# Enhancing user creativity: Semantic measures for idea generation

arXiv: https://arxiv.org/abs/2106.10131 · HTML: https://mlsanity.com/paper/2106.10131/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 66 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [The Shifting Sands of Creative Thinking: Connections to Dual Process Theory](https://mlsanity.com/paper/1409.2207/) (2014)
- [Revenge of the 'Neurds': Characterizing Creative Thought in terms of the Structure and Dynamics of Memory](https://mlsanity.com/paper/1308.5037/) (2013)

## Built on by (most cited first, 8 of 8)

- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models](https://mlsanity.com/paper/2601.20546/) (2026)
- [The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI](https://mlsanity.com/paper/2303.13300/) (2023)
- [Scaffolding Recursive Divergence and Convergence in Story Ideation](https://mlsanity.com/paper/2507.03307/) (2025)
- [WikiLink: an encyclopedia-based semantic network for design innovation](https://mlsanity.com/paper/2208.14349/) (2022)
- [Automatic Creativity Measurement in Scratch Programs Across Modalities](https://mlsanity.com/paper/2211.05227/) (2022)
- [Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking](https://mlsanity.com/paper/2411.00168/) (2024)
- [Dynamic semantic networks for exploration of creative thinking](https://mlsanity.com/paper/2501.11090/) (2025)

## Abstract (arXiv)

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC S'anchez-Batet) and semantic similarity (Lin/S'anchez-Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence.