FROM STATISTICIAN-DEPENDENT PRACTICE TO DATA-ANALYTIC AGENCY: AN ACTIVITY-THEORETICAL CASE FOR TRAINING VETERINARY AND HEALTH PROFESSIONALS AS DATA ANALYSTS IN THE AGE OF AI
A SYSTEMIC-STRUCTURAL ACTIVITY THEORY ARGUMENT FOR INTEGRATED DATA WORK IN VETERINARY MEDICINE, ANIMAL HUSBANDRY, AND HEALTH SCIENCES
DOI:
https://doi.org/10.29121/JISSI.v2.i2.2026.35Keywords:
Activity Theory, Systemic-Structural Activity Theory, Data Literacy, Veterinary Informatics, Rstudio, Quarto, Reproducible Research, Machine Learning, Large Language ModelsAbstract
Veterinary medicine, animal husbandry, and health sciences operate in data-saturated ecologies shaped by electronic records, sensor systems, laboratory platforms, and increasingly automated analytic infrastructures. In this context, the dominant educational dilemma is not whether professionals should be statistically literate, but whether they should remain dependent on specialist analysts for the core analytic actions that define contemporary evidence-based practice, production optimization, and welfare governance. This article argues that training veterinarians, zootechnists, and health professionals as data analysts is superior to training them primarily as statisticians or as researchers who routinely outsource analytic work. The argument is grounded in cultural-historical activity theory and Bedny’s systemic-structural activity theory, and it is updated for a technical environment in which machine learning, large language models, and agentic workflows reduce the friction of coding and documentation while increasing the need for domain-grounded judgement. R, RStudio, and Quarto are presented as an exemplary toolchain that unifies analysis, explanation, and publication into a single reproducible activity. The conclusion proposes a competence-oriented curriculum in which communicative, transformative, and evaluative capabilities co-develop through authentic analytic work tied to welfare and community responsibility.
References
Allaire, J. J. (2011). RStudio: Integrated Development Environment for R. User! 2011 Conference Abstracts.
Bedny, G., and Karwowski, W. (2006). A Systemic-Structural Theory of Activity: Applications to Human Performance and Work Design. CRC Press. https://doi.org/10.1201/9781420009743 DOI: https://doi.org/10.1201/9781420009743
Donoho, D. (2017). 50 Years of Data Science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734 DOI: https://doi.org/10.1080/10618600.2017.1384734
Engeström, Y. (1987). Learning by Expanding: An Activity-Theoretical Approach to Developmental Research. Orienta-Konsultit.
Fajt, V. R., Brown, D., and Scott, M. M. (2009). Practicing the Skills of Evidence-Based Veterinary Medicine Through Case-Based Pharmacology Rounds. Journal of Veterinary Medical Education, 36(2), 186–195. https://doi.org/10.3138/jvme.36.2.186 DOI: https://doi.org/10.3138/jvme.36.2.186
Hazarika, I. (2025). Impact of Consumer Analytics and AI on Inventory Management in UAE with Reference to UN SDG 12: Sustainable Consumption and Production Patterns and SDG 2 Zero Hunger in UAE. In 2025 IEEE Integrated STEM Education Conference (ISEC) (1–8). IEEE. https://doi.org/10.1109/ISEC64801.2025.11460353 DOI: https://doi.org/10.1109/ISEC64801.2025.11460353
Jiang, J., et al. (2024). A Survey on Large Language Models for Code Generation. ACM Computing Surveys. Advance Online Publication.
Jiang, R. A., Aburajouh, H., and Catal, C. (2025). Large Language Models for Code Completion: A Systematic Literature Review. Computer Standards and Interfaces. Advance Online Publication. https://doi.org/10.1016/j.csi.2024.103917 DOI: https://doi.org/10.1016/j.csi.2024.103917
Johnson, L. M., Ames, T. R., Jacko, J. A., and Watson, L. A. (2011). The Informatics Imperative in Veterinary Medicine: Collaboration Across Disciplines. Journal of Veterinary Medical Education, 38(1), 5–9. https://doi.org/10.3138/jvme.38.1.5 DOI: https://doi.org/10.3138/jvme.38.1.5
Kasarapu B. C. (2026). Generative AI-Enabled Micro-Frontend Framework for Scalable and Intelligent Enterprise Retail Applications. International Journal of Computer Information Systems and Industrial Management Applications, 18(5s), 297–309. https://doi.org/10.70917/ijcisim-2026-2708 DOI: https://doi.org/10.70917/ijcisim-2026-2708
Knuth, D. E. (1984). Literate Programming. The Computer Journal, 27(2), 97–111. https://doi.org/10.1093/comjnl/27.2.97 DOI: https://doi.org/10.1093/comjnl/27.2.97
Leontiev, A. N. (1978). Activity, Consciousness, and Personality. Prentice-Hall.
Liu, J., Wang, K., Chen, Y., Peng, X., Chen, Z., Zhang, L., and Lou, Y. (2024). Large Language Model-Based Agents for Software Engineering: A Survey (Arxiv Preprint).
Menendez, H. M., Brennan, J. R., Gaillard, C., Ehlert, K., Quintana, J., Neethirajan, S., Remus, A., Jacobs, M., Teixeira, I. A. M. A., Turner, B. L., and Tedeschi, L. O. (2022). ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and Challenges of Confined and Extensive Precision Livestock Production. Journal of Animal Science, 100(6), Article Skac160. https://doi.org/10.1093/jas/skac160 DOI: https://doi.org/10.1093/jas/skac160
Peng, R. D. (2011). Reproducible Research in Computational Science. Science, 334(6060), 1226–1227. https://doi.org/10.1126/science.1213847 DOI: https://doi.org/10.1126/science.1213847
Posit Team. (2025). RStudio: Integrated Development Environment for R (Version 2025) [Computer Software]. Posit Software, PBC.
Quarto Development Team. (2025). Quarto: Scientific and Technical Publishing System [Computer Software].
R Core Team. (2025). R: A Language and Environment for Statistical Computing [Computer Software]. R Foundation for Statistical Computing.
Sandve, G. K., Nekrutenko, A., Taylor, J., and Hovig, E. (2013). Ten Simple Rules for Reproducible Computational Research. PLoS Computational Biology, 9(10), Article e1003285. https://doi.org/10.1371/journal.pcbi.1003285 DOI: https://doi.org/10.1371/journal.pcbi.1003285
Tukey, J. W. (1962). The Future of Data Analysis. The Annals of Mathematical Statistics, 33(1), 1–67. https://doi.org/10.1214/aoms/1177704711 DOI: https://doi.org/10.1214/aoms/1177704711
Weber, T., Brandmaier, M., Schmidt, A., and Mayer, S. (2024). Significant Productivity Gains Through Programming with Large Language Models. In Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3661145 DOI: https://doi.org/10.1145/3661145
Wickham, H., and Grolemund, G. (2023). R for Data Science (2nd ed.). O'Reilly Media.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., ... Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), Article 1686. https://doi.org/10.21105/joss.01686 DOI: https://doi.org/10.21105/joss.01686
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Copyright (c) 2026 Federico De la Colina Flores , Paul Alexis de la Colina Flores , Tzitzi De la Colina García , Heriberto Rodríguez Frausto (Author)

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