Generative artificial intelligence in mental health: A preliminary study on automating materials development for cognitive bias modification.

Abstract People with depression tend to interpret ambiguous events in a negative biased direction, which contributes to symptomatology. Cognitive bias modification-interpretation (CBM-I) is a digital therapeutic that targets negative interpretation bias using text-based scenarios. CBM-I offers greater flexibility in combating depressive-related issues, but the development of its training materials can be costly. In the present study, we used Generative AI to produce CBM-I training materials and compared them with human-generated materials. The aim is to examine whether AI-generated materials are equivalent to human-generated materials, as per a set of pre-defined criteria designed to capture common experiences of individuals with depression. We followed the typical CBM-I materials development procedure, first creating raw items and then adapting them into standard CBM-I format. We compared participants’ ratings of 100 raw scenarios and 100 CBM-I scenarios, half of which were created by Copilot and half created by people with depression. Living/lived experts of depression (N = 30) rated raw items, and another 30 experts rated CBM-I items on readability, relevance, and severity of scenarios as related to depression. With the exception of severity ratings, results revealed that ratings of human-generated and AI-generated scenarios were statistically non-equivalent. The differences in the overall actual mean ratings, however, were small (range 0.03–0.41); the overall direction of ratings between AI-and human-generated scenarios were the same and consistent with the scenarios’ emotional content. Interpretation of the data and future implications of outsourcing AI in CBM-I materials production are discussed.