To save time and expense related to the admissions process, some higher education institutions are looking to asynchronous video interviewing (AVI) technology that allows applicants to answer questionnaires without a human interviewer on the other end. While much of the audio and video technology used in asynchronous video interviewing has been around for years, recent advances in artificial intelligence it could help screen large pools of applicants more efficiently and accurately, according to Sunny Saurabh, CEO of AVI firm Interviewer.AI.
Saurabh said recent advances in machine learning mean 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 measure social skills such as professionalism, sociability, positive attitude and communication skills, among others. other metrics. He added that AI can also help rank applicants to make it easier for admissions officers to shortlist large pools of candidates for the first round of human interviews.
According to Saurabh, about 70 percent of Singapore’s top universities, including the National University of Singapore (NUS) and Nanyang Technological University (NTU), are now using Interviewer.AI for admission interviews as the technology gains more interest in higher education and the company seeks to penetrate other markets in the future.
“With the pandemic still ongoing in different parts of the world, in-person on-campus interviews for thousands of applicants are challenging and can trigger a huge spread event. Universities can leverage AVIs to screen applicants in addition to other relevant parts of the application process, not only saving time and cost for both universities and students, but also providing a great experience for each applicant,” he wrote. in an email to government technologynoting that the intake process is an early use case for AI-powered AVI technology.
In addition to streamlining the admissions process, Saurabh said, higher education institutions may also increasingly look to AVI technology for on-campus career services, which help students find jobs related to their degree programs after graduation. graduation.
He said another use case for the emerging technology is helping universities train seniors for their first job interviews, and employers score resumes for work experience and academic qualifications, among other uses.
“Career Services [at universities], which typically consists of fewer than 10 faculty members, must train hundreds of students from the same batch and aim for 100 percent placement,” Saurabh noted in an email. “This can be a tall order, especially when most of the companies college kids want to join are multinational companies that typically have an asynchronous video interview before a human recruiter can screen candidates.”
According to a 2020 trial By Alan Jones, Suzan Harkness, and Nathan Mondragon for the nonprofit Educause Examining the effectiveness of AVI technology tools for processes like these, advances in AI for asynchronous interviewing could create a “change to a new paradigm for interviewing and hire”, also in terms of career guidance in higher education. The essay quotes 2018 results of the test from the National Association of Colleges and Employers which found that many employers are looking for new methods to screen and screen applicants beyond degree earned, schools attended and GPA, which may be less predictive of on-the-job success than general mental ability and interpersonal skills.
“Employers who want to cast a wider net to increase the diversity and inclusion of their hiring pools can use video interview technology to more easily reach larger pools of diverse candidates,” Educause wrote. “Breaking free of on-campus career fairs allows employers to recruit nationally and internationally without the need to physically be on multiple campuses or make decisions based on recruiting and travel budgets, university size or the classification”.
Efficiencies aside, Educause urged caution and the need to “not be too quick to cede power to AI or attribute too much power, scale and capability to machine learning and AI in its current form.” She argued that historical data ingested through machine learning is often biased with respect to gender, race, ethnicity, or social class.
Despite these concerns related to bias in AISaurabh believes that recent improvements in AVI and AI technology could still prove particularly useful for evaluating large pools of applicants without inherent bias.
“Data aggregated through AVI can provide a lot of objectivity and data-driven insights, which are often lost in an environment where thousands of applicants are screened by human staff whose individual experience and skills in finding the right candidate may vary. resulting in conscious and unconscious bias,” she wrote in an email. “By using AVI, companies like Interviewer.AI can not only present data and insights to various stakeholders, but can also improve over time by incorporating machine learning techniques based on the success of selected candidates’ performance over time. weather”.
As for the solutions, the Educause essay points out that the providers involved in the development of AVI technology have worked in recent years on procedures to identify biased variables that were incorporated into data sets and algorithms, and eliminate functions that used those variables. . Some also hire outside auditors to help mitigate risk.
Still, according to Educause, the data sets that go into algorithms like those used for AI-driven AVIs may reflect human judgments that are inherently subjective.
“While attempts to democratize experiences and cleanse data may build more public trust, there are some broader aspects of machine learning that need to be addressed as we scale AI more broadly and establish reliability and validity,” Educause wrote.