When determining to use or not use AIG for item development there are both quantitative and qualitative factors to consider. Together these factors contribute to the return on investment (ROI) of operationalizing AIG in an organization. Quantitative Factors
1.     Cost per item
2.     Item survival rate from field testing
3.     Statistical item quality
4.     Scalability potential with and without using AIG
5.     Product diversification potential

Is AIG artificial intelligence?
AIG is the process of using item models to generate items using computer technology.  It can be considered a form of augmented intelligence.  Augmented intelligence is an area within artificial intelligence that deals with how computer systems emulate and extend human cognitive abilities thereby helping to improve human task performance.  It requires the interaction between a computer system and a human in order for the computer system to produce an output or solution.  Augmented intelligence combines the strength of modern computing using computational analysis and data storage with the human capacity for judgment to solve complex unstructured problems.  Augmented intelligence can therefore be characterized as any process or system that improves the human capacity for solving complex problems by relying on a partnership between a machine and a human.  The AIG methodology at MGHL Partners is an augmented intelligence approach to item development.

AIG is a method for producing evidence needed to validate the knowledge, skills, and/or competencies required to solve problems in a particular domain or content area. Given this definition, a human-in-the-loop AIG methodology is so important.

How do you ensure that the generated items are diverse?
The items produced by AIG are anchored to the content specified in the cognitive model.  Diversity in the generated items can be produced using constraint coding.  Constraint coding is the method by which logical constraints are used to implement the decisions, judgments, and rules described by the subject-matter experts. Diversity can be infused into the generation process by including more distractors in the item model.  Increasing the list of distractors will always increase both the number and the diversity of the generated items.

MGHL Partners pioneered automatic item generation technology that has been implemented in the largest testing organizations for almost 2 decades. We are committed to supporting a successful AIG implementation by providing services to help our clients train team members, implement change management, and organize and manage the expanded item bank.