Editor’s Note: Today’s post is by Tracy Bergstrom and Dylan Ruediger. Tracy is he program manager for collections and infrastructure within Ithaka S+R’s Libraries, Scholarly Communication, and Museums program. Dylan is Program Manager for the Research Enterprise at ITHAKA S+R.

Over the past 24 months, generative AI has become inescapable. As a tool that is capable of generating content, its implications for how scholarly research is conducted and for scholarly publishing and communication are potentially transformative. What is not yet clear is how transformative this impact will be, and which areas of scholarly communication may see more rapid and revolutionary change than others. In a report published today, A Third Transformation? Generative AI and Scholarly Publishing, Ithaka S+R explores these issues. 

This report continues from our January 2024 publication on the state of the scholarly publishing industry as it navigates “the second digital transformation,” and its assessment of shared infrastructure needs in light of ongoing change in the industry. Scholarly publishing’s first digital transformation saw a massive shift from paper to digital, but otherwise retained many of the structures, workflows, incentives, and outputs instrumental within the print era. As we explored in the first report, many workflows that characterized the print era are currently being revamped in favor of new approaches that bring tremendous opportunities, and also non-trivial risks, to scholarly communication. Today’s report examines how generative AI accelerates change within this space to the extent that we are already on the cusp of “a third transformation.” 

Designed as a companion or addendum to the larger Second Digital Transformation of Scholarly Publishing report, A Third Transformation? Generative AI and Scholarly Publishing follows the original report’s internal structure. As such, today’s report addresses many of the same themes to provide analysis of the present landscape and recommendations to address key needs. 

Transitioning Towards Service Provision 

Scholarly publishing as a whole is in the midst of a long-term shift away from a model centered on editorial work towards one based on services and platforms. We expect generative AI to accelerate this trend, and publishing organizations are already engaged in strategic planning about how to map generative AI services to support the workflows of readers, authors, and editorial staff. The boundaries between discovery, interpretation, and writing practices have already become increasingly interconnected, and these services will likely become a fully integrated suite of tools aimed at keeping researchers engaged with a single platform across the research process In the near future. 

Efficiency gains to the peer review process through generative AI in particular is an area poised for impact, although specific ideas as to how generative AI may facilitate peer review in the near future varied considerably. Even the most optimistic advocates for AI’s potential in peer review recognize the need for careful consideration of how to use generative AI to enhance rather than substitute for human engagement and knowledge throughout the review process. 

Consolidation and Competition

For several decades, consolidation has been one of the major trends in the scholarly publishing industry, and competition over the platform, analytics, and author services businesses in the sector has been fierce. Generative AI has implications for each of these business lines. As several of our interviewees noted, despite the widespread expectations that generative AI will create new revenue streams and affect business lines, how exactly it will do that outside of content licensing agreements is not yet clear. 

Competition from big tech companies will likely affect publishers in myriad ways. Deals that many universities are signing with Microsoft or OpenAI to provide sandboxed research environments for scholars could, for example, acclimate researchers to using CoPilot or ChatGPT for research purposes and as an intermediary to accessing the scholarly record. This scenario would presumably not alter researchers’ desire to publish in scholarly journals, but it would complicate efforts to build or expand services across the research lifecycle. Depending on how LLMs develop, and how publishers respond and lead, there may be a variety of different directions ahead. 

Humans and Machines 

By creating new opportunities for human/machine interaction across that value chain, generative AI raises important questions about where, when, and how, human labor and knowledge add essential value to scholarly communication. These questions have complex ethical, practical, and legal components. Generative AI opens up a new phase of scholarship in which a human researcher may be the respondent, rather than the instigator, of new avenues of inquiry. Guardrails around usage, for which we become confident we share an understanding, are therefore imperative. 

Adapting to generative AI will require decisions about where human judgment is necessary in conducting research and writing articles, in peer review, and throughout the editorial process. Looking ahead, this will require coordinated and careful consideration as to how increased use of LLMs by researchers can be balanced with the imperative to uphold the fundamental tenets of scholarship, including respect for provenance, attribution, reproducibility, and transparency. 

Research Integrity

One overriding theme we heard across interviews was that the quality of content that currently underlies LLMs is not reliable enough to ensure the integrity of the scholarly record. To guarantee research integrity in conjunction with increasing usage of generative AI tools, interviewees frequently cited the need for new standards to ensure consistency and transparency, and that that publishers must articulate with more granularity how it is acceptable to utilize generative AI tools, and what types of use cases are reliable or not, so that authors understand areas in which they need to be transparent about use. 

The issue of research integrity provokes the larger speculative question of how we define trust, or trustworthiness, in a world in which machine-to-machine communication increasingly generates more of the scholarly record. Ensuring trustworthiness is not an issue with a singular answer, but one that will require diligence and effort as generative AI tools continue to evolve. 

Making Meaning 

Generative AI tools provide researchers with the ability to derive new meaning from the scholarly record in ways not possible before. The underlying question of where the human will be necessary within this work, however, is critical to the transformational possibility of the tools, especially as we begin to see radical frameworks for completely automated science which leverage generative AI to generate research ideas, write necessary code, conduct experiments, and create visualizations and written outputs to communicate findings emerge. 

Generative AI tools may also be transformative in how we think about reading. For publishers, one risk is that the use of generative AI to summarize or synthesize scholarly outputs leads fewer researchers to engage directly with articles, setting off a decline in readership and a corresponding decline in metrics used to measure the value of publisher and aggregator collections. 

Supporting New Business Models 

In parallel to how open access has disrupted the fundamental business model of publishing over the past two decades, generative AI has the same potential to be transformative for those with the resources to best adapt and harness its potential. One challenge is that smaller publishing organizations may not have the human or technological capital to keep pace with their larger counterparts, potentially resulting in further consolidation within the industry. Large commercial publishing organizations that hold significant content within their purview will be able to create new functionality and attract users, simultaneously making it more difficult to operate independently within this space. 

The pace of iteration over the past 24 months has outstripped publishing organizations’ abilities to adapt underlying business models. Individuals we spoke to across the sector acknowledged that organizations need to invest more time in understanding generative AI technology more deeply because it has the potential to upend a number of underlying systems in the future. Many of the thorniest challenges necessitate cross-sector collaboration involving not only publishing organizations but also universities, funders, and technology providers. These parties will need to work together in the critical and ongoing work of adopting ethical usage of generative AI to advance the ideals of scholarly communication. 

We appreciated the opportunity to continue to explore how scholarly publishing is addressing change and thank STM Solutions and six of its member organizations who supported this project.