Federal law requires all public school students in grades three to eight to take an annual assessment in reading and math at the end of the year and requires students to take an assessment once during high school. The goal of this assessment is to measure the extent to which all students are meeting the state's academic standards. These standards must align with the knowledge and skills in reading and math that students need to succeed in first-year college reading and math courses. Ensuring all students are held to rigorous standards is a key goal of equity in education. Yet many question the value of yearly standardized testing in schools since the opportunity to receive a high-quality education and graduate high school adequately prepared for college-level academics is still wholly inequitable. Students who are Black, Indigenous, and Hispanic graduate high school at lower rates than their white peers, and they require catch-up coursework in college more often. What is more, the costs and time associated with assessments, delayed results, and failure of tests alone to improve students' academic results leave many to wonder if they are worth the effort at best, and at worst, if they harm students and punish teachers and schools. Still, there are ways to design an assessment to reduce the amount of time it takes to administer, ensure that it collects information about students throughout the year, or base the test on performing tasks. This report describes the advancements in testing technology that make such assessments possible, and it concludes with recommended changes in federal testing policy to make the use of these designs effective. This report is part three in a series about the future of testing in America's schools. Part one of the series "Effective and Equitable Assessment Systems. Future of Testing in Education" (ED617053) presents a theory of action that assessments should play in schools. Part two "Artificial Intelligence. Future of Testing in Education" (ED617052) reviews advancements in technology, with a focus on artificial intelligence that can powerfully drive learning in real time.