Social, emotional, and behavioral (SEB) skills comprise a broad set of abilities that are essential for building and maintaining relationships, regulating emotions, selecting and pursuing goals, or exploring novel stimuli. Toward an improved SEB skill assessment, Soto and colleagues recently introduced the Behavioral, Emotional, and Social Skills Inventory (BESSI). Measuring 32 facets from 5 domains with 192 items (assessment duration: ~15 min), BESSI constitutes the most extensive SEB inventory to date. However, so far, BESSI exists only in English. In three studies, we comprehensively validated a novel German-language adaptation, BESSI-G. Moreover, we expanded evidence on BESSI in three ways by (1) assessing the psychometric properties of the 32 individual skill facets, in addition to their domain-level structure; (2) providing first insights into the temporal stabilities of the 32 facets over 1.5 and 8 months; and (3) investigating the domains’ and facets’ associations with intelligence, in addition to personality traits. Results show that BESSI-G exhibits good psychometric properties (unidimensionality, reliability, factorial validity). Its domain-level structure is highly similar to that of the English-language source version. The facets show high temporal stabilities, convergent validity with personality traits, and discriminant validity with fluid and crystallized intelligence. We discuss implications for research on SEB skills.

The alternate uses task (AUT) is the most popular measure when it comes to the assessment of creative potential. Since their implementation, AUT responses have been rated by humans, which is a laborious task and requires considerable resources. Large language models (LLMs) have shown promising performance in automatically scoring AUT responses in English as well as in other languages, but it is not clear which method works best for German data. Therefore, we investigated the performance of different LLMs for the automated scoring of German AUT responses. We compiled German data across five research groups including ~50,000 responses for 15 different alternate uses objects from eight lab and online survey studies (including ~2300 participants) to examine generalizability across datasets and assessment conditions. Following a pre-registered analysis plan, we compared the performance of two fine-tuned, multilingual LLM-based approaches [Cross-Lingual Alternate Uses Scoring (CLAUS) and the Open Creativity Scoring with Artificial Intelligence (OCSAI)] with the Generative Pre-trained Transformer (GPT-4) in scoring (a) the original German AUT responses and (b) the responses translated to English. We found that the LLM-based scorings were substantially correlated with human ratings, with higher relationships for OCSAI followed by GPT-4 and CLAUS. Response translation, however, had no consistent positive effect. We discuss the generalizability of the results across different items and studies and derive recommendations and future directions.

[PLEASE SEE PUBLISHED VERSION AT https://doi.org/10.3390/jintelligence10030063] Social, emotional, and behavioral (SEB) skills comprise a broad set of inter- and intrapersonal abilities that are essential for building and maintaining relationships, regulating one's emotions, selecting and pursuing goals, or exploring new learning opportunities. Toward an improved assessment of SEB skills for educational, clinicial, and other purposes, Soto et al. (2022) introduced the Behavioral, Emotional, and Social Skills Inventory (BESSI). Measuring 32 SEB skill facets from 5 domains with 6 items each (i.e., 192 items in total), BESSI constitutes the most comprehensive SEB inventory to date. However, so far, BESSI exists only in its English-language source version. Here we present three studies in which we comprehensively validate a German-language adaptation of this instrument, called BESSI-G, and expand evidence on the BESSI framework more broadly. We use data from two samples of adolescents and adults aged 14 to 65 years and multiple assessment waves. In Studies 1 and 2, we assessed the psychometric properties (unidimensionality, reliability, and factorial validity) of the 32 facets. We also present first evidence on the facets' test--retest stability over 1.5 and 8 months, respectively, and analyze their domain structure. In Study 3, we investigate the nomological network of BESSI-G, testing associations of the 32 facets with Big Five personality traits and, for the first time, cognitive ability (i.e., fluid and crystallized intelligence). Results from the three studies show that BESSI-G exhibits good psychometric properties with a relatively short assessment duration (~15 min.). Its facet- and domain-level structure is as theoretically expected and highly similar to the English-language source version. The facets are moderately to highly stable (average 8-month true-score stability: rho = .79). BESSI-G shows both convergent and discriminant validity in relation to personality traits, similar to the source version, and is largely independent of cognitive ability. We discuss implications for SEB skill assessment and research on SEB skills.