The field of artificial intelligence is moving towards a deeper understanding of creativity and its applications. Recent studies have focused on evaluating the creative potential of large language models, with some findings suggesting that these models have reached a plateau in terms of creativity. However, other research has highlighted the importance of nuanced evaluation frameworks and the need to consider variability in model performance. The use of AI-generated codes to guide selection of texts for qualitative analysis has also shown promise in improving the efficiency and effectiveness of this process. Furthermore, new methodologies have been introduced to measure the novelty and originality of AI-generated content, which could have significant implications for intellectual property law. Noteworthy papers in this area include: Has the Creativity of Large-Language Models peaked, which found no evidence of increased creative performance over time. Charting the Parrot's Song, which introduced a robust methodology to measure distributional differences between generative processes. Probing and Inducing Combinational Creativity in Vision-Language Models, which proposed a framework to evaluate and improve the creative quality of vision-language models.