In the past ten years, applications of generative artificial intelligence (GAI) have found rapidly growing use in medicine, science, and daily life. Large language models (LLMs) opened up new avenues in particular for education. LLMs have been used to create interactive educational content for students, stimulate their curiosity, generate code explanations, and develop assessment questions. Additionally, LLMs been applied for language practice, anxiety alleviation, and feedback provision. In higher education, LLMs have shown potential for assisting in medical exam preparation and clinical decision-making. In school education, LLMs can help teachers with automated evaluation of student responses and respective adaptive feedback. More recently LLM-based applications such as chatGPT have been used to generate teaching materials or assessment tasks across different subjects. The fields’ understanding of the effects of the use of LLM-based applications in classroom teaching, however, is still in its infancy.GAI tools may help solving a range of tasks in education, in particular with respect to teachers’ and students’ and teachers’ efforts to generate content. However, it is critical that teachers and students do not overly rely on GAI generated solutions but instead critically assess each solution. Students should furthermore not use GAI tools to avoid investing relevant mental effort to create mental models or, more broadly, build-up competencies.This Research Topic aims to address issues around the use of GAI tools to advance students’ cognition or, more broadly, competencies, and how to enable both teachers and students to critical reflect upon the use of GAI tools instead of overly relying on them. The Research Topic will create a collection of empirical research that provides insights into the interplay of teachers’ and students’ mindsets when using GAI tools, the way and the tasks the GAI tools are used for, and the effects on learning and competence development. This Research Topic will focus on research on the meaningful use of large language model-based GAI tools such as chatGPT for learning and cognition in order to foster critical reflection in the field on how GAI tools can be used to support teachers in formative assessment, diagnosing students’ difficulties, implement novel cognitive activities and targeted interventions, and provide individualized attention to students. The Research Topic seeks evidence on the potential of GAI tools to support students in cognitive, metacognitive, and motivational activities due to automated feedback, scaffolding support, collaborative generative processes and related issues, prompting a critical reflection on this use of GAI tools.Due to their complexity, GAI tools sometimes lack controllability and correctness in their output. Therefore, such GAI tools also demand certain affordances from learners to properly evaluate these tools and use them effectively. For this reason, this Research Topic also welcomes studies that investigate students’ affordances to learn effectively with GAI tools and conditions for a successful classroom implementation.
In the past ten years, applications of generative artificial intelligence (GAI) have found rapidly growing use in medicine, science, and daily life. Large language models (LLMs) opened up new avenues in particular for education. LLMs have been used to create interactive educational content for students, stimulate their curiosity, generate code explanations, and develop assessment questions. Additionally, LLMs been applied for language practice, anxiety alleviation, and feedback provision. In higher education, LLMs have shown potential for assisting in medical exam preparation and clinical decision-making. In school education, LLMs can help teachers with automated evaluation of student responses and respective adaptive feedback. More recently LLM-based applications such as chatGPT have been used to generate teaching materials or assessment tasks across different subjects. The fields’ understanding of the effects of the use of LLM-based applications in classroom teaching, however, is still in its infancy.GAI tools may help solving a range of tasks in education, in particular with respect to teachers’ and students’ and teachers’ efforts to generate content. However, it is critical that teachers and students do not overly rely on GAI generated solutions but instead critically assess each solution. Students should furthermore not use GAI tools to avoid investing relevant mental effort to create mental models or, more broadly, build-up competencies.This Research Topic aims to address issues around the use of GAI tools to advance students’ cognition or, more broadly, competencies, and how to enable both teachers and students to critical reflect upon the use of GAI tools instead of overly relying on them. The Research Topic will create a collection of empirical research that provides insights into the interplay of teachers’ and students’ mindsets when using GAI tools, the way and the tasks the GAI tools are used for, and the effects on learning and competence development. This Research Topic will focus on research on the meaningful use of large language model-based GAI tools such as chatGPT for learning and cognition in order to foster critical reflection in the field on how GAI tools can be used to support teachers in formative assessment, diagnosing students’ difficulties, implement novel cognitive activities and targeted interventions, and provide individualized attention to students. The Research Topic seeks evidence on the potential of GAI tools to support students in cognitive, metacognitive, and motivational activities due to automated feedback, scaffolding support, collaborative generative processes and related issues, prompting a critical reflection on this use of GAI tools.Due to their complexity, GAI tools sometimes lack controllability and correctness in their output. Therefore, such GAI tools also demand certain affordances from learners to properly evaluate these tools and use them effectively. For this reason, this Research Topic also welcomes studies that investigate students’ affordances to learn effectively with GAI tools and conditions for a successful classroom implementation.