Abstract:

The integration of digital simulation tools in higher education has become an essential pillar for technical disciplines, especially where physical laboratory access is constrained by logistical or economic factors. This paper presents an innovative methodological framework for creating virtual chemistry laboratories specifically tailored for first-year Forest Engineering students. The core of this research explores a novel solution to the "programming skill gap" often faced by specialized educators: the use of Generative Artificial Intelligence (AI) as a primary software architect.

To demonstrate the viability of this approach, the article presents a practical case study focused on a Calorimetry laboratory session. The development workflow is structured through a sophisticated two-stage AI-mediated pipeline. In the first phase, a "macro-prompt" is engineered using a Large Language Model (LLM), such as Gemini. This prompt serves as a bridge between pedagogical intent and technical requirements, synthesizing experimental protocols, safety guidelines, and the thermodynamic equations of calorimetry, all extracted directly from traditional laboratory manuals. In the second phase, this structured logic is processed by a specialized coding AI, like Google Antigravity, which manages the framework-based architecture, automated debugging, and cloud deployment of the final web application.

The resulting simulation allows students to virtually measure heat transfer and specific heat capacities, variables crucial to forest biomass studies and thermal wood properties. Preliminary results suggest that this AI-assisted pipeline significantly reduces development time and lowers the technical barrier for faculty, providing students with a ubiquitous, web-based platform to reinforce practical knowledge. Ultimately, this study demonstrates that AI-mediated software engineering democratizes the creation of high-quality, customized educational resources, fostering a more resilient and accessible learning environment in specialized engineering fields.

Keywords:

Low-code Development, AI Assisted Simulation, Engineering Training, Virtual laboratory.