Reimagining plant science training in the era of generative AI: A global perspective

Abstract

In recent years, a deluge of big and diverse datasets from hundreds of plant species coupled with spectacular innovations in artificial intelligence (AI) and generative AI (GenAI), has altered the landscape of plant science. These developments are increasingly democratizing the field, reducing the entry barriers to complex data analysis and enabling a new wave of innovative research while introducing new challenges. Therefore, in this era, it is critical that we train the next generation of plant scientists to be AI-literate, i.e., not only proficient in using AI but also vigilant about its pitfalls and biases. In this Perspective, we call for six strategic shifts necessary for training the next generation of plant scientists. We argue that while maintaining a core focus on subject expertise, educators should simultaneously emphasize development of new AI-forward pedagogical and evaluation frameworks that reward interdisciplinary and critical thinking, human-driven knowledge synthesis, self-directed learning, and conceptual understanding of workflows. For effective critique and sound interpretations based on biological reality, plant scientists must be explicitly trained in recognizing biases underlying GenAI models. Finally, we highlight the structural barriers hindering the equitable and ethical use of GenAI, where awareness and resolution is critical for sustainable growth of the field. Through the above conceptual framework and numerous plant-science focused illustrative activities, examples, and resources meant for students and educators alike, this Perspective defines high-level emphasis areas for GenAI-enabled scientific training, aimed at creating a more effective, engaged, and adaptive community of plant scientists.