## 1 Introduction  
The proliferation of information technology (IT) in educational curricula has intensified the demand for efficient and effective assessment methodologies. Multiple-Choice Questions (MCQs) have long been a cornerstone of educational evaluation, particularly crucial in IT education where systematic evaluation of both technical knowledge and problem-solving abilities is essential [1]. While MCQs offer significant advantages in terms of standardization and automated scoring, the manual creation of high-quality questions remains a resource-intensive process, requiring substantial expertise in both subject matter and assessment design [2]. This challenge is particularly acute in IT education, where the rapid evolution of technologies necessitates continuous updates to assessment materials. Traditional question banks quickly become outdated, and the creation of new, relevant questions places an unsustainable burden on educators. Consequently, there is a pressing need for automated systems that can generate pedagogically sound questions aligned with current IT curricula and capable of assessing various cognitive levels. This challenge has prompted the development of automated question generation systems (QGSs) [3]. Recent advances in artificial intelligence have led to significant progress in automated question generation. Knowledge graphs (KGs), knowledge bases (KBs), and large language models (LLMs) have emerged as powerful tools for this purpose [4]. KG-based approaches leverage structured relationships between concepts to generate contextually relevant questions, while LLMs utilize their extensive training on diverse texts to produce linguistically sophisticated questions [5]. Some systems have successfully combined these technologies, demonstrating improved performance in generating basic factual questions across various domains, including limited applications in IT education [6]. Despite these advancements, existing QGSs face significant limitations when applied to IT education. Our comprehensive analysis of current systems reveals that they predominantly generate lower-order questions focused on recall and basic understanding, failing to adequately assess the higher-order thinking skills essential for IT professionals. Recent research emphasizes that effective IT education assessment must progress through various cognitive levels to develop students' critical thinking and problem-solving abilities [7]. This theoretical foundation underscores the importance of generating questions that not only test factual knowledge but also promote higher-order cognitive skills [8]. Current QGSs face three critical challenges that impede their widespread adoption in IT education. Foremost among these is the generation of contextually relevant and accurate distractors. These incorrect options in MCQs play a pivotal role in discriminating between varying levels of student understanding. The intricacy of IT concepts magnifies this challenge, necessitating distractors that are plausible yet distinctly incorrect within specific technological contexts. Poorly designed distractors can lead to misinterpretation of a student's knowledge and skills, ultimately undermining the validity of the assessment [9]. Equally significant is the need to enhance the diversity of generated questions. A comprehensive assessment demands a varied question set that spans different cognitive levels and content areas. Current systems often produce homogeneous questions, thereby limiting their ability to evaluate a broad spectrum of knowledge and skills [10]. This lack of diversity not only results in incomplete assessments but also fails to engage students effectively across different learning styles and abilities. While some researchers have attempted to incorporate educational frameworks like [PERSON]'s taxonomy into question generation [11], these efforts have been limited in scope and effectiveness.