AI in Assessments

Here are some recommendations on how to enhance with AI the delivery of psychological assessment and mental health screening, informed by a set of evidence-informed articles (Click on the button below for a shared folder of articles.)

• Apply AI for automated scoring of self-report scales or open-ended responses, but always review results manually and interpret them within the broader clinical context.

• Employ AI-generated summaries to highlight patterns across multiple assessments, helping you track trends and present a clearer narrative to clients and families.

• Integrate natural language processing to transcribe, organize, and preliminarily code qualitative interview data, speeding up thematic analysis for diagnostic clarity.

• Leverage AI to personalize follow-up screening, suggesting targeted questionnaires based on initial responses, while maintaining human oversight to avoid bias.

• Use AI-supported personality profiling tools carefully to complement, not replace, traditional personality measures and interviews.

• Apply AI tools to support collaborative, therapeutic assessment — for example, co-creating assessment questions with the client to increase engagement and clarity.

• Continuously monitor AI scoring tools for cultural validity, updating models and human oversight protocols to ensure validity across diverse client populations.

• Follow published AI scoring guidelines: always disclose to clients when AI is used, verify scoring accuracy, and avoid scoring AI-generated or AI-edited client responses (per Dumas et al., 2025; review article on the left).