Artificial intelligence (AI) and machine learning have opened up new opportunities for psychological assessments and psychometrics. These technologies have also transformed assessments; game and image-based assessments are increasingly being used in place of traditional self-report measures. We no longer have to completely rely on self-report data and can instead rapidly infer insights from a number of sources such as verbal and non-verbal behaviour in videos and the language used in social media posts.
Artificial intelligence (AI) and machine learning have opened up new opportunities for psychological assessments and psychometrics. These technologies have also transformed assessments; game and image-based assessments are increasingly being used in place of traditional self-report measures. We no longer have to completely rely on self-report data and can instead rapidly infer insights from a number of sources such as verbal and non-verbal behaviour in videos and the language used in social media posts.
These innovative solutions are increasingly being adopted in the context of recruitment, with several benefits such as increased engagement, decreased test-taking times, less test-taking anxiety, and time and cost savings for employers. When designed and used right, they can also increase diversity in the workforce.
However, it is important not to get too carried away with overhauling assessments to improve user experience without keeping in mind the validity of the measure. In this blog post, we outline some of the key types of validity that are important to consider when designing and deploying selection assessments.
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