Measuring Constructive Creativity in AI-Augmented Work: A Scale-Invariant Embedding Framework

Abstract

As AI-generated content proliferates in workplace settings, objective metrics that capture dimensions relevant to constructive creativity—work that balances novelty with usefulness—are needed to support metacognitive monitoring and quality evaluation. Existing creativity instruments either measure unconstrained divergence or assess accuracy against fixed item sets, limiting their applicability to real-world text of varying complexity. We present a framework for evaluating "constructive creativity" through three orthogonal, scale-invariant metrics derived from embedding geometry: *divergence* (semantic spread among target concepts), *alignment* (semantic fit between associations and targets), and *parsimony* (non-redundancy of associations). Across 3,600 random configurations with targets ranging from 1 to 50 words and associations ranging from 2 to 50 words, aggregate pairwise correlations remained below |r| < 0.22 under OpenAI ada-002 embeddings and |r| < 0.23 under GloVe embeddings. Regime analysis decomposing orthogonality by task scale (m, n) revealed that two of three metric pairs—divergence–parsimony and alignment–parsimony—maintained independence uniformly across all scales and both embedding models, while divergence–alignment exhibited independence at smaller task sizes. Benchmarking against 144 Remote Associates Test items provided partial validation: alignment distinguished correct solutions from chance (mean similarity = 0.356), though divergence did not predict item difficulty or solution time as hypothesized. LLM judges (GPT-4o-mini, Claude Haiku 4.5) discriminated correct solutions from foils with 84–88% accuracy, and model-solving difficulty correlated with human difficulty (r = 0.559). These metrics offer candidate dimensions for applying semantic associations to creative tasks, providing a foundation for future validation as cognitive feedback tools in AI-augmented workflows.