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It is particularly important to identify and address issues of fairness and equity in educational contexts as academic performance can have large impacts on the types of opportunities that are made available to students. While it is always the hope that educators approach student assessment with these issues in mind, there are a number of factors that likely impact how a teacher approaches the scoring of student work. Particularly in cases where the assessment of student work requires subjective judgment, as in the case of open-ended answers and essays, contextual information such as how the student has performed in the past, general perceptions of the student, and even other external factors such as fatigue may all influence how a teacher approaches assessment. While such factors exist, however, it is not always clear how these may introduce bias, nor is it clear whether such bias poses measurable risks to fairness and equity. In this paper, we examine these factors in the context of the assessment of student answers to open response questions from middle school mathematics learners. We observe how several factors such as context and fatigue correlate with teacher-assigned grades and discuss how learning systems may support fair assessment. 

In this work-in-progress poster, we will present how a team including game designers, learning scientists, and assessment scientists collaborated on an online adventure game, Aqualab, with the goal of creating a comprehensive long-format game that can be used across multiple classroom sessions to support development science inquiry practices as well as assess different learning pathways within the game. In this work-in-progress poster, we discuss how the team approached design and development of the game to ensure validity of the game, and how we are planning to further investigate validity evidence of the game as a whole. 

This paper describes an experience report centered on high school mathematics teachers’ use of ALICE, a Generative AI (GenAI) module of the Edfinity homework system. Given natural language prompts (from teachers), ALICE generates the programming code (in WeBWorK format) for the corresponding interactive, isomorphic, auto-gradable problem along with hints and a solution. Writing such code would normally require programming skills. Working with teachers in high schools across a mid-western US state, this paper presents teachers’ experiences using ALICE, on prompt engineering, and the factors that influence these experiences. The implementation study also examines the impact of this experience on teachers’ classroom practice and their views about AI. Findings suggest that teachers’ experiences were largely very positive, however these experiences are shaped by several factors including their context, their attitudes toward technology and AI use, and the perceived usefulness of the tool. These factors hold different levels of importance for individual teachers. The promising results contribute to the burgeoning field of GenAI in education and understanding teacher-AI teaming. 

It is particularly important to identify and address issues of fairness and equity in educational contexts as academic performance can have large impacts on the types of opportunities that are made available to students. While it is always the hope that educators approach student assessment with these issues in mind, there are a number of factors that likely impact how a teacher approaches the scoring of student work. Particularly in cases where the assessment of student work requires subjective judgment, as in the case of open-ended answers and essays, contextual information such as how the student has performed in the past, general perceptions of the student, and even other external factors such as fatigue may all influence how a teacher approaches assessment. While such factors exist, however, it is not always clear how these may introduce bias, nor is it clear whether such bias poses measurable risks to fairness and equity. In this paper, we examine these factors in the context of the assessment of student answers to open response questions from middle school mathematics learners. We observe how several factors such as context and fatigue correlate with teacher-assigned grades and discuss how learning systems may support fair assessment.