Abstract:

Artificial Intelligence (AI) is increasingly influencing Landscape Architecture by supporting ideation, spatial exploration, and visual representation. In educational contexts, these tools can enrich teaching and learning by enabling students to generate, compare, and refine design alternatives more efficiently. This study focused on the evaluation of AI tools for educational use in Landscape Architecture, with particular attention to their pedagogical value, functional differences, and contribution to the early stages of the design process.

The research involved a systematic review and categorisation of AI tools currently used in landscape design, followed by a comparative evaluation based on predefined technical, functional, and educational criteria. Four representative tools—Ideal.House, Neighborbrite, mnml.ai, and DreamzAR—were selected and examined through a case study in order to assess their usefulness in supporting student learning, visual experimentation, and design decision-making.

The findings showed that AI tools can make a meaningful contribution to Landscape Architecture education, especially in the phases of concept generation, rapid visualisation, and comparative exploration of alternative proposals. Ideal.House was found to be particularly effective for early ideation and for introducing broad stylistic directions. Neighborbrite provided stronger connections to real site conditions through image-based redesign. Mnml.ai proved to be the most balanced and flexible tool, offering coherent and more photorealistic outputs while preserving the geometry of the existing landscape. DreamzAR supported rapid visualisation and interactive exploration, although its outputs showed lower realism and some technical limitations.

Despite these advantages, the evaluation also identified significant constraints related to precision, environmental and climatic accuracy, realism of planting representation, and limited control over detailed design decisions. These limitations indicate that AI tools should not be treated as substitutes for professional design software or expert judgement. Instead, their educational value lies in their role as complementary learning tools that strengthen creative exploration, accelerate the production of alternatives, and promote critical reflection on generated outcomes.

Overall, the study demonstrates that the evaluation of AI tools for educational purposes in Landscape Architecture is essential for understanding their role in design education. The results suggest that the combined use of multiple AI tools can provide broader and more effective educational support than reliance on a single platform.

Acknowledgement:
This publication is part of the TALLHEDA project that has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101136578. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.

Keywords:

Artificial Intelligence, AI tools, Landscape Architecture education, educational technology, design education, visualisation, ideation, design decision-making.