What’s the difference between Assess.ai and FastTest?

FastTest has been ASC’s flagship platform for the past decade, securely delivering millions of exams, loaded with best practices like computerized adaptive testing and item response theory. FastTest is based off decades of experience in computerized test delivery and item banking, from MICROCAT in the 1980s to FastTest PC/LAN in the 1990s. And now the time has come for FastTest to be replaced with its own nextgen successor: Assess.ai.

With Assess.ai, we started by redesigning everything from the ground up, rather than just giving a facelift to FastTest. This leads to some differences in capability. Moreover, FastTest has seen more than 10 years of continuous development, so there is a massive amount of powerful functionality that has not yet been built into Assess.ai. So we’ve provided this guide to help you understand the advancements in Assess.ai and effectively select the right solution for your organization?

Will FastTest be riding into the proverbial sunset? Yes, but not anytime soon. For current users of FastTest, we’ll be working with you to ensure a smooth transition.

Important differences between FastTest and Assess.ai

Aspect FastTest Assess.ai
Availability Cloud or On-Premise Cloud only
UI/UX 2010 design with right-click menus Modern Angular with completely new UX
Item types 12 50+
Automated item generation No Yes
Test delivery methods Linear, LOFT, CAT Linear (LOFT and CAT in development)
Examinees Not reusable (must upload for each test) Reusable (can take more than one test)
Examinee testcode emails Not customizable Customizable
Accessibility Time Time, zoom, color
Widgets Calculator, protractor Protractor, calculator, scientific calculator
Content management Folders Orthogonal tags
Delivery languages English, Spanish, Arabic English, Arabic, Chinese, French, German, Italian, Russian, Spanish, Tagalog

There are of course many more differences. Want to hear more? Email solutions@assess.com to set up a demo. You might also be interested in this outline.

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nthompson

Nathan Thompson earned his PhD in Psychometrics from the University of Minnesota, with a focus on computerized adaptive testing. His undergraduate degree was from Luther College with a triple major of Mathematics, Psychology, and Latin. He is primarily interested in the use of AI and software automation to augment and replace the work done by psychometricians, which has provided extensive experience in software design and programming. Dr. Thompson has published over 100 journal articles and conference presentations, but his favorite remains https://pareonline.net/getvn.asp?v=16&n=1.
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