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AIGRA: Agentic Item Generation, Review, and Analysis for Educational Assessment
Assessment item development requires careful control of construct alignment, response structure, difficulty, language, and review evidence. Large language models can accelerate item drafting, but ad hoc prompting is difficult to audit or reproduce. AIGRA is an R package for structured, agentic item generation, review, and analysis. It supports tabular item-bank templates, Python-backed generation workflows, direct API-key handling, diagram-aware item generation, quality summaries, and HTML reporting. The package is designed for template-based item clone generation, where new items preserve the source item's assessed skill, reasoning pattern, response format, language requirements, and diagram dependency. This article describes the software architecture, workflow, examples, and research impact of AIGRA.
Priority areas:
humanitarian
Language:
English
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JOURNAL OF EDUCATIONAL TECHNOLOGY DEVELOPMENT AND EXCHANGE 2026 Vol. 19 No. 2 P. 141–169
Educational assessments, from low-stakes classroom tests to high-stakes national examinations, require item pools that are valid, fair, and secure. Automated Item Generation (AIG) aims to efficiently produce large pools of calibrated test items. This paper adopts a two-part design: (1) a brief historical mapping situating LLM-based AIG within the broader AIG trajectory; and (2) a ...
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