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The speed–accuracy trade-off (SAT) suggests that time constraints reduce response accuracy. Its relevance in observational settings—where response time (RT) may not be constrained but respondent speed may still vary—is unclear. Using 29 data sets containing data from cognitive tasks, we use a flexible method for identification of the SAT (which we test in extensive simulation studies) to probe whether the SAT holds. We find inconsistent relationships between time and accuracy; marginal increases in time use for an individual do not necessarily predict increases in accuracy. Additionally, the speed–accuracy relationship may depend on the underlying difficulty of the interaction. We also consider the analysis of items and individuals; of particular interest is the observation that respondents who exhibit more within-person variation in response speed are typically of lower ability. We further find that RT is typically a weak predictor of response accuracy. Our findings document a range of empirical phenomena that should inform future modeling of RTs collected in observational settings.

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This mini review summarizes the current state of knowledge about automatic item generation in the context of educational assessment and discusses key points in the item generation pipeline. Assessment is critical in all learning systems and digitalized assessments have shown significant growth over the last decade. This leads to an urgent need to generate more items in a fast and efficient manner. Continuous improvements in computational power and advancements in methodological approaches, specifically in the field of natural language processing, provide new opportunities as well as new challenges in automatic generation of items for educational assessment. This mini review asserts the need for more work across a wide variety of areas for the scaled implementation of AIG.