Charles Sanders Peirce’s sign theory has shaped much of my research on visualization, knowledge organization, peer review, and, more recently, artificial intelligence. My early work used Peirce’s semiotics to examine concept mapping, classification, visual representation, and open-source tools for analyzing signs. This included the development of an R package based on Peirce’s sign theory, as well as studies connecting semiotics to knowledge organization and visual communication.
Over time, this work has expanded from visual signs and scholarly classification to the broader problem of digital semiosis: how meaning is produced, represented, interpreted, and evaluated in computational environments. This shift has become especially important in the age of generative AI. AI systems now produce texts, classifications, explanations, visualizations, and other signs that appear meaningful, but they do not interpret those signs in the human sense. This raises important questions for semiotics, information science, education, and scholarly judgment.
My recent work with Martin Thellefsen develops this direction through Peircean digital semiotics and AI. We argue that Peirce’s triadic model of sign, object, and interpretant offers a stronger theoretical foundation for understanding AI-generated signs than models that treat meaning as a purely formal or statistical output. This work also connects Peircean semiotics to domain analysis, machinic interpretation, visual communication, and the changing role of human judgment in AI-mediated knowledge environments.
This research program therefore connects three phases of my work: early semiotic and knowledge organization studies, open-source R development, and current work on digital semiotics, AI, visualization, and peer review.
Here is my work:
2025 Friedman, A., & Thellefsen, M. “The Peircean Theory of AI: Advancing Text Generation Through Peirce’s Triadic Model, Speculative Grammar, and Methodeutics.” Digital Age in Semiotics & Communication, Vol. VIII, 50–70.
2025 Thellefsen, M., & Friedman, A. “Knowledge without a knower? Domain analysis in the age of machinic interpretation.” AI & Society.
2025 Thellefsen, M., & Friedman, A. “From realism and socio-cognitivism to AI constructs: enhancing domain analysis through artificial intelligence?” Information Research: An International Electronic Journal.
2024 Dinh, L., Friedman, A., & Hawley, K. “Examining peer review network dynamics in higher education visual communication courses using ERGM.” Computers and Education Open.
2024 Friedman, A., & Beasley, Z. “Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education.” Journal of Statistics and Data Science Education.
2023 Thellefsen, M., & Friedman, A. “Icons and metaphors in visual communication: The relevance of Peirce’s theory of iconicity for the analysis of visual communication.” Public Journal of Semiotics, 10(2).
2022 Friedman, A., & Thellefsen, M. “Big data visualization through the lens of Peirce’s visual sign theory.” Punctum: International Journal of Semiotics, 08.
2022 Friedman, A. “Visualizing protein data sets in R through a student peer-review rubric.” Biochemistry and Molecular Biology Education.
2017 Friedman, A., & Feichtinger, E. “Peirce’s sign theory as an open-source R package.” Signs: International Journal of Semiotics, Vol. 8.
2013 Friedman, A., & Smiraglia, R. P. “Nodes and Arcs: Concept Map, Semiotics, and Knowledge Organization.” Journal of Documentation, Vol. 69 No. 1. Emerald Group Publishing Limited.
2011 Friedman, A., & Thellefsen, M. “Concept Theory and Semiotics Theory in Knowledge Organization.” Journal of Documentation, Vol. 67 No. 4. Emerald Group Publishing Limited.