Nigel J Francis, David P Smith
Scientific discovery is built on the rigorous interrogation of the unknown. In immunology, exploring novel mechanisms requires researchers to identify what is unknown, reconcile contradictory data through original thinking, and integrate disparate ideas to generate testable hypotheses. Undertaking this process is how students construct their own knowledge and experiences. However, the rapid integration of Generative AI (GenAI) into higher education risks short-circuiting this cognitive process. Time-poor students, facing mounting pressure, increasingly use GenAI to offload thinking onto algorithms, sidestepping the very struggle that develops scientific curiosity and critical judgment. This opinion piece argues that without adequate guardrails, we risk training scientists who simulate competence but lack the capacity for genuine discovery. Taught programmes must be designed with academic integrity in mind and embrace assessment innovations. Rather than engaging in a futile arms race to detect outputs, educators must shift assessment from traditional final products, such as essays and lab reports, to validating the research process itself and assessing directly the capacities that future graduates will need. By assessing research trails, interpretation of raw data, and the thinking process behind GenAI use (identifying gaps, bias, and hallucinations) through viva-style interactions, we ensure that student cognitive effort remains the driver of the work. Ultimately, the quality of future immunological research depends on the integrity of our current assessment methods.