Automated Item Generation (AIG): Revolutionizing test item creation 


Automated Item Generation (AIG): Revolutionizing test item creation 

Brooke Dresden, PhD Manager, Statistical Reporting


In the realm of test development, efficiency, accuracy, and security are paramount. To meet these demands, the innovative concept of Automated Item Generation (AIG) has emerged, transforming the traditional item writing process. With the aid of technology, AIG utilizes models or “templates” to create multiple variations of test items simultaneously, streamlining the entire test development process.

So, how does AIG work? Let’s delve into the process:

Defining the Content Domain: Subject matter experts play a crucial role in establishing the content domain for the assessment. They create a model structure or framework that allows for the generation of various test content iterations.

Crafting the Template: Building upon the established framework, authors develop a template that serves as the foundation for item generation. This template incorporates placeholders for variables, such as symptoms in a medical diagnosis scenario, that can be manipulated to generate different iterations of correct answers.

Logic Implementation: The next step involves establishing the logic within the template, determining how different variables interact to form correct conclusions. This ensures that the generated items remain valid and reliable.

Automated Generation: With the logic in place, a computer algorithm takes over the reins, producing a multitude of test items based on the template. Each item shares a common scenario but differs in key attributes, resulting in diverse and comprehensive assessment content.

Benefits of AIG:

Efficiency: AIG allows for the rapid creation of a large number of test items. Considering that crafting a single high-stakes operational item could cost upwards of $2,000, the time and cost savings achieved through AIG on a larger scale are significant.

Security: Expanding the item bank and increasing the number of items measuring each content area reduces item exposure. In case of any item compromise, viable replacement options that assess the same content area but are not identical are readily available.

Now, you might be wondering about Generative AI and its role in AIG. Generative AI, represented by tools like ChatGPT, is an emerging technology that adds another layer of possibility to AIG (read our previous blog on this topic). While traditional AIG relies on pre-defined templates, incorporating Generative AI enables more dynamic and adaptive content generation. This means that AI models can produce test content efficiently and on a large scale, opening up new avenues for the future of AIG.

In conclusion, Automated Item Generation has brought a remarkable revolution to the field of test development. By harnessing the power of technology and innovative AI methods, AIG offers unparalleled efficiency, enhanced security, and boundless potential for the creation of reliable and diverse assessments.


Rudner, L. (2010) Implementing the graduate management admission test computerized adaptive test. In W. J. Van der Linden & C. A. W. Glas, (Eds.), Elements of adaptive testing (pp. 151-165). New York: Springer.

Gierl, M. J., Lai, H., & Turner, S. R. (2012). Using automatic item generation to create multiple-choice test items. Medical Education, 46, 757-765.