Traditional: academic journals, targeting an academic audience; conference presentations
Modern: social media, podcasts, podcasts, data visualization, open access publishing, AI-powered tools
Using various methods of dissemination can help researchers reach a broad audience. While journal publications will remain an important method of disseminating research to other investigators, social media, press releases, and websites are examples of other dissemination methods that can reach diverse audiences (Capili and Anastasi, 2021).
Ross-Hellauer calls this “multi-directional dissemination” (Ross-Hellauer et al., 2020). They also include methods like TEDx talks and science festivals as moder methods of research dissemination.
Ross-Hellauer and colleagues also discuss how traditional outputs like journal articles can get an “impact boost” by pairing with more modern dissemination methods like lay summaries, blog posts, and video abstracts.
UMD Libraries has created a helpful research guide about AI and how it works. Click the button below to be taken directly to their website. They have provided this resource through a Creative Commons license that allows others to re-share this content (CC-BY-NC).
Forbes has a great article that outlines and debunks some myths around AI. Read the full list at this link, but here are some of the myths presented in their list that relate to research:
AI is unethical by its very nature.
AI alone can improve productivity and innovation.
AI is a one-size-fits-all tool.
One day, AI won't need any human input at all.
Exploring the potential of AI to streamline communication and broaden impact:
Through qualitative data collection, researchers Chubb, Cowling, and Reed from the UK found that participants believe AI can help research “move beyond the academy” because of its ability to create connections between researchers and the community (Chubb et al., 2021).
Ethical considerations and responsible AI use in research:
AI systems “lack capacities regarded as essential for moral agency such as consciousness, self-concepts, personal memory, life experiences, goals, and emotions (Resnik & Hosseini, 2024) - can produce dangerous results
Opacity of AI systems (“black box”) means we may not be able to really trust them and may lead to increases public distrust in science
Addressing AI biases
Reducing errors
Table 2 from Resnik & Hosseini’s paper is great – provides recommendations for ethical use of AI in research.