arXiv subjects

Sergei L. Kosakovsky Pond

Publications and source records attributed to Sergei L. Kosakovsky Pond.

4 recordsLinked to original sources

arXiv2026

Changing the Optics: Comparing Traditional and Retrieval-Augmented GenAI E-Tutorials in Interdisciplinary Learning

Understanding information-seeking behaviors in e-learning is critical, as learners must often make sense of complex and fragmented information, a challenge compounded in interdisciplinary fields with diverse prior knowledge. Compared to traditional e-tutorials, GenAI e-tutorials offer new ways to navigate information spaces, yet how they shape learners information-seeking behaviors remains unclear. To address this gap, we characterized behavioral differences between traditional and GenAI-mediated e-tutorial learning using the three search modes of orienteering. We conducted a between-subject study in which learners engaged with either a traditional e-tutorial or a GenAI e-tutorial accessing the same underlying information content. We found that the traditional users maintained greater awareness and focus of the information space, whereas GenAI users exhibited more proactive and exploratory behaviors with lower cognitive load due to the querying-driven interaction. These findings offer guidance for designing tutorials in e-learning.

cs.HC↗

arXiv2026

Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning

This full research paper investigates the impact of generative AI (GenAI) on the learner experience, with a focus on how learners engage with and utilize the information it provides. In e-learning environments, learners often need to navigate a complex information space on their own. This challenge is further compounded in interdisciplinary fields like bioinformatics, due to the varied prior knowledge and backgrounds. In this paper, we studied how GenAI influences information search in bioinformatics research: (1) How do interactions with a GenAI chatbot influence learner orienteering behaviors?; and (2) How do learners identify information scent in GenAI chatbot responses? We adopted an autoethnographic approach to investigate these questions. GenAI was found to support orienteering once a learning plan was established, but it was counterproductive prior to that. Moreover, traditionally value-rich information sources such as bullet points and related terms proved less effective when applied to GenAI responses. Information scents were primarily recognized through the presence or absence of prior knowledge of the domain. These findings suggest that GenAI should be adopted into e-learning environments with caution, particularly in interdisciplinary learning contexts.

cs.HC↗

arXiv2025

WIP: Identifying Tutorial Affordances for Interdisciplinary Learning Environments

This work-in-progress research paper explores the effectiveness of tutorials in interdisciplinary learning environments, specifically focusing on bioinformatics. Tutorials are typically designed for a single audience, but our study aims to uncover how they function in contexts where learners have diverse backgrounds. With the rise of interdisciplinary learning, the importance of learning materials that accommodate diverse learner needs has become evident. We chose bioinformatics as our context because it involves at least two distinct user groups: those with computational backgrounds and those with biological backgrounds. The goal of our research is to better understand current bioinformatics software tutorial designs and assess them in the conceptual framework of interdisciplinarity. We conducted a content analysis of 22 representative bioinformatics software tutorials to identify design patterns and understand their strengths and limitations. We found common codes in the representative tutorials and synthesized them into ten themes. Our assessment shows degrees to which current bioinformatics software tutorials fulfill interdisciplinarity.

cs.CY↗

arXiv2012

Lineage-specific transposons drove massive gene expression recruitments during the evolution of pregnancy in mammals

A major challenge in biology is explaining how novel characters originate, however, the molecular mechanisms that underlie the emergence of evolutionary innovations are unclear. Here we show that while gene expression in the uterus evolves at a slow and relatively constant rate, it has been punctuated by periods of rapid change associated with the recruitment of thousands of genes into uterine expression during the evolution of pregnancy in mammals. We found that numerous genes and signaling pathways essential for the establishment of pregnancy and maternal-fetal communication evolved uterine expression in mammals. Remarkably the majority of genes recruited into endometrial expression have cis-regulatory elements derived from lineage-specific transposons, suggesting that that bursts of transposition facilitate adaptation and speciation through genomic and regulatory reorganization.

q-bio.PE↗