Apriora Identifies Top Candidates for Professors at The University of Alberta

The University of Alberta is a top university in Canada and home to over 40,000 students in a wide variety of programs. In an increasingly competitive hiring world, academic institutions like the University of Alberta look for innovative technologies to streamline their candidate selection processes while amplifying hire quality. Apriora, an AI-driven candidate screening platform, has set a new standard by helping them identify top candidates swiftly and effectively.

Hiring for a niche, technical role

Samer Adeeb, Chair of Civil and Environmental Engineering at the University of Alberta, recently embarked on a hiring process to fill a niche Ph.D. position requiring expertise in both engineering and dentistry- two distinct and specialized skill sets. To see if Apriora would be able to effectively identify the best candidates, Samer conducted a direct side-by-side comparison of Apriora's recommendations to his professors' recommendations.

Apriora vs. Manual Screening: The Experiment

To carry out the experiment, professors at the University of Alberta used traditional manual review alongside Apriora’s autonomous recruiting agent, Alex, to interview and screen candidates, allowing for a direct comparison of outcomes. Here’s how each method was carried out:

1. Manual Screening: Professors conducted a three-round manual review of resumes and cover letters, meticulously evaluating each candidate for experience and technical skills. After an extensive process, four top candidates out of thirty were identified.
2. Screening with Alex: The professors also gave Alex the job description, which she used to write the interview questions and scoring rubric for the role. Alex then autonomously reached out to candidates, scheduled interviews with each, and had in-depth conversations in finite element analysis, machine learning, and other technical knowledge relevant to the role. After every interview, Alex analyzed the conversations based on the role's scoring rubric to build a shortlist of the top candidates.

The Results: Apriora Matches Human Judgment

When the results were compared, Apriora’s recommendations were well aligned with the manual selection process.

• Top Selections Aligned: Apriora's highest-ranking candidates were among the top three selected by the manual process, demonstrating the Alex’s capability to identify top talent based on the professor’s predefined metrics.
• Consistent Quality Across Skills: The manual process emphasized balanced proficiency in both skill areas, and Apriora’s AI-driven rankings provided a high level of accuracy. Three out of the four manually selected candidates were among Apriora's top scorers, proving that AI could match the nuanced judgment of human evaluators in this context.

Apriora even identified two amazing candidates that our professors did not initially shortlist because their CVs were not well aligned with the advertisement. We ended up moving forward with both.
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‍Samer Adeeb, Chair of Civil and Environmental Engineering at the University of Alberta

How Apriora Made It Possible

Apriora’s AI-driven evaluation process made it possible to match human judgment by employing a blend of advanced scoring and in-depth candidate assessment.

• Tailored Metrics and AI Evaluation: Apriora’s customizable metrics allowed the professor to focus on specific skills critical to the role. By aligning these metrics with job requirements, Apriora’s AI was able to produce rankings that aligned closely with the human process.
• Holistic Candidate Assessment: Alex conducted the interviews over video, which goes beyond just evaluating resumes. By capturing each candidate’s verbal responses through a two-way conversation, Alex took into account analysis of both communication skills and technical knowledge, offering a well-rounded perspective similar to what a human evaluator would gather.
• Data-Driven Accuracy: Apriora’s AI scored candidates based on quantifiable metrics, minimizing the risk of unconscious bias and enabling a consistent approach to evaluating each individual’s strengths.

Key Benefits: Speed, Accuracy, and Unbiased Evaluation

This case study illustrates how Apriora offers recruiters a solution that’s both accurate and time-efficient. By using Apriora’s scoring and ranking, the university was able to accelerate the hiring process without compromising on quality:

• Efficient Screening at Scale: With Apriora, the professor quickly reviewed thirty candidates, streamlining a process that traditionally requires weeks of scheduling, calling, and evaluating.
• Objective Candidate Evaluation: Apriora’s scoring is based on clear, quantifiable metrics, removing subjective biases that can influence manual reviews.
• High-Quality Matches: Apriora’s AI matched human selection in identifying qualified candidates, proving its potential to function as a robust screening tool that keeps pace with traditional methods.

Apriora interviewed 30 candidates in a matter of days, automatically surfacing the top candidates just as effectively, if not more, as the professors.
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‍Samer Adeeb, Chair of Civil and Environmental Engineering at the University of Alberta

Conclusion: Apriora – Delivering Human-Level Screening, Powered by AI

Through this case study, Apriora demonstrated its effectiveness in matching human judgment in candidate selection for a highly specialized academic role. By combining powerful AI with customizable evaluation metrics, Apriora empowers hiring teams to conduct efficient, accurate, and unbiased screening processes. The results are clear: Apriora’s AI-driven screening can be as precise as manual selection, making it an invaluable tool for today’s fast-paced hiring environment.

Ready to experience human-level candidate selection with the power of AI? Discover how Apriora can transform your recruitment process today.