From Homer’s Odyssey to Agatha Christie’s crime fiction, humans have produced plenty of tales to grace the bookshelf. But research suggests artificial intelligence may now be providing some competition.

In a study published in the journal Judgment and Decision Making, 1,682 adults were asked to read one of six short stories, three of which were written by humans and three by ChatGPT. Each AI story had a similar theme to one of the human-authored works.

The team told participants whether their story was written by a human or AI, but this information was not always correct. The researchers then asked participants to rate how absorbing and engaging they found the story, and its quality.

Participants who read an AI-generated story rated it as more absorbing and of higher quality than those who read a story written by a human. However, participants gave higher ratings to stories they had been told were written by people.

Dr Deena Skolnick Weisberg, a senior author of the research from Villanova University in Pennsylvania, said: “AI systems can already generate short stories that are seen as being at least as good as – if not better than – human-written stories. We should update our views of AI’s abilities accordingly.”

However, Weisberg, who is herself a creative writer, said that did not mean writing should be left to AI, noting that novels produced by tech were probably going to be different from those written by humans.

“We may need to make room for AI-generated novels, and for AI/human co-written novels, but that doesn’t mean that there’s no longer space for us to appreciate the process of human creativity,” she said.

“Humans write as a form of creative self-expression or to challenge ourselves, or to make sense of our experiences. The fact that AI can generate human-like stories doesn’t change any of that.”

The results also suggest attitudes to AI matter: participants with more positive attitudes toward AI gave higher ratings to stories they were told were produced by ChatGPT than those with a less positive outlook.

The researchers carried out two further experiments in which 905 adults were given two of the six short stories – both on the same theme and only one of which was penned by a person. They were then asked to guess which was written by a human and which by AI.

In one experiment, about 40% guessed correctly, a result that is worse than chance, while in the other experiment 52% guessed correctly.

“There is no general evidence that participants were able to distinguish human-written from AI-generated stories,” the authors write.

The researchers found that while participants’ self-reported expertise with fiction was not linked to how accurately they guessed, greater expertise with AI systems was.

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“AI writing has a particular style that we can learn to recognise with practice – just as we can learn to recognise different human authors’ unique styles,” Weisberg said.

Weisberg suggested that the preference for stories labelled as being written by humans was linked to a desire for authenticity, but that people actually preferred AI-generated stories because they were easier to read and digest.

However, Luke Kennard, a professor of film and creative writing at the University of Birmingham, who was not involved in the work, emphasised that AI was highly contentious.

“Because we’re human, we tend to personify AI and imagine it as some kind of glowing robot with a benign and neutral face, when it’s really just predictive code based on a massive stolen database of actual writing, physically based in vast, expensive and destructive datacentres with catastrophic repercussions for the surrounding communities,” he said.

Kennard also stressed that AI was not a “tool” similar to spellcheck or a thesaurus, but an existential threat.

“Of course it’s capable of spitting out a coherent and even enjoyable poem with the right prompting and finessing. Given that it’s been trained on thousands of years of human work, it would be embarrassing if it wasn’t,” he said. “I suppose the question we should be asking is: is it worth it? And the answer is absolutely, categorically, it is not.”

Presenter

Marcy Burstein, Ph.D. 
Division of Services and Intervention Research

Goal

This concept encourages youth (ages 0-25 years) mental health prevention, treatment, and services research that addresses barriers to empirically-supported and high-quality youth mental health care. It is expected that research supported by this concept will generate knowledge, methods, and approaches that can be readily applied in real-world settings and systems of care. Therefore, this concept emphasizes research that is practice-based, deployment-focused, guided by implementation science frameworks (e.g., hybrid effectiveness-implementation type 1, 2, or 3 designs), designed to prevent threats to implementation fidelity, and relevant across diverse individuals, with the ability to address drivers of mental health disparities.

Rationale

This concept is a call to action in response to the youth mental health crisis in the United States. As many as one in eleven children and adolescents in the United States are diagnosed with a mental health disorder in their lifetime, yet only a fraction of these youth receive treatment. The unmet need for mental health illness prevention, treatment, and services is greatest among youth who experience health disparities, those living in rural and inner-city areas, and those affected by housing and food insecurity. In recent years, the American Academy of Pediatrics, the American Academy of Child and Adolescent Psychiatry, and the Children’s Hospital Association declared a youth mental health national emergency, and the Office of the U.S. Surgeon General issued an advisory statement on youth mental health. Access to empirically-supported, high quality practices for youth mental health is limited by multiple barriers including: (1) screening, assessment, and intervention approaches that are not scalable or designed for use in the settings where youth are typically identified as being in need of care, (2) interventions that are not sufficiently responsive to the needs of youth and families and not optimally effective, (3) a poorly distributed workforce that is disincentivized to accept insurance, particularly Medicaid, (4) system and service barriers, including fragmented services (e.g., across schools, primary care, and specialty mental health clinics) and poor continuity of care, particularly during the transition from adolescence to adulthood, and (5) a lack of decision support tools and quality measures to guide the delivery of mental health interventions and services.

Examples of relevant research include studies focused on developing, optimizing, testing, and/or comparing:

• Scalable interventions that are of the appropriate intensity or dose based on the severity of the mental health problem.   
• Effective training approaches, including technology-assisted strategies for initial competency and sustained fidelity in the use of evidence-based assessments and interventions.
• Strategies to support integration of empirically-supported practices into child welfare, juvenile justice, foster care, schools, pediatric medicine, or online services, including system redesign.
• Optimal financial and other incentive strategies to encourage mental health providers to participate in insurance. 
• Interventions and implementation strategies to reduce disparities in service access, quality, and outcomes.
• Services interventions to promote detection, engagement, and referral to community-based mental health settings.
• Decision support tools to guide the selection of empirically supported approaches that will be offered in a given practice setting or for matching individual youth to services of appropriate intensity.