Self-regulated-learning (MSLQ) construct scores simulated by large language models

Five data sets of Motivated Strategies for Learning Questionnaire (MSLQ) construct scores, each a random sample of 300 cases generated by one large language model in the study "Delving into the psychology of Machines: Exploring the structure of self-regulated learning via LLM-generated survey responses" (Computers in Human Behavior, 2025). One data set per model: SRL_GPT, SRL_Gemini, SRL_Claude, SRL_Mistral, and SRL_LLaMa.

Usage

SRL_GPT

SRL_Gemini

SRL_Claude

SRL_Mistral

SRL_LLaMa

Format

Each is a data frame with 300 rows and 5 variables – the MSLQ construct scores (each the mean of that construct's Likert items, range 1–7):

CSU

cognitive strategy use

IV

intrinsic value

SE

self-efficacy

SR

self-regulation

TA

test anxiety

Source

Saqr, M. (2025). Delving into the psychology of Machines: Exploring the structure of self-regulated learning via LLM-generated survey responses. Computers in Human Behavior, 173, 108769. doi:10.1016/j.chb.2025.108769

Details

Only the five models whose responses carry estimable structure are included. Two other generators from the original study (ChatGPT and LeChat) produced near-independent items (mean absolute inter-item correlation \(\approx 0.03\)), so their network is the empty graph; they are omitted. The five retained models span the spectrum the paper describes, from realistically structured (SRL_GPT) to strongly "over-coherent" (SRL_Gemini, SRL_Claude).

Examples

# partial-correlation network of the five constructs for one model
net <- ebic_glasso(SRL_GPT)
net
#> <psychnet> glasso network
#>   nodes: 5   edges: 10   (undirected)
#>   lambda: 0.00861   gamma: 0.5
#>   optimality (KKT residual): 2.21e-10
net_centralities(net)
#>   node  strength expected_influence
#> 1  CSU 1.1984512         1.19845117
#> 2   IV 1.0012765         1.00127649
#> 3   SE 0.8492185         0.84921854
#> 4   SR 1.2314317         0.53172852
#> 5   TA 0.6082238        -0.09147935