Navigating the New Frontier: AI and the Future of Scholarly Publishing
General Manager- Marketing
The widespread adoption of artificial intelligence (AI) represents a pivotal moment in global history. Often described as a transformative ‘superpower,’ generative AI is accessible to anyone with a computer and internet connection. Its influence is already evident across various industries, signaling the onset of far-reaching and disruptive changes yet to be fully realized.
AI, a remarkable product of decades of scholarly research and development, stands as a paradox in the realm of scholarly communication. This field, pivotal in broadcasting progress across various research areas, now faces the dual impacts of AI. This article aims to examine AI’s role in scholarly communication, with a special focus on journal article publishing lifecycle, and to contemplate its future implications for all involved stakeholders.
A basic analysis using the Dimensions app reveals a significant increase in the number of journal articles published from 2012 to 2022. There was an 89% growth over the past decade, with the number of articles rising from 2.8 million in 2012 to 5.3 million in 2022. In this period, the industry also experienced notable growth in the number of open access journals, a model also adopted by commercial publishers too. These journals were a major contributor in providing platforms for disseminating research to a broader audience.
The surge in publication numbers, coupled with disruptions to traditional business models and increasing publishing costs, has compelled publishers to reevaluate their operational strategies. They have swiftly embraced various technological enablers throughout the publishing lifecycle. As digital content consumption has skyrocketed over the years, publishers have had to revise their workflows. The shift from print-only to online-ahead-of-print, and now to a predominantly online-only model for article publication, has become the norm.
Journal publishers in the academic publishing sector were early adopters of technology, enhancing their publishing workflows significantly. This included using markup languages to partly automate content production and to aid in archival and retrieval. With AI-enabled systems and generative AI, today, we stand at a pivotal juncture with the potential to revolutionize the industry.
Disruptive innovation, a hallmark of any industry, opens up vast opportunities to delve into unexplored areas and confront challenges. This is particularly true for scholarly communication and journal publishing, which navigate their unique challenges in this unfamiliar landscape. At the same time, these fields are harnessing the potential that AI technologies offer.
AI-enablement has been a welcome change in the journal publishing domain which has been trying to accommodate to emerging business models and changing market demands. In an industry where speed of publication of journal has been continuously reducing, publishers have been quick to adopt technology solutions to manage both upstream and downstream publishing activities. There are many AI-enabled solutions for every stakeholder involved in the entire scientific publication life-cycle.
The advent of social media and widespread smartphone use has led to a staggering increase in the volume of content published and consumed every minute. Journal publishing is also experiencing a surge in content, overwhelming the niche audiences it caters to. This content overload poses significant challenges for two crucial players in the scientific publication lifecycle: researchers and practitioners. The repercussions are profound, affecting individual careers and society at large, as funding is squandered on unrealized benefits. We have many examples on predatory journals, papermills and unethical practices in the academia that pose a significant damage.
Regardless of the publishing model, the rise of AI has highlighted the essential role of journal publishers in the ecosystem, particularly in curating valuable information through meticulous peer review. While AI tools may boost content production, they also risk increasing the volume of low-quality content creating a strain in the research publication pipeline. This surge in content necessitates a more rigorous editorial review process, which is both costly and labor-intensive. The entire ecosystem must collaborate and embark on a journey of discovery to address the challenges and potential short- or long-term threats that are emerging as we begin to uncover the full potential of AI.
This exploration into AI within scholarly publishing not only highlights technological advancements but also raises critical questions about ethical considerations. It underlines the need for a balance between innovation and responsibility, urging the academic community to lead with foresight and ethical rigor. The article ‘AI & Machine Learning in Scholarly Publishing: Services, Data, and Ethics’ offers an interesting starting point on tackling these challenges and ensuring the integrity of scholarly communication. The path forward remains uncharted, reflecting the true essence of scholarship in this domain. It emphasizes the importance of finding solutions to issues affecting society at large, underscoring the role of academia in navigating unexplored territories and contributing to global knowledge.
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