## 1 Introduction  
The emergence of large language models (LLMs) has generated significant enthusiasm in higher education due to their demonstrated ability to generate and analyze text, thereby potentially transforming academic tasks ranging from content creation to academic assessment. The authors in [1, 2] acknowledge this potential of LLMs to transform higher education. Despite the promising opportunities that LLMs offer to streamline teaching and engage students, their deployment in educational settings, such as exam question generation, poses substantial challenges in terms of alignment, reliability, and fairness [3]. Unlike many general-purpose applications where broad knowledge is an advantage, university courses are built on carefully curated content--such as instructor-prepared notes,concise lecture summaries, and tailored presentations, and are particularly sensitive to even slight discrepancies or biases in AI-generated content. Such misalignment can undermine academic integrity and significantly affect student performance. This specificity demands that any AI-generated exam questions not only be accurate and clear but also align precisely with course objectives, maintain appropriate difficulty levels, and uphold strict standards of fairness. A number of studies have reviewed the evolutionary potential of AI in education [1; 4] and have examined automated question generation techniques and evaluations [5; 6; 7]. Existing studies tend to focus on broad trends or isolated tasks without critically examining the specific challenges that arise when integrating LLMs into the domain of academic assessment. In particular, prior research has rarely addressed the dual need for (i) ensuring that generated questions are rigorously aligned with course objectives and pedagogical standards, and (ii) providing interpretable justifications for the AI's decisions. This lack of a holistic framework means that, even when automated question generation appears technically sound, educators are left uncertain about the quality, fairness, and contextual relevance of the output. In response to these gaps, our study proposes a comprehensive methodological framework specifically tailored for higher education. Our approach centers on three interrelated tasks:  
1. Alignment Evaluation --assessing how accurately an existing exam question reflects a course's learning objectives. 2. Question Improvement--refining suboptimal exam questions to enhance clarity, specificity, and appropriate difficulty. 3. New Question Generation--creating entirely new exam questions that conform to intended learning outcomes. These tasks are applied across multiple university courses that span both theory-oriented and application-oriented domains and cover various cognitive levels as delineated in [PERSON]'s Taxonomy [8]. On top of this, our framework integrates a meta-evaluation layer that is supervised by human experts. In this setup, a specialized LLM that is augmented by rigorous human oversight assigns ground-truth scores and evaluates the quality of explanations underlying each AI agent's decision. This dual-level evaluation not only enables robust comparisons among different AI architectures (VectorRAG, VectorGraphRAG, and a Fine-Tuned LLM), but also produces detailed explanations that help identify sources of potential biases and/or misalignment. By offering detailed, empirically validated insights, our study contributes a set of best practices and actionable guidelines for the responsible and context-aware integration of AI in academic assessments. Our goal is not to compete solely on the generation of quality metrics but rather to ensure that LLM-based agents are finely tuned to the specific demands of higher education, safeguarding academic integrity and enhancing assessment effectiveness. Ultimately, our work is designed to serve as a meticulous guide for educators and technology developers.