Search

Past research on knowledge has differentiated between dimensions (e.g., amount, accuracy, specificity, coherence) of knowledge. This paper introduces a novel dimension of knowledge, the Motivational Utility of Knowledge (MUK), that is based on fundamental human needs (e.g., physical safety, affiliation, actualization, reproduction). Adults in the United States (N = 190) were recruited from an online survey platform and paid for participation. Participants read a set of four texts arguing different views of houselessness and were administered a comprehension test after each text. Participants were asked about their conceptions of houselessness before and after reading. Finally, they were given the MUK scale, a demographics questionnaire, including questions about their personal experience with houselessness, and were administered a general prior knowledge test and a vocabulary knowledge test. We examined MUK, the factor structure of the scale and the relationship between MUK and other measures of knowledge. The analyses showed that the subscales of MUK loaded onto a single factor--an overall value of houselessness knowledge. In addition, we found that MUK was correlated with conceptions of houselessness and comprehension of texts on houselessness, indicating that the scale was valid. Overall, the findings demonstrate that MUK is an important dimension of knowledge to consider in learning tasks.

Artificial intelligence (AI)-driven generative chatbots can produce large quantities of text instantly across a range of domains, using authoritative tones that create the perception of expertise. This critical synthesis compares artificial expertise and human expertise and examines ways in which generative chatbots can support cognition using an expert thinking framework. Findings indicate that generative chatbots may support experts' cognition as a collaborator or to offload lower level tasks. Moreover, generative chatbots are a promising training tool in developing future experts in part because they can provide learning models and practice opportunities. The use of generative chatbots to offload lower level tasks, however, may harm expert development by disrupting knowledge communities. Finally, a lack of domain knowledge in nonexpert users may limit the effectiveness of generative chatbots in supporting higher level cognition and agency. Overall, existing research suggests that the potential for generative chatbots to support users' cognition depends on a user's level of expertise. General Audience Summary: Generative chatbots are artificial intelligence (AI) programs designed to have natural conversations with users. Since the release of ChatGPT (GPT stands for Generative Pre-trained Transformer), generative chatbots have become widely available. Generative chatbots are especially powerful because they are built on computer neural networks and trained on vast amounts of data. In addition, the text they produce can closely resemble expert knowledge and writing on nearly any topic. Consequently, individuals across industries and governments are interested in the potential for generative chatbots to support cognition in experts and nonexperts. This article reviews the history of chatbots, compares human expertise and artificial expertise, and then describes how individuals attain expertise through observing models, completing scaffolded tasks, and engaging in deliberate practice. Afterward, the article discusses how generative chatbots have been used—and could be used in the future—to support cognition (i.e., thoughts and mental processes) for users with varied amounts of domain knowledge (i.e., experts, novices, and laypersons). Research on potential approaches to leveraging generative chatbots to support cognition by these users is primarily drawn from three applied domains: education, medicine, and law. Research with the current generation of generative chatbots like ChatGPT is new and rapidly progressing, but research thus far suggests that (a) the roles that generative chatbots take on to support thinking vary depending on how much knowledge a user has on a particular topic and (b) generative chatbots show promise in supporting experts' cognition and the training of novices who might be future experts in their fields, but (c) laypersons' lack of prior knowledge currently limits generative chatbots' ability to support their thinking and their agency in engaging with unfamiliar domains more broadly. [ABSTRACT FROM AUTHOR]

Students in the 3rd and 4th grade often encounter what has been called a reading "slump" when their class curriculums increasingly ask them to comprehend and learn from texts. Students are more likely to struggle if they have not been offered sufficient opportunities to build world and domain knowledge and engage in challenging comprehension tasks while developing their reading skills. Thus, it is essential to give young readers opportunities to build their world and domain knowledge and to teach them comprehension strategies such as asking questions, paraphrasing, and self‐explaining. This paper introduces iSTART‐Early, an intelligent tutoring system designed to provide instruction and practice opportunities for students to learn comprehension strategies and build knowledge about diverse topics. The theoretical foundations, history and efficacy, and design of iSTART‐Early are discussed. [ABSTRACT FROM AUTHOR]

The Interactive Strategy Training for Active Reading and Thinking (iSTART) game-based intelligent tutoring system (ITS) was developed with a foundation of comprehension theory and principles of learning science to improve students' comprehension of complex scientific texts. iSTART has been shown to improve reading comprehension for learners from middle school through adulthood, particularly lower knowledge readers, through strategy instruction and game-based practice. This paper describes iSTART, the theoretical foundations that have guided iSTART development, and evidence for the feasibility of game-based practice to improve learning outcomes. This paper also introduces a novel method of assessing students' reading comprehension through game-based literacy assessments that have been incorporated in iSTART. The development of these stealth assessments was guided by recent work emphasizing the need for rapid, dynamic, and low stakes assessments that evaluate students' reading skills in the context of brief, dynamic games. Stealth assessments can generate estimates of multiple aspects of students' reading comprehension quickly and within a motivating environment. The work described in this paper is a promising method to assess students' literacy in an unobtrusive and authentic way that may lead to improved learning outcomes for students. [ABSTRACT FROM AUTHOR]

Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its roots in two theoretical models of the comprehension process (i.e., the Construction-Integration model and the Landscape model), and the new version leverages state-of-the-art Large Language models, more specifically ChatGPT, to have a better contextualization of the text and a simplified construction of the underlying graph model. Besides showcasing the usage of the model, the study introduces three in-depth psychological validations that argue for the model's adequacy in modeling reading comprehension. In these studies, we demonstrated that AMoC is in line with the theoretical background proposed by the Construction-Integration and Landscape models, and it is better at replicating results from previous human psychological experiments than its predecessor. Thus, AMoC v4.0 can be further used as an educational tool to, for example, help teachers design better learning materials personalized for student profiles. Additionally, we release the code from AMoC v4.0 as open source in a Google Collab Notebook and a GitHub repository. • Introduce the Automated Model of Comprehension version 4.0 that integrates state-of-the-art Large Language models - ChatGPT. • Perform 3 in-depth psychological validation studies to support the model's adequacy in modeling reading comprehension. • AMoC v4.0 aligns with theoretical frameworks, namely Construction-Integration and Landscape models. • The model outperforms its predecessor and replicates results from human psychological experiments. • AMoC v4.0 can serve as an educational tool for designing personalized learning materials. [ABSTRACT FROM AUTHOR]