In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties. These difficulties often result in the generation of misleading or incorrect information, primarily due to their limited capacity to process evolving factual knowledge and complex temporal logic. In response, we propose a novel, constructivism-based approach that advocates for a paradigm shift in LLM learning towards an active, ongoing process of knowledge synthesis and customization. At the heart of our proposal is the Abstract Reasoning Induction ARI framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This division aims to reduce instances of hallucinations and improve LLMs' capacity for integrating abstract methodologies derived from historical data. Our approach achieves remarkable improvements, with relative gains of 29.7\% and 9.27\% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code will be released at https://github.com/czy1999/ARI.
