Chapter 6: Formative Assessment interventions or supports. When an algorithm suggests hints, next steps, or resources to a student, we have to check whether the help-giving is unfair because one group systematically does not get useful help which is discriminatory. Fairness goes beyond bias as well. In AI-enabled formative assessment, both the opportunity to learn through feedback loops, as well as the quality of learning in and outside of such loops, should be addressed. Issues of bias and fairness have arisen in traditional assessments, and the field of psychometrics has already developed valuable tools to challenge and address these issues. Assessment as a field may have a head start on tackling bias and fairness for AI in education. And yet the issues expand with AI, so the work is not done. Strong and deliberate attention to bias and fairness is needed as future formative assessments are developed. 6.7. Related Questions As indicated, formative assessment is an area in which AI is expanding along a continuum that can be guided by visions already in place, such as the 2017 NETP. It is an area in which AI is poised to grow, especially with capabilities that power more feedback loops in student learning. As this growth takes place, we suggest ongoing attention to the following questions: ● Is formative assessment bringing benefits to the student learning experience and to the efficacy of classroom instruction? ● Are humans being centered in AI-enabled formative assessment and feedback loops? ● Are we providing empowering professional development to teachers so they can leverage feedback loops and safeguard against concerns? 70 | P a g e
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