Chapter 6: Formative Assessment Third, we advocate for teachers and students to be strongly involved in designing feedback loops as developers produce AIenhanced formative assessments so they can directly voice what would make assessments less onerous and more convenient and valuable to them. Earlier in the Teaching section, we emphasized how important it is to involve teachers in designing, selecting, and evaluating AI-enhanced technologies. Students need to be centered, too. They are experiencing AI in their everyday lives, and they have strong opinions on what is valuable and safe. There are local and cultural variations in how people provide and receive feedback, so adjusting feedback to align with community norms is important. 6.6. Key Recommendation: Harness Assessment Expertise to Reduce Bias Bias and fairness are important issues in assessment design and administration, and they hold relevance for the area of AIenabled assessment. In traditional assessment, a test item might be biased if unnecessary details are included that differentially advantage some students (e.g., a story-based item that references a sport that only boys play regularly may be less helpful to girls). As discussed earlier, with AI, we now must worry about algorithmic discrimination which can arise due to the manner in which AI algorithms are developed and improved from large datasets of parameters and values that may not represent all cohorts of learners. Algorithmic discrimination is not just about the measurement side of formative assessment; it is also about the feedback loop and the instructional interventions and supports that may be undertaken in response to data collected by formative assessments. There is a question both about access to such interventions and the quality or appropriateness of such 69 | P a g e
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