Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers (2409.14570v1)

Abstract: The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11% increase in tool use, while those who cite insufficient training resources experience an 8% decrease. Female researchers experience a 7% decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19% decrease. Researchers associated with government advisory groups are 45% more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19%, while those in medical research institutions and hospitals show an increase of 16% and 15%, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.

• The paper analyzes survey data from 1,600 researchers to identify demographic, workplace, and barrier effects on generative AI usage frequency.
• Key findings reveal significant disparities in usage by gender and career stage, with less frequent use among females and advanced-career researchers.
• Usage rates vary significantly by workplace type and field, and surprisingly, perceived barriers correlate with higher usage frequency.

Overview of Generative Artificial Intelligence Usage in Research Environments

The paper "Generative Artificial Intelligence Usage by Researchers at Work: Effects of Gender, Career Stage, Type of Workplace, and Perceived Barriers," provides a comprehensive analysis of factors influencing the use of generative AI tools among researchers. Utilizing survey data from 1,600 researchers and robust statistical methodologies, the paper explores variables such as gender, career stage, workplace type, and perceived barriers to AI usage, offering valuable insights into the dynamics of AI adoption within scientific fields.

Key Findings

1. Demographic Disparities: The analysis highlights notable disparities in the usage of AI tools. Female researchers are reported to have a 7% reduced usage compared to their male counterparts. Moreover, advanced-career researchers exhibit a significant 19% decrease in AI usage frequency, which may imply a generational divide or resistance to technological adoption among more experienced researchers.
2. Barriers and Motivations: Interestingly, the presence of perceived barriers to AI usage correlates with an 11% increase in the frequency of tool utilization. This counterintuitive finding suggests that confronting barriers may drive researchers to engage more with AI, potentially due to the perceived necessity or benefits of overcoming such obstacles.
3. Workplace Influence: The paper finds that workplace type significantly affects AI adoption rates. Researchers in for-profit companies, medical research institutes, hospitals, and government advisory groups are more likely to use generative AI tools frequently compared to those in government roles, with increases of 19%, 16%, 15%, and 45% respectively. This suggests that organizational culture and available resources play a crucial role in facilitating AI integration.
4. Collaboration with AI Companies: Collaborating with AI companies is associated with an 11% increase in AI tool usage, highlighting the positive impact of cross-disciplinary partnerships in fostering technological uptake among researchers.
5. Field of Study: Usage rates also vary by research field, with lower frequencies observed in environmental sciences, ecology, mathematics, and biomedical fields compared to computing or information sciences. This difference underscores the varying degrees of AI integration and needs across disciplines.

Implications and Future Directions

The paper's findings have important implications for shaping AI integration policies in research environments. Addressing the observed gender and career stage disparities requires tailored training programs and organizational support to ensure equitable AI uptake among researchers. Enhancing training resources and reducing perceived barriers can facilitate wider adoption and effective use of generative AI technologies.

In terms of future developments, fostering strategic partnerships between academia and industry, as well as cross-sector knowledge exchange, may further integrate AI into the scientific workflow. Understanding the nuanced motivations and obstacles faced by researchers will be essential to developing comprehensive strategies that leverage AI's potential while addressing ethical and practical concerns.

Overall, this paper contributes valuable insights into the complex dynamics of generative AI adoption in research, highlighting critical areas for policy intervention and potential paths for enhancing the role of AI in driving scientific innovation.