# TwinStar: A Novel Design for Enhanced Test Question Generation Using Dual-LLM Engine
## 1 Introduction  
Large language models (LLMs), such as GPT-4 [1, 2], Gemini [3], Claude [4], LLaMA [5], and ChatGLM [6, 7], have brought transformative changes across various application domains, including machine translation [8], text summarization [9], sentiment analysis [10], medical advice consultation [11, 12], and legal document analysis [13, 14], with powerful capabilities in natural language understanding and generation [15]. A trend of applying LLMs to professional domains with specialized requirements that stimulates research interest across various fields also casts a profound impact on the field of education. LLM-based AI technology not only provides reading or writing assistance [16, 17] but also aims to be involved in the process of teaching and learning [16, 18, 19]. Specifically, the technique of generating high-quality test questions exhibits sophisticated intelligence, emphasizing thatthe purpose of a test in teaching processes is to evaluate the development curve of cognitive levels for students in certain learning stages. Our study, as well as previous research [19; 20], shows that LLMs, even those as immense as GPT-4 and Gemini, can hardly fulfill the design objectives of test question generation, lacking clear focus on cognitive levels pertaining to specific knowledge points and also exposing the limitations of intelligence displayed by LLMs. An interesting question is whether an LLM is able to generate high-quality test questions without a substantial question bank. It is also desirable to further understand the resources that need to be invested accordingly in order to achieve a certain level of intelligence in solving domain-specific problems. A meaningful exploration of the possibilities of upgrading the intelligence demonstrated by LLMs will become an important step toward highly effective AI-assisted teaching and learning. This paper studies the problem of generating high-quality test questions with specified knowledge points and target cognitive levels. As oppose to the well-studied classical test design problem, also known as the optimal test design on a question bank with multiple objectives, the test question generation problem studied in this paper focuses on the connection between content and cognitive levels that can be rendered by LLM-based AI systems for educational purposes. Based on the widely recognized [PERSON]'s taxonomy [21], there are six major cognitive levels in the learning process. These levels encompass remembering, understanding, applying, analyzing, evaluating, and creating, progressing from lower-order to higher-order thinking. It is critical to use questions targeting various cognitive levels to assess the development of students' understanding of specific knowledge points in effective teaching; however, designing high-quality test questions that meet these requirements is usually challenging and time-consuming. This work aims to investigate methods for enhancing LLMs' capability to generate cognitive-level-aware test questions. We explore this opportunity through system design, instead of training models with substantial domain-specific data, consuming mass computing and memory resources. We propose a framework for enhanced test question generation utilizing a dual-LLM AI engine, called TwinStar. The framework orchestrates two LLMs: a question generation model and a cognitive-level evaluation model. The question generation model generates test questions based on specified cognitive level and knowledge points with the assistance of multiple rounds of prompts. The cognitive-level evaluation model evaluates the generated test question by cognitive level and provides feedbacks interatively to the question generation model to try to realign with cognitive-level requirements. Our implementation of TwinStar, using two fine-tuned, lightweight baseline models (ChatGLM2-6B) and prompting technology, generates test questions targeting specific knowledge points in university-level calculus at designated cognitive levels without additional question bank or expert evaluation.