OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design

Abstract

The proliferation of generative artificial intelligence (GenAI) has fundamentally challenged the validity of traditional assessments in higher education. In data-rich, laboratory-based disciplines such as undergraduate plant physiology, conventional product-heavy assessments—like essay writing and closed-book examinations—often fail to capture underlying scientific reasoning and are highly susceptible to GenAI simulation. Drawing upon frameworks of constructive alignment, evaluative judgment, and authentic assessment, this paper argues for a systemic shift from misconduct management to “AI-resilient” structural task design. We propose replacing the traditional assessment paradigm with an integrated, seven-part ecology of multimodal tasks. This reconstructed system distributes evidence of learning across the semester through in-class rapid reasoning exercises, process-oriented laboratory notebooks, collaborative inquiry projects, interactive oral defenses (mini-vivas), and reflective AI-use portfolios. By prioritizing visible reasoning, structural verification of knowledge, and critical AI literacy over unsupervised textual output, this framework ensures that students develop and demonstrate authentic disciplinary competence. Ultimately, this approach not only safeguards academic integrity but also enhances the educational validity of plant science curricula, offering a scalable template for assessment reform across the biosciences.