The AI revolution is continuing its spread throughout the world. Private school, though often slow to adapt its ways, is facing the inevitable; it’s here. The admission office is not isolated from the influence that it will have on the way we do business either. Although colleges have already started its march, one of the last resistance will probably be around the private school process for reading applications.While AI can enhance efficiency and consistency, there are also concerns about its limitations. This article explores the potential advantages and disadvantages of integrating AI into the admissions reading process. By no means is this a complete list, nor does it convey the strong emotion on the topic that people will bring to the table.
THE ADVANTAGES
Whether a school is dealing with a growing number of applicants, aiming to reduce biases, or seeking to better understand student profiles, AI can offer a range of solutions that optimize the admissions workflow. Below are some of the key benefits of incorporating AI into the private school admissions process, highlighting how it can revolutionize the way applications are reviewed and decisions are made.
1. Efficiency and Speed
- Explanation: AI can process applications much faster than human readers. It doesn’t experience fatigue, doesn’t need breaks, and can work around the clock. This allows schools to significantly shorten their admissions cycle, which is particularly beneficial during peak application periods.
- Example: AI can analyze a batch of 500 applications in hours rather than weeks, rapidly sorting through academic records, essays, and recommendations. In contrast, a human team needs weeks to do the same task.
2. Consistency
- Explanation: Human readers, no matter how well-trained, can introduce variability into the admissions process. AI, on the other hand, can apply the same criteria uniformly to all applications, ensuring that each one is evaluated with the same level of detail and fairness.
- Example: If leadership experience is a key criterion, AI can be programmed to identify and highlight relevant leadership roles consistently across all applications, ensuring that this criterion is never overlooked or interpreted differently by various readers.
3. Comprehensive Analysis
- Explanation: AI can review every part of an application, ensuring that nothing is missed. It can cross-reference information from different sections of the application to identify patterns or key strengths that human readers might overlook.
- Example: If an applicant had a drop in grades due to illness, as explained in their personal statement, AI can cross-reference that with their overall academic performance to contextualize the drop, rather than flagging it as a weakness.
4. Personalized Recommendations
- Explanation: AI can provide personalized insights not only during the admissions process but also after students are admitted. It can analyze an admitted student’s application to recommend school programs or extracurriculars that match their interests and strengths.
- Example: A student who excels in creative writing and music might be recommended for the school’s advanced arts program or encouraged to join the music club, based on the AI’s analysis of their application. Advisors often failed to make these types of connections due to their busy schedules.
5. Multilingual Capabilities
- Explanation: Many AI systems have advanced language processing capabilities, enabling them to analyze applications written in different languages. This can open up the admissions process to a more diverse group of international students and, in particular, can accommodate parents who don’t speak the language. It can be a better document if parents can complete their portion in their native language.
- Example: A school might receive applications in Spanish, Mandarin, or French, and AI can evaluate these without needing human translators, broadening the school’s appeal to non-native English speakers.
6. Predictive Analytics
- Explanation: AI can use historical data to make predictions about an applicant’s future success at the school. By analyzing patterns in the data of previous students, it can identify the characteristics most strongly associated with success and apply this knowledge to new applicants.
- Example: AI could determine that applicants who have excelled in leadership roles and maintained strong academic records are more likely to thrive in the school’s rigorous environment, and it could flag new applicants with similar profiles as strong candidates.
7. Preliminary Analysis for Interview Selection
- Explanation: AI can help narrow down the applicant pool by identifying candidates who meet certain thresholds or exhibit standout qualities. This can make the interview process more efficient by ensuring that only the most promising candidates are selected for further review.
- Example: AI might identify students with exceptional academic performance combined with unique extracurricular achievements and flag them for an interview, helping admissions officers focus their time on the most competitive candidates.
8. Error Reduction
- Explanation: Human readers can overlook details, especially when fatigued or rushed. AI can review every section of the application with the same level of care, ensuring that no part of the application is accidentally skipped or misinterpreted.
- Example: If an applicant’s extracurricular section is located in an unusual part of the application, AI won’t miss it, whereas a human reader might unintentionally overlook it due to application formatting or time constraints.
9. Real-Time Feedback
- Explanation: AI systems can provide immediate feedback to applicants during the application process, ensuring that applications are complete and formatted correctly before submission. This reduces the number of incomplete or improperly filled-out applications that schools have to review.
- Example: If an applicant submits an essay that exceeds the word limit, AI can prompt them to edit it before submitting, helping applicants meet the requirements and reducing the workload on admissions staff to follow up on incomplete applications.
THE DISADVANTAGES
While AI offers exciting possibilities for streamlining and enhancing the admissions process, it is not without its drawbacks. As schools explore the use of AI to read and assess applications, it is crucial to recognize the potential limitations and challenges that come with this technology. AI may enhance efficiency and data analysis, but it also raises concerns regarding the loss of human judgment, the potential for algorithmic bias, and ethical considerations in decision-making. Below are some of the most significant disadvantages of integrating AI into the private school admissions process, highlighting the risks and complexities that schools must navigate when relying on artificial intelligence for such a critical task.
1. Potential for Missed Nuances
- Explanation: AI may struggle to interpret the subtleties of human expression, particularly in essays or recommendation letters, where tone, creativity, or personal circumstances play a significant role. Human readers are better at recognizing underlying themes and nuances that reflect the applicant’s character or unique experiences.
- Example: An AI might overlook a heartfelt essay about overcoming a personal challenge because it focuses more on key terms or writing structure, whereas a human reader would recognize the emotional depth and perseverance the applicant displayed.
2. Complexity of Prompt Development
- Explanation: Setting up and training AI systems to effectively read and evaluate applications can be a highly complex and time-consuming process. Schools must create detailed prompts and rules to ensure the AI evaluates applications correctly, which can be resource-intensive.
- Example: A school might need to spend weeks or months programming the AI to recognize leadership experience across various contexts, ensuring it doesn’t miss relevant achievements due to inconsistent terminology.
3. Lack of Human Judgment(expansion on #2)
- Explanation: AI can only assess data based on the rules it has been programmed with. It lacks the intuition, experience, and emotional intelligence that human admissions officers bring to the decision-making process, making it difficult for AI to make final, holistic decisions.
- Example: An applicant might be borderline based on grades, but a human reader could detect a strong recommendation letter from a respected figure, indicating potential that the AI would not consider heavily enough.
4. Ethical Considerations
- Explanation: The use of AI in admissions raises ethical concerns around fairness and transparency. Students and parents might feel uncomfortable with decisions being influenced by a machine, and the lack of transparency about how AI decisions are made can lead to distrust in the process.
- Example: If an AI rejects an applicant, the family may question the fairness of the process and want to understand why, but the inner workings of AI algorithms can be difficult to explain, leading to concerns about objectivity.
5. Potential for Algorithmic Bias
- Explanation: AI is only as good as the data and algorithms it is trained on. If the training data contains biases—such as a historical preference for certain demographics or characteristics—the AI could perpetuate or even amplify those biases, leading to unfair treatment of some applicants.
- Example: If an AI system was trained on data where applicants from affluent backgrounds were more successful, it might inadvertently favor similar applicants in future admissions cycles, further entrenching inequalities.
6. Difficulty in Assessing Soft Skills
- Explanation: Soft skills like leadership, creativity, resilience, and emotional intelligence are critical in holistic admissions but are difficult for AI to accurately assess, as they often require subjective interpretation of qualitative information such as essays or interviews.
- Example: An AI might fail to recognize an applicant’s creative potential or leadership skills because those qualities are demonstrated through less quantifiable achievements, such as organizing a community event rather than winning an award.
7. Technical Challenges and Reliability
- Explanation: AI systems are not immune to technical failures, glitches, or errors. System downtimes, bugs, or inaccurate processing could lead to incorrect evaluations or delays in the admissions process, creating inefficiencies rather than solving them.
- Example: If an AI system crashes or malfunctions during a crucial part of the admissions cycle, it could delay the entire process, or worse, generate inaccurate results that require manual rechecking.
8. Potential for Over-Screening
- Explanation: AI could inadvertently filter out applicants who might be a good fit based on holistic or non-traditional criteria. Over-screening based on specific metrics could result in missed opportunities for students who excel in areas not emphasized by the AI’s programmed evaluation system.
- Example: A student with unconventional achievements, such as self-teaching a musical instrument or founding a small community group, could be screened out because their accomplishments don’t fit neatly into the standard categories that AI is designed to evaluate.
9. Lack of Empathy
- Explanation: Human admissions officers can empathize with applicants and take into account personal struggles, hardships, or extraordinary circumstances when evaluating applications. AI lacks the ability to feel empathy, which can lead to cold, data-driven decisions without consideration of individual stories.
- Example: A student who had to overcome significant family issues that affected their academic performance might receive a more lenient and understanding evaluation from a human reader, while an AI might not factor in these personal hardships.
10. Privacy Concerns
- Explanation: AI systems often require extensive data to function optimally, which raises concerns about data privacy and how sensitive applicant information is being used, stored, and protected.
- Example: Families may worry about how their children’s personal data, including academic records, extracurricular activities, and essays, are stored and whether it could be exposed in a data breach or used for unintended purposes.
On one hand, AI promises to enhance the efficiency, speed, and consistency of the application review process, allowing schools to process larger volumes of applications while maintaining fairness. AI can provide data-driven insights, offer personalized recommendations, and handle multilingual applications, making it a valuable tool for schools looking to streamline their admissions processes.
However, there are emotional, moral, and ethical concerns that AI use may invoke. The loss of human judgment, the potential for missed nuances in essays and recommendation letters, and the challenge of assessing soft skills like creativity and leadership are all critical limitations. Additionally, there are concerns about algorithmic bias, ethical transparency, and privacy. While AI offers exciting opportunities, the move toward its integration in admissions raises questions about fairness and the irreplaceable value of human empathy and intuition in shaping a school community.
I suspect that this shift will be met with resistance as schools face the balance between technological innovation and the deeply personal, emotional nature of the admissions process.
I did ask the AI for its opinion about the advantages and disadvantages.
