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Charting the Future of Assessments AI SJT GBA LLM AIG

Author(s):
Kyllonen, Patrick C.; Sevak, Amit; Ober, Teresa M.; Choi, Ikkyu; Sparks, Jesse R.; Fishtein, Daniel
Publication Year:
2024
Source:
Report
Document Type:
Report
Page Count:
72
Subject/Key Words:
Assessment Design, Assessment Development, Assessment Practices, Assessment Research, Test Security, Score Bias, Artificial Intelligence, Evidence-Based Assessment, Workforce Assessment, High Stakes Tests, Formative Assessment, Low-Stakes Assessment, Personalization, Test Validity, K-12 Assessments, Situational Judgment Tests (SJT), Game-Based Assessment (GBA), Multimodal Assessment, Large Language Models (LLM), Test Assembly, Automatic Item Generation (AIG), Intelligent Tutoring, Tutoring

Abstract

In this paper, we argue and provide evidence for our belief that the future of assessment contains challenges but is promising. The challenges include risks associated with security and exposure of personal data, test score bias, and inappropriate test uses, all of which may be exacerbated by the growing infiltration of artificial intelligence (AI) into our lives. The promise is increasing opportunities for testing to help individuals achieve their education and career goals and contribute to well-being and overall quality of life. To help achieve this promise we focus on the evidence-based science of measurement in education and workplace learning, a theme throughout this paper.

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