The research, led by Victor de Brito of the Cornell University Museum of Vertebrates, addresses a persistent problem in the museum world. Collections serve research, education, conservation, and public outreach, and a central goal is making their growing volumes of data accessible to researchers, educators, and the public. In most cases that access happens through institutional websites and web applications, but limited access to programming expertise, dedicated personnel, and large-scale data management infrastructure remains a major challenge for many collections.

Large-scale aggregators such as the Global Biodiversity Information Facility, VertNet, and MorphoSource have substantially improved data availability across institutions. However, the study notes that these platforms often lack the most up-to-date information, because collections upload their data in episodic batches. They are also vulnerable to shifts in funding policies that may limit hosted data or impose new fees on partner institutions. Aggregators are generally not tailored to the specific needs of individual collections, and the hosting institutions are often overlooked in acknowledgments by authors of downstream analyses.

Recent advances in generative artificial intelligence, particularly large language models that understand and generate human-like text, have transformed how developers create, maintain, optimize, and test code. The scale of adoption is striking: GitHub, the largest repository for software development projects, added more than 36 million developers in 2025 and reached over 180 million users, a surge largely attributed to AI coding tools. A survey found that 90 percent of software developers at Google reported using AI in their work, and 71 percent of those who write code used AI-assisted coding. By translating natural language instructions into executable code, these systems substantially lower the technical barrier to software creation.

To test whether that barrier has fallen far enough for museum professionals, the researcher developed two applications using GPT-5.3-Codex on Windows 11 through Visual Studio Code, with a medium reasoning effort setting. Both were built from .csv files exported from the museum’s Specify 7 database, tested locally, and then deployed online using GitHub Pages. The repositories included an AI-generated code disclosure statement and an MIT License file clarifying usage and distribution permissions.

The first application is a specimen search portal for the museum’s ichthyology collection. Built with React, TypeScript, Vite, Tailwind CSS, Leaflet for mapping, and PapaParse for CSV parsing, it allows users to filter specimen records by order, family, genus, species, catalog number, preparation type, date, and geography. Filters support dropdown lists with autocomplete, multi-select, and dynamic interdependence, meaning that selecting one value updates all other dropdowns to show only compatible options. Users can export filtered results as a new .csv file and view an interactive map of sampling localities that updates live as filters change, with clicking a map point highlighting the corresponding table row.

The second application is an interactive visualization of sampling localities across the museum’s four vertebrate collections, covering mammals, birds, reptiles and amphibians, and fishes. Color-coded, semi-transparent points distinguish the collections so overlap density remains visible, and an animated timeline makes points appear year by year from earliest to latest. Playback controls include play and pause, speed settings, year navigation, a loop toggle, and a draggable year slider. Below the map, a Plotly line chart displays cumulative specimen and lot counts over time, with one line per collection and a black total line, updating dynamically as the selected year changes.

Validation showed the tools perform accurately. The specimen search portal produced results consistent with the museum’s existing Specify Web Portal across all 50 randomly selected genus and species combinations tested, with query results exported as .csv files and compared in R to identify any catalog numbers present in one portal but absent in the other. The interactive map accurately reproduced specimen localities when visually compared with coordinates exported from the Specify database and plotted in QGIS. Once the data structure was exported, functional applications were generated and tested in less than 30 minutes, allowing staff to rapidly test different ways of presenting and exploring their data.

The study also weighs alternatives and caveats. Low-code and no-code platforms allow tool development with minimal programming, but they constrain customization, lock users into platform-specific architectures, and carry subscription costs. AI-assisted coding instead generates fully editable source code, offering greater adaptability as taxonomies, digitization priorities, and data standards evolve. However, large language models are stochastic by design, sampling from probability distributions, so results varied with each execution even when the same prompt was repeated. Clearly structured prompts describing interface elements, functionality, data structure, and outputs in detail greatly improve code quality, and multi-step refinement remains essential for producing fully functioning applications.

Several practical considerations accompany adoption. AI-assisted coding works best when users have some programming knowledge, since they remain responsible for troubleshooting, maintenance, and deployment, effectively acting as their own technical support. Complexity raises the skill requirement: both applications here operate from exported .csv files and are tightly coupled to the original data structure, so connecting to a live server or database would require substantial refactoring. Data security is critical, and sensitive information should never appear in publicly accessible applications or connect directly to live institutional databases; exported datasets or copies reduce breach risks. Access to AI platforms may require paid subscriptions, though applications can be developed quickly and maintained independently afterward, minimizing long-term expense. The broader challenge now shifts from implementation to identifying meaningful tools and sharing them in standardized ways across institutions, since rapidly generated customized tools risk fragmenting database ecosystems. With careful prompt design and attention to interoperability standards such as Darwin Core, the approach could transform collection staff from end-users of pre-built systems into active designers of their own digital infrastructure.

Subject of Research: AI-assisted coding for developing digital tools in natural history collections

Article Title: Using AI-assisted coding to build digital tools for natural history collections

Article References: Using AI-assisted coding to build digital tools for natural history collections. (n.d.). https://doi.org/10.3897/nhcm.3.191202

Image Credits: AI Generated

DOI: 10.3897/nhcm.3.191202

Keywords: AI-assisted coding, natural history collections, large language models, GPT-5.3-Codex, Cornell University Museum of Vertebrates, specimen data, digitization, web applications, data visualization, GBIF, Specify, museum informatics

During the study, a cohort of 71 junior and senior computer science students engaged with GitHub Copilot over several weeks. Initially, half of the participants were unfamiliar with the AI assistant. After an intensive 80-minute session introducing Copilot’s functionalities—centered on AI-driven code synthesis via large language models—students were encouraged to employ the tool across tasks of varying complexity. Early findings revealed a surge in students’ trust; approximately half reported heightened confidence in Copilot’s capabilities shortly after exposure. Yet, this initial enthusiasm presented only one facet of a more nuanced evolution in trust.

Extending beyond initial interactions, students embarked on a 10-day project involving modifications within a large-scale, open-source codebase. This endeavor aimed to emulate real-world programming challenges where understanding and navigating vast code structures is paramount. Throughout the project, students relied on Copilot to augment their coding, but reflections at the conclusion showed marked shifts in perception. Notably, around 39% expressed increased trust, while nearly 37% conveyed diminished confidence in the tool. Approximately a quarter reported no significant change in trust levels.

This bifurcation underscores the complexities of integrating AI assistants in programming education. While generative AI accelerates code production and potentially boosts productivity, it also exposes students to incorrect or suboptimal code outputs. AI tools occasionally generate syntax or logic errors and might embed vulnerabilities that could have serious security implications if uncritically accepted. Consequently, students recognized that mastery of programming principles remains indispensable, enabling them to critically evaluate AI suggestions and maintain rigorous debugging discipline.

From a pedagogical perspective, this insight advances the argument that to harness AI’s transformative potential, computer science curricula must evolve. Educators are challenged to craft learning experiences where students actively engage with AI assistants for a spectrum of coding tasks — from isolated algorithms to contributions within extensive, multifile projects. Such exposure not only calibrates expectations about AI’s strengths and limitations but also reinforces the necessity for students to retain and deepen their own coding proficiency.

Equally important is ensuring students develop the capacity to maintain comprehension, modification, testing, and debugging skills independent of AI assistance. This skillset is critical, as overreliance on AI can erode fundamental programming fluency, leaving graduates ill-prepared to scrutinize or improve AI-generated code in professional contexts. The researchers emphasize that understanding the underlying mechanics of AI outputs—rooted in natural language processing and probabilistic text generation—is vital for users to grasp why AI may produce flawed solutions under certain conditions.

Moreover, educators are encouraged to articulate and demonstrate practical techniques within AI tools that amplify their utility in managing large codebases. Features like contextual file inclusion and command keywords (“/explain”, “/fix”, “/docs”) can empower students to leverage AI effectively while comprehending the rationale behind the generated code. By framing AI as a collaborative partner rather than a replacement for human expertise, instruction can foster balanced trust that evolves with experience.

The study’s findings hold broader implications as generative AI assistants become ubiquitous in software development workflows. While immediate productivity gains are attractive, cultivating the discernment to critically assess AI contributions remains paramount in sustaining software quality and security. Graduates must emerge with the dual competencies of proficient standalone programming and adept interaction with intelligent tools.

Researchers plan to extend their inquiry with a larger sample size of 200 students in an upcoming winter quarter, aiming to refine recommendations and validate patterns across diverse educational settings. This scaling reflects the urgency of preparing the next generation of programmers to navigate an AI-augmented future responsibly and effectively.

Ultimately, this research reinforces that while AI assistants bring revolutionary capabilities to programming, they do not—and should not—replace the foundational knowledge and skills intrinsic to computer science education. Instead, these tools require a complementary pedagogical model that fosters judicious use, critical evaluation, and continuous learning, ensuring that emerging professionals remain both innovative and vigilant.

As AI continues to evolve, educators and institutions face the dual challenge of embracing novel technologies while preserving rigorous educational standards. Integrating AI programming assistants thoughtfully within curricula presents an unprecedented opportunity to enhance learning outcomes, propel innovation, and prepare students for a workforce in which human-AI collaboration becomes the norm.

The researchers’ work thereby provides a critical roadmap for the future of computer science education—one that aligns student trust with competence, leveraging generative AI to enrich, rather than undermine, the development of programming expertise.

Subject of Research: People

Article Title: Evolution of Programmers’ Trust in Generative AI Programming Assistants

News Publication Date: 11-Nov-2025

Web References: Evolution of Programmers’ Trust in Generative AI Programming Assistants (arXiv)

References:
Anshul Shah, Elena Tomson, Leo Porter, William G. Griswold, and Adalbert Gerald Soosai Raj. Department of Computer Science and Engineering, University of California San Diego
Thomas Rexin, North Carolina State University

Image Credits: University of California San Diego

Keywords: Generative AI, Artificial Intelligence, Computer Science, Education, Education Technology, Educational Methods