在教育評量技術中，題目反應理論（Item Response Theory；IRT），克服了許多在傳統教育與心理測驗中對測驗建構、評量與使用的問題，為數不少的研究與高品質測驗之建構均以此理論完成；然而，一般選題策略的方法，雖然很有效率卻缺乏彈性，以至於建構之測驗很難趨近設計者測驗訊息量之需求。本文中，我們將提出一人工智慧技術“類神經網路“做為選題策略，其時間（運算）複雜度與傳統方法，而與其他新近研究結果相近，肯定了本研究所提之人工智慧技術，對測驗發展技術給予一新的研究方向。

　　　　　In the educational measurement, the item response theory (IRT) has overcome many shortcomings the ways in which educational and psychological tests are usually constructed, evaluated and used. Many research results on item selection are based on the item response models, and obtained a good quality of testing. However, the traditional item selection methods are not flexible enough to satisfy the target information of the designed test. In the paper, we propose an AI technique-neural network to select test items such that the difference of information between the constructed test and the desired test is greatly reduced. The simulation results are similar to the related works proposed in the past ten years. In addition the time complexity of the proposed neural network method is the same as the traditional methods. The proposed method significantly reduces the errors of the information between the constructed test and the desired test while maintaining the efficiency of the item selection process, and then gives a new research direction on the test development.