The field of medical education has consistently been at the cutting edge of integrating innovative approaches and strategies to improve student learning and evaluation due to its dynamic character. The multiple-choice question (MCQ) is a significant evaluation tool that has been well recognized for its efficiency, objectivity, and capacity to encompass a wide range of knowledge in a brief manner [1]. MCQs were initially implemented with the intention of optimizing the testing procedure and establishing uniformity. However, they have since progressed to encompass broader educational goals, covering not only memorization but also the development of critical thinking skills and the ability to make clinical decisions [2].

Nevertheless, due to the growing emphasis on the practicality of clinical knowledge and the significance of problem-solving abilities in the medical education framework, there has been a shift in the approach towards including case-based MCQs. These kinds of questions offer students the opportunity to engage with clinical scenarios that they may experience during their professional practice, therefore facilitating the integration of theoretical knowledge with practical implementation [3]. The aforementioned transformation is not solely a result of pedagogical inclination, but rather stems from the necessity for physicians to possess proficiency in navigating practical clinical predicaments and in rendering well-informed judgments.

The rapid expansion of technology, particularly the emergence of Artificial Intelligence (AI) and Natural Language Processing (NLP), has significantly enhanced this field, providing resources that can assist in the automated creation of case-based MCQs. The previously mentioned developments exhibit potential in enhancing the process of question production, guaranteeing a broader scope, and delivering personalized learning experiences [4].

1.1. Historical Background

MCQs were first developed during the early 20th century, representing a significant departure from the conventional essay-based exams that were commonly used during that era ^[5][9]. MCQs are appealing, standardized assessments which have the ability to bring uniformity to the evaluation process, hence guaranteeing a consistent gauge of students’ knowledge and skills within extensive groups. According to Stough (1993), this particular structure enabled the use of objective grading methods and streamlined the evaluation process for a diverse range of subjects, all within a constrained timeframe [6].

1.2. Structure of a Case-Based MCQ

Irrespective of their role, case-based MCQs follow a standard format ^[7][8][10,11] as follows:

• The stem (sometimes the portion referred to as the “question”). This might consist of a simple question, but might also be more complex, and include a scenario and media. The key element in creating a robust multiple-choice question is to ensure that the stem is well-defined and focused. The stem of the query must contain the primary concept.
• Alternatives (sometime referred to as “options”). These include all the items, from which the user must select one.
• Answer (sometimes referred to as the “correct answer” or the “key”). This is one of the alternatives, and is the actual required answer to the question. The crucial characteristic is that the selected option deemed as accurate must be absolutely indisputable, without any doubt or debate. It is preferable to have a manuscript citation or reference on hand for verification purposes. When providing a correct answer, beware of using ambiguous phrases like “frequently”, “often”, “rarely”, or “sometimes”. These hints indicate that an answer is correct and demonstrate test-taking intelligence rather than subject content knowledge.
• Distractors. There are all the alternatives that are not the answer. From a cognitive perspective, it is acceptable to have two distractors. However, in health sciences testing, it is more common to have three or four distractors. Writing plausible distractors can be the most difficult aspect of developing a well-formulated examination.

1.3. Transition to Clinical Significance

Initial MCQs primarily emphasized the retrieval of factual information. However, educators swiftly acknowledged the necessity of assessing more advanced cognitive abilities, particularly within the intricate and diverse field of medicine. During the 1980s, there was an increasing focus on the alignment of MCQs with clinical scenarios, thereby replicating authentic medical situations that students may face during medical practice ^[9][12]. The transition discussed in this context was motivated by a pedagogical shift towards problem-based and team-based learning. This approach placed greater importance on the application of acquired knowledge in clinical settings, as opposed to solely focusing on knowledge acquisition ^[10][13].

1.4. Emergence of Case-Based MCQs

The advent and fast acceptance of case-based MCQs in the late 20th and early 21st centuries might be seen as a continuation of the focus on clinical relevance. The questions were based on actual or simulated patient scenarios, and examinees were expected to apply their knowledge, analyze clinical data, and make well-informed decisions. These activities closely resembled the responsibilities of a practicing physician [4].

1.5. Integration of Technology

The emergence of the digital era brought about a significant transformation in the development and administration of MCQs. The prevalence of computer-based testing has led to the emergence of increasingly interactive and dynamic question styles. Concurrently, the incorporation of Artificial Intelligence and data analytics caused an impact on the construction of MCQs, presenting the possibility of customization and adaptive testing [2]. It can be inferred that the aforementioned points collectively support the notion that MCQs in medical education have evolved in parallel with the broader educational and technological advancements within the profession, progressing from their modest origins to their present complex forms. The enduring significance of their contribution to the development of proficient and analytically minded medical practitioners is unquestioned ^[11][12][14,15].

2. The Significance of Case-Based MCQs

The primary objective of medical education is not only to provide students with fundamental knowledge, but also to provide them with the abilities required to effectively use this knowledge in practical clinical situations. Case-based MCQs are an essential tool in this pursuit, providing numerous unique benefits.

2.1. The Integration of Theory and Practice

Case-based MCQs provide a connection between theoretical medical principles and practical clinical scenarios. The questions presented to students involve real or theoretical patient scenarios, which necessitate the navigation of complicated clinical reasoning. This approach aims to foster a more profound comprehension and practical application of academic information ^[11][14].

2.2. Evaluating Higher-Order Cognitive Abilities

Traditional MCQs frequently assess the ability to recall factual information. On the other hand, case-based MCQs require the utilization of advanced cognitive abilities, such as analysis, application, and evaluation. The promotion of critical thinking and decision-making skills, which are fundamental abilities for healthcare professionals, is achieved by involving the students in clinical vignettes ^[13][16].

2.3. Improving Clinical Readiness

The process of clinical decision-making encompasses more than the simple recollection of knowledge; it necessitates the integration of information within the limitations of ambiguity and time sensitivity. According to Chéron et al., case-based MCQs effectively replicate these obstacles, hence enhancing students’ readiness for real-world clinical responsibilities ^[14][5].

2.4. Embracing Contemporary Pedagogical Approaches

The trend towards problem-oriented combined learning in medical education is well-supported by the utilization of case-based MCQs. Zhao et al. posits that student-centered education is promoted by fostering an environment that encourages students to actively engage in the process of learning ^[12][15].

2.5. The Provision of Objective Assessment Metrics

Although case-based MCQs contain a substantial amount of information, they continue to possess the inherent objectivity associated with the MCQ format. Zhao et al. assert that the implementation of this approach guarantees impartial evaluation and provides quantifiable measures that may be utilized for the goals of feedback, enhancing the curriculum, and achieving accreditation ^[12][15].

2.6. Enhancing Proficiency in Differential Diagnosis Abilities

Frequently, case-based MCQs pose scenarios wherein symptoms may correspond to many illnesses, necessitating students to discern and rank potential diagnoses based on their relative importance. Engaging in this activity enhances their proficiency in differential diagnosis, a fundamental component of clinical practice ^[11][14]. Case-based MCQs serve a dual purpose beyond mere evaluation, as they play a crucial role in developing a prospective physician’s clinical expertise by facilitating the integration of theoretical knowledge with practical application. These questions effectively bridge the divide between academic learning and real-world medical practice. The role they play in contemporary medical education is undeniably essential and significant [6].

3. Approaches for Generating Case-Based MCQs

The creation of case-based MCQs, which play a crucial role in evaluating practical medical knowledge, can be accomplished using both conventional human techniques and novel automated methods. Every methodology inherently possesses its unique array of benefits and challenges.

3.1. Generation through Manual Procedures

In the ensuing subsections, a detailed exploration regarding the manual generation procedure will be undertaken. Manual generation delineates a methodology whereby textual or content creation is executed solely by human authors, devoid of any incorporation or interference from technological apparatuses or systems. The manual formulation of case-based MCQs occurs through several stages:

• Selection of Topic: During the initial phase, educators execute a meticulous selection of a medical subject or issue that bears relevance to the curriculum, as indicated by Al-Rukban ^[15][7]. If the institution uses Learning Objectives, then these must also be noted to ensure that the questions are aligned with them.
• Development of Case Scenario: This phase entails the crafting of a patient scenario which could be derived from either authentic experiences or hypothetical situations, aiming to construct a contextual framework. Typical elements of a patient’s medical record integrate their medical history, vital statistics, laboratory results, and other pertinent information ^[11][14].
• Question Framing: The core objective of framing questions is to evaluate understanding, analysis, or application in connection with the presented case study ^[11][15][7,14].
• Distractor generation: Distractor conceptualization involves formulating conceivable incorrect alternatives (distractors) that are coherent and non-deceptive, a notion underscored by Al-Rukban and Kurdi ^[11][15][7,14].
• Validation: The refinement and validation of MCQs are optimized through a peer review, executed by educationalists and clinical experts. This collaborative methodology ascertains the enhancement of question clarity, accuracy, and pertinence ^[11][14].

3.2. Challenges of Manual Generation

There are several challenges and constraints associated with the generation of case-based MCQs. The process of creating case-based MCQs of superior quality using manual means can be a time-consuming endeavor, as noted by Leo et al., (2019) [4]. Also, the presence of prejudice is a possibility in educational settings, as the personal biases held by educators might potentially impact the formulation and presentation of questions. In addition, the diversity of manually constructed MCQs may be limited, as they may not cover the full range of probable clinical circumstances or question styles ^[16][17].

3.3. The Process of Automated Generation

The advent of digital transformation in the field of education has given rise to the utilization of artificial intelligence (AI) and natural language processing (NLP) as highly effective instruments. According to Zhang et al. these systems possess the capability to analyze extensive quantities of text, detect patterns within the data, and provide queries that are contextually appropriate ^[17][18]. The tools and techniques are employed in the process of generating MCQs automatically. Firstly, the database-driven approach involves the utilization of algorithms to extract information from medical ontology or texts in order to generate questions that are grounded in current, evidence-based content [6]. In addition, Natural Language Processing (NLP) tools such as Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) are utilized to analyze medical texts. These tools are capable of extracting essential concepts and relationships from the texts, enabling the generation of preliminary MCQs ^[18][19]. Additionally, according to Torrealba et al., adaptive learning systems, which are powered by artificial intelligence, have the capability to modify the difficulty level of MCQs based on the performance of individual students ^[18][19]. This adaptive approach aims to optimize the learning process.

3.4. The Advantages and Obstacles Associated with Automated Generation

There exist numerous advantages associated with the automated creation of case-based MCQs. Firstly, the ability to generate a substantial quantity of questions quickly is considered an important aspect of efficiency in question creation [4]. In addition, the continuous updates from medical databases guarantee the relevance of the content. Also, diversity can be achieved by incorporating a range of question styles and clinical settings. One potential issue with automatically generated MCQs is the potential lack of depth and clinical relevance in the absence of human scrutiny [6]. Also, the potential degradation of the educator’s role in curriculum design is a significant ethical consideration associated with the over-reliance on artificial intelligence (AI).