The authors adapt a developmental psychology framework (Kegan's stages of meaning-making) to evaluate LLMs. They build a 20-item sentence-completion test (DSCT) designed to elicit developmental stage in text responses, then test whether LLMs can (1) simulate personas at specified stages, (2) score real human DSCT responses, and (3) what stage LLMs themselves exhibit when answering without persona-conditioning. Frontier models recover simulator-intended stages well on synthetic personas; human-LLM agreement on real responses is fair; and default LLM outputs show stable stage-like patterns, with larger/newer models tending toward higher-rated text.
Main takeaways:
- Introduces a 20-item Developmental Sentence Completion Test to elicit developmental stage signal in text
- Top models accurately recover intended stages when simulating synthetic personas
- Human-LLM agreement on real DSCT responses is fair (stronger within-neighborhood than exact)
- LLMs answering DSCT prompts without personas show stable stage-like differences across model families
- Larger and newer models tend to generate higher-rated (more developed) text
- Stage signal is cleaner in synthetic/simulated responses than in real human DSCT text