Humans outperform large language models (LLMs) on sophisticated tasks because human cognition involves a range of cognitive functions and their dynamic interactions. This study explores how integrating human cognition through concept-guided instruction and few-shot teaching in the prompt can guide LLMs to improve translation outcomes. We first demonstrate that for simple and widely used concepts, concept-guided prompting approaches offer significant benefits. We then test prompt engineering with Chinese-to-English translation examples, using hypothetical spaces—generated by GPT4—to estimate the complexity of various concepts and Likert scores—generated by human experts—to evaluate the translation performance. Our findings show that LLM translation performance declines as concept complexity increases. We also identify additional challenges: LLMs struggle with continuity in explaining and practicing sophisticated concepts due to the lack of human-like cognitive functions, such as cognitive dissonance. Additionally, LLMs lack a graceful speed-accuracy tradeoff because they do not possess the dynamic information processing, response strategies, and performance assessment that humans do. However, LLMs can mitigate some of these challenges by using Chain-of-Thought (CoT) reasoning, which is especially effective for problems requiring consistent, well-structured reasoning steps. Despite this, LLMs can only represent the effects of complex human cognitive functions through (often) fragmented linguistic descriptions, whereas humans excel at understanding critical and broader contexts and the interconnections between cognitive aspects.