Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/161125

Scientific Theories and Artificial Intelligence

Desjardins-Proulx, P.·Poisot, T.·Gravel, D.
View original source ↗Export metadata ↓

Abstract

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the worlds biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logics ability to represent complex mathematical ideas with probability theorys ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.\n\ncase study you explore using the Salix tritrophic system.

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Desjardins-Proulx, P., Poisot, T., Gravel, D.. 2017-07-10. Scientific Theories and Artificial Intelligence. https://doi.org/10.1101/161125

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Preprint solicitation as a natural experiment in scholarly publishing

Preprinting is a growing and increasingly recognized mechanism for scientific communication and productivity and a way to facilitate global access to science. The interplay of preprinting practices with formal peer-reviewed publication is an open question that is now tractable to targeted analysis. Here, we take advantage of nearly a decade of preprint solicitation at Proceedings B to address this question, which offers a quasi-experimental setting for comparing outcomes for solicited preprints and traditionally submitted manuscripts. We used submission records from 15,368 manuscripts from 2018-2024 to assess whether preprint solicitation affects acceptance rates, journal scope, author geographic diversity, and post-publication uptake. Solicited manuscripts were accepted at nearly twice the rate of traditional submissions and received more downloads and citations after publication. Submission topics were broadly similar between the two routes, though solicited manuscripts showed marginally greater subject diversity. Geographic disparities favoring the Global North persisted, but acceptance rates for solicited manuscripts converged across regions, narrowing the gap seen in traditional submissions. These findings extend earlier work on this initiative and indicate that editor-driven solicitation can identify high-quality preprints, broaden geographic representation, and increase research visibility, offering a replicable model for journals seeking to diversify authorship while maintaining editorial standards.

scientific communication and education↗
bioRxiv2026

Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers

Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.

scientific communication and education↗
bioRxiv2026

The Impact of Small-group Education on Student Wellbeing in Undergraduate Biology: a Student Perspective on Self-Determination Theory

Alarming reports about mental health have raised awareness of student wellbeing in STEM Education. Self-Determination Theory (SDT) implies that wellbeing can be improved by fulfilling the needs for autonomy, competence and relatedness. Educational design elements such as small-group teaching, peer feedback and offering a low-stakes practice environment can help fulfill these SDT needs. These elements were applied in a new academic skills course in our undergraduate Biology program. Here, we investigated how this affected the students sense of autonomy, competence and relatedness, and whether this was related to wellbeing. First, a survey among 48 students confirmed that student wellbeing is positively correlated to SDT fulfillment. Next, group interviews with students revealed that wellbeing is mostly dependent on relatedness, which is facilitated through small-group teaching in the first semester. Furthermore, students reported that (1) relatedness evolves through bonding with peers over the shared experience of struggle, (2) competence requires (peer) feedback and opportunities for growth, especially in laboratory coursework and (3) a structured, low-stakes environment supports competence, but without optimal challenge, also undermines a sense of autonomy. This illustrates how students perceive the effects of small-group educational activities on their wellbeing through the lens of Self-Determination Theory.

scientific communication and education↗