing feedback for students' professional growth, our study introduces LORI: LOR Insights, a novel AI-based detection tool for assessing leadership skills in LORs submitted by online master's program applicants. By employing natural language processing and leveraging large language models using RoBERTa and LLAMA, we seek to identify leadership attributes such as teamwork, communication, and innovation. Our latest RoBERTa model achieves a weighted F1 score of 91.6%, a precision of 92.4%, and a recall of 91.6%, showing a strong level of consistency in our test data. With the growing importance of leadership skills in the STEM sector, integrating LORI into the graduate admissions process is crucial for accurately assessing applicants' leadership capabilities. This approach not only streamlines the admissions process but also automates and ensures a more comprehensive evaluation of candidates' capabilities.

Stanley Zhong had a 4.42 grade point average, a nearly perfect SAT score, had bested adults in competitive coding competitions and started his own electronic signing service all while still in high school. When it came time to apply to colleges, Zhong's family wasn't overly concerned about his prospects even amid an increasingly competitive admissions environment. But, by the end of his senior year in Palo Alto in 2023, Zhong received rejection letters to 16 of the 18 colleges where he applied, including five University of California campuses that his father had figured would be safety schools. "It was surprise upon surprise upon surprise, and then it turned into frustration and, eventually, anger," his father, Nan Zhong, told The Times in a recent interview. "And I think both Stanley and I felt the same way, that something is really funky here."

Fairness in both machine learning (ML) predictions and human decisions is critical, with ML models prone to algorithmic and data bias, and human decisions affected by subjectivity and cognitive bias. This study investigates fairness using a real-world university admission dataset with 870 profiles, leveraging three ML models, namely XGB, Bi-LSTM, and KNN. Textual features are encoded with BERT embeddings. For individual fairness, we assess decision consistency among experts with varied backgrounds and ML models, using a consistency score. Results show ML models outperform humans in fairness by 14.08% to 18.79%. For group fairness, we propose a gender-debiasing pipeline and demonstrate its efficacy in removing gender-specific language without compromising prediction performance. Post-debiasing, all models maintain or improve their classification accuracy, validating the hypothesis that fairness and performance can coexist. Our findings highlight ML's potential to enhance fairness in admissions while maintaining high accuracy, advocating a hybrid approach combining human judgement and ML models.

This article addresses the challenge of validating the admission committee's decisions for undergraduate admissions. In recent years, the traditional review process has struggled to handle the overwhelmingly large amount of applicants' data. Moreover, this traditional assessment often leads to human bias, which might result in discrimination among applicants. Although classical machine learning-based approaches exist that aim to verify the quantitative assessment made by the application reviewers, these methods lack scalability and suffer from performance issues when a large volume of data is in place. In this context, we propose deep learning-based classifiers, namely Feed-Forward and Input Convex neural networks, which overcome the challenges faced by the existing methods. Furthermore, we give additional insights into our model by incorporating an interpretability module, namely LIME. Our training and test datasets comprise applicants' data with a wide range of variables and information. Our models achieve higher accuracy compared to the best-performing traditional machine learning-based approach by a considerable margin of 3.03\%. Additionally, we show the sensitivity of different features and their relative impacts on the overall admission decision using the LIME technique.

Access to higher education is critical for minority populations and emergent bilingual students. However, the language used by higher education institutions to communicate with prospective students is often too complex; concretely, many institutions in the US publish admissions application instructions far above the average reading level of a typical high school graduate, often near the 13th or 14th grade level. This leads to an unnecessary barrier between students and access to higher education. This work aims to tackle this challenge via text simplification. We present PSAT (Professionally Simplified Admissions Texts), a dataset with 112 admissions instructions randomly selected from higher education institutions across the US. These texts are then professionally simplified, and verified and accepted by subject-matter experts who are full-time employees in admissions offices at various institutions. Additionally, PSAT comes with manual alignments of 1,883 original-simplified sentence pairs. The result is a first-of-its-kind corpus for the evaluation and fine-tuning of text simplification systems in a high-stakes genre distinct from existing simplification resources.

This story is from The Hill's Changing America publication. The job of a college admissions officer is not an easy one. For any competitive higher learning institution, the admissions process used to hand-pick each incoming student has also come under increasing scrutiny in recent years. To ensure the ongoing success of an institution, admissions officers are tasked with the nearly impossible task of efficiently evaluating thousands of applications each school year, with the expectation that their choices will reflect the institution's standards, grow diversity and inspire enough students to enroll. The process is a balancing act, and one that is expected to proceed without gender-based or racial bias.

Artificial Intelligence, for most people, is a tech that powers chatbots or image recognition at best – basically, a software that tells images of cats from dogs. Others view it as a serious threat to their regular day jobs. Regardless of its impact on their lives, people view AI as a technology with tremendous future potential. While the future of AI elicits awe and fear, its impact on the present remains largely unacknowledged. From shortlisting resumes to spreading propaganda, AI is working harder on us than most of us know.

The increasing use of technology in everyday life is changing the way students learn and absorb information. It is because of artificial intelligence that the educators today are able to provide a personalized learning environment to the students. The researchers have developed systems that can automatically detect whether students are able to understand the study material or not. Chatbots or artificially intelligent conversational tools, built to improve student interaction and collaboration, are acting as a game changer in the innovative ed-tech world. This article discusses 7 ways in which artificial intelligence and chatbots are influencing the education.