In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…

Subject-verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject-verb agreement from a sentence preamble ("The key to the cabinets") and eliciting verb errors (… "*were shiny"). Through reanalysis of previous data (50 experiments; 102,369…

Open-ended questions in mathematics are commonly used by teachers to monitor and assess students' deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes…

Most current models of sentence comprehension assume that the human parsing mechanism (HPM) algorithmically computes detailed syntactic representations as basis for extracting sentence meaning. These models share the assumption that the representations computed by the HPM accurately reflect the linguistic input. This assumption has been challenged…

Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to…

A theory of sentence production is presented that accounts for facts about speech errors, including (1) the kinds of errors that occur; (2) the constraints on their form; and (3) the conditions that precipitate them. Two simulation models are introduced to illustrate how the theory applies to phonological encoding processes. (Author/LMO)