Innovation and the artistic, musical, and literary expression of human experiences and emotions depend on creativity. However, the idea that material created by humans is inherently better is coming under pressure from the emergence of generative artificial intelligence (AI) technologies, such as Large Language Models (LLMs). Content in several formats, such as text (ChatGPT), graphics (Midjourney), audio (Jukebox), and video (Pictory), can be produced using generative AI.
These technologies have already shown that they can speed up programming processes, increase customer service productivity, improve white-collar work quality and efficiency, reinforce persuasive messaging, and enhance collaborative storytelling. However, more information is needed about how generative AI might affect creativity, which is one of the fundamental facets of human behavior.
Recently, a team of researchers from the University College London and the University of Exeter carried out a study looking at the influence of generative AI on creative written output to study how it affects human creativity, specifically in the setting of short fiction. In the business world and society, writing is a common means of human expression. The purpose of the study is to evaluate the impact of generative AI on participants’ creative short story production. Participants were instructed to compose a narrative on a randomly chosen topic, with precise guidelines about its length and intended readership to prevent contradictory outcomes.
The results have shown that having access to generative AI concepts greatly improves the stories’ perceived inventiveness, literary caliber, and overall enjoyment. This effect is more noticeable in writers who are typically less imaginative. These authors gain the most from the AI’s capacity to come up with initial concepts since it enables them to get over creative barriers and produce more captivating narratives.
The research also revealed a drawback to the application of generative AI in creative writing. When AI is used to create stories, the stories that result are typically more alike than when humans develop them alone. According to this, generative AI may increase individual creativity but may also homogenize creative outputs, decreasing the variety of original stuff created.
This dynamic produces social difficulty. Writers gain from AI’s increased quality and inventiveness on an individual basis. However, due to the dependence on AI-generated concepts, the variety of original and distinctive content is reduced. This compromise has important ramifications for scholars, decision-makers, and practitioners who work to promote creativity.
The team has summarized the results as follows.
- The study compared creative outputs between a Human-only condition and two Generative AI-assisted conditions, one AI idea and five AI ideas. Generative AI made stories much more unique and valuable.
- Compared to the Human-only condition, the Single AI Idea Condition demonstrated a 3.7% gain in usefulness and a 5.4% rise in tale freshness.
- Five AI Ideas Condition produced a 9.0% gain in usefulness and an 8.1% rise in uniqueness.
- Evaluators deemed AI-assisted stories to be more entertaining, better written, and more likely to contain plot twists. There were no discernible differences in the writers’ self-evaluations between the conditions.
- Results were impacted by inherent inventiveness as determined by the Divergent Association Task (DAT). With AI support, high-DAT writers’ stories didn’t significantly alter their quality. On the other hand, uniqueness, utility, writing quality, and enjoyment all significantly improved for low-DAT authors, matching the performance of high-DAT writers.
- AI-assisted stories were shown to be more similar to each other and to AI-generated ideas through objective analysis using cosine similarity scores.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
