Automated Reading Passage Generation with OpenAI's Large Language Model

The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts at each step of the process. AIG has been used in test development for some time. Still, the use of machine learning algorithms has introduced the potential to improve the efficiency and effectiveness of the process greatly. The approach presented in this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. For each prompt, we generated multiple passages, the final passage was selected according to the Lexile score agreement with the original passage. In the final round, the selected passage went through a simple revision by a human editor to ensure the text was free of any grammatical and factual errors. All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability

Modelling Conditional Dependence Between Response Time and Accuracy in Cognitive Diagnostic Models

With the novel data collection tools and diverse item types, computer-based assessments allow to easily obtain more information about an examinee’s response process such as response time (RT) data. This information has been utilized to increase the measurement precision about the latent ability in the response accuracy models. Van der Linden’s (2007) hierarchical speed-accuracy model has been widely used as a joint modelling framework to harness the information from RT and the response accuracy, simultaneously. The strict assumption of conditional independence between response and RT given latent ability and speed is commonly imposed in the joint modelling framework. Recently multiple studies (e.g., Bolsinova & Maris, 2016; Bolsinova, De Boeck, & Tijmstra, 2017a; Meng, Tao, & Chang, 2015) have found violations of the conditional independence assumption and proposed models to accommodate this violation by modelling conditional dependence of responses and RTs within a framework of Item Response Theory (IRT). Despite the widespread usage of Cognitive Diagnostic Models as formative assessment tools, the conditional joint modelling of responses and RTs has not yet been explored in this framework. Therefore, this research proposes a conditional joint response and RT model in CDM with an extended reparametrized higher-order deterministic input, noisy ‘and’ gate (DINA) model for the response accuracy. The conditional dependence is modelled by incorporating item-specific effects of residual RT (Bolsinova et al., 2017a) on the slope and intercept of the accuracy model. The effects of ignoring the conditional dependence on parameter recovery is explored with a simulation study, and empirical data analysis is conducted to demonstrate the application of the proposed model. Overall, modelling the conditional dependence, when applicable, has increased the correct attribute classification rates and resulted in more accurate item response parameter estimates

Automated reading passage generation with OpenAI's large language model

The widespread usage of computer-based assessments and individualized learning platforms has increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts. While AIG has been used in test development, recent advances in machine learning algorithms offer the potential to enhance its efficiency further. This paper presents an innovative approach utilizing OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. Multiple passages were generated for each prompt, and the final passage was selected based on Lexile score agreement with the original passage. To ensure accuracy, a human editor conducted a simple revision of the chosen passage, correcting any grammatical and factual errors. To evaluate the effectiveness of the AI-generated passages, human judges assessed their coherence and appropriateness for fourth-grade readers. The results indicated that GPT-3-produced passages closely resembled human-authored passages regarding coherence, appropriateness, and readability for the target audience. By combining GPT-3's capabilities with carefully designed prompts and human editing, this study demonstrates an efficient and effective method for generating reading passages. The findings highlight the potential of incorporating large language models into automated item generation, contributing to improved scalability and quality in educational assessment development