Enhancing Verb‐Noun Collocation Learning among Chinese EFL Learners: A Comparison of Cognitive Linguistics‐Inspired, GenAI‐Assisted, and Traditional Instruction

ABSTRACT This study investigates the effects of Cognitive Linguistics (CL)‐inspired, Generative AI (GenAI)‐assisted, and traditional instruction on Chinese EFL learners' knowledge and retention of English verb‐noun collocations. A quasi‐experiment was conducted involving three intact classes with a total of 133 second‐year college students randomly assigned to one of the three instructional treatments. Learners’ learning of verb‐noun collocations was assessed through pre‐, immediate post‐, and delayed posttests, while their subjective assessment of the instruction was captured via a learner questionnaire. The results indicate that the CL‐inspired instruction was the most effective in both knowledge gains and retention, highlighting the benefits of emphasizing the conceptual and semantic motivations behind collocations to create meaningful and lasting learning outcomes. The GenAI‐assisted group demonstrated moderate improvement but lagged in long‐term retention, suggesting that GenAI tools may not foster deep cognitive engagement in processing and analyzing input necessary for durable learning. Learner assessment corroborated these findings, with CL participants reporting high satisfaction due to the instruction's emphasis on semantic understanding, while GenAI users appreciated its efficiency but reported limited opportunities for active reasoning and deep cognitive engagement with the learning process. The study underscores the potential of CL‐inspired instruction for promoting meaningful collocation learning and calls for further exploration of integrating GenAI tools with cognitively engaging strategies.