Asynchronous Video Interviews: Future of University Admissions?

In order to save time and costs in the admission process, some higher education institutions are looking for asynchronous video interview technology (AVI), which allows applicants to answer questionnaires without a human interviewer on the other side. While much of the audio and video technology used in asynchronous video interviews has been around for years, recent advances in artificial intelligence could help analyze large candidate pools more efficiently and accurately, said Sunny Saurabh, CEO of the AVI company. Interviewer.AI.

Saurabh said recent advances in machine learning mean that new AVI technologies can not only record video but also effectively assess and analyze interviewees through computer vision, speech analysis and natural language processing to develop soft skills such as professionalism, sociability, positive attitude and communication skills. measure, among other things. other standards. He added that AI can also help rank candidates to make it easier for admissions officers to shortlist large groups of candidates for the first round of human interviews.

According to Saurabh, about 70 percent of the top universities in Singapore, including the National University of Singapore (NUS) and Nanyang Technological University (NTU), are now using Interviewer.AI for admissions interviews as the technology gains interest in higher education and the company tries penetrate other markets in the future.

“With the pandemic still raging in various parts of the world, face-to-face campus interviews for thousands of applicants are challenging and can spark a super-dissemination event. Universities can use AVIs to screen applicants alongside other relevant parts of the application process to not only save time and costs for both universities and students, but also provide a great experience for every applicant,” he wrote in an email. at Government technologynoting that the admission process is an early use case for AI-driven AVI technology.

Aside from streamlining the admissions process, Saurabh said, higher education institutions can also increasingly look to AVI technology for on-campus career services, which help students find jobs related to their education after graduation.

He said another use case for the emerging technology is to help universities coach seniors for their first interviews, and employers to score resumes for work experience and academic qualifications, among other things.

“Career Services [at universities]which typically consists of fewer than 10 faculty members, is required to coach hundreds of students from the same batch and achieve 100 percent placement,” Saurabh noted in an email. “This can be an arduous task, especially when most companies where young college students want to work are multinational companies that typically have an asynchronous video interview before a human recruiter can review the candidates.”

According to a 2020 essay by Alan Jones, Suzan Harkness, and Nathan Mondragon for the nonprofit Educause that examines the effectiveness of AVI tech tools for processes like these, improvements in AI for asynchronous interviews could “shift to a new paradigm for interviewing and recruitment”, as well as for career guidance in higher education. The essay cites 2018 survey results from the National Association of Colleges and Employers, which found that many employers are looking for new methods of screening and filtering applicants beyond degrees earned, schools attended and GPAs, which can be less predictive for success at work than general mental skills and soft skills.

“Employers looking to leverage a wider network to increase recruitment pool diversity and uptake can use video interview technology to more easily reach larger applicant pools of diverse candidates,” Educause wrote. “By breaking away from campus-based career fairs, employers can recruit nationally and internationally without physically being on multiple campuses or making choices based on recruiting and travel budgets, college size, or rank.”

Aside from efficiency, Educause urged caution and the need to “not cede power to AI too quickly or attribute too much to the power, scale, and capability of machine learning and AI in its current form.” .” It argued that historical data recorded through machine learning is often biased with regard to gender, race, ethnicity or social class.

Despite these concerns regarding AI bias, Saurabh believes recent improvements in AVI and AI technology could still be particularly useful for screening large groups of applicants without inherent bias.

“The data collected using WIPs can provide a lot of objectivity and data-driven insights, which are typically lost in an environment where thousands of applicants are screened by human staff whose individual experience and skills in finding the right candidate can vary, which means results in conscious and unconscious bias,” he wrote in an email. “Using WIPs, companies like Interviewer.AI can not only present data and insights to the various stakeholders, but they can actually get better with time by machine incorporate learning techniques based on the success of screened candidates’ performance over time.”

In terms of solutions, the Educause essay notes that in recent years, vendors involved in the development of AVI technology have been working on procedures to identify biased variables built into datasets and algorithms, and to remove functions that disable those variables. used. Some hire external auditors to help mitigate risk as well.

Still, according to Educause, datasets used in algorithms like those used for AI-driven WIPs can reflect human judgments that are inherently subjective.

“While efforts to democratize experiences and clean up the data may build greater public trust, there are some broader aspects of machine learning that need to be addressed as we scale AI more widely and establish reliability and validity,” Educause wrote.

Leave a Comment