Editorial: Generative AI in education
In the field of education, there is a growing interest in the use of Generative Artificial Intelligence (Generative AI) to reshape the educational landscape. This Research Topic investigates the transformative potential of Generative AI in various aspects of education. The papers in this edited volume shed light on the latest discoveries, new insights, novel developments, and future challenges in this rapidly advancing field.By leveraging machine learning models, these intelligent systems extract useful insights from vast amounts of data, making them capable of delivering highly individualized content. They can analyze a learner's proficiency level, learning style, and pace, and then tailor the study material accordingly. Generative AI can adapt its content generation strategies to meet distinct preferences and learners’ needs. This can increase student engagement and comprehension, highlighting its potential to transform traditional teaching methodologies.This Research Topic also explores the use of Generative AI as part of AI tutors, capable of tailoring instructions and feedback dynamically based on each learner's progress. Acting as an ever-present mentor, Generative AI can offer learning aids beyond class hours, facilitating continuous learning and immediate doubt clarification. This can be crucial for learners encountering obstacles outside the typical school hours or during self-study periods. Anyway, to use Generative AI as a tutor, further research is needed to examine not only the accuracy of its answers but also their emotional content, as emotions play a crucial role in the learning process.This Research Topic includes 11 papers (Original Research: 6; Perspective: 2; Opinion: 2, and Mini-Review:1). These papers explore areas such as: (a) using Large Language Models (LLMs) to generate feedback, (b) the use and perceived usefulness of a Generative AI chatbot for schoolwork among adolescents, (c) the potential of Generative AI in supporting critical thinking and enhancing human interactions, (d) using ChatGPT to support pre-service mathematics teachers in constructing mathematical proofs, (e) opportunities and challenges of LLMs to model the “whole learner,” (h) exploring Generative AI for personalized educational assessment, (g) the use of AI-mentors in career exploration, (i) the responsible integration of AI in education, (j) the use of LLMs to automatically generate interactive listening tasks, (k) the potential of AI-enhanced robots to generate incorrect information and deceive students, and (l) a mini-review on Generative AI for supporting students' cognitive and emotional needs. The main contributions of these articles are described below.Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow by Fatahi, Vassileva, and Roy (2024). This paper presents a study aimed to compare the emotional content in human and AI answers. Specifically, it examines the emotional aspects in answers from ChatGPT and humans to 2000 questions sourced from Stack Overflow, finding that ChatGPT's answers tend to be more positive, while human responses often express anger and disgust. Additionally, human emotions exhibit a broader spectrum than ChatGPT. The authors suggest that ChatGPT shows promise as a virtual tutor for students by answering queries and fostering collaboration. However, further research is needed on the emotional aspects of responses.Adolescents’ use and perceived usefulness of generative AI for schoolwork: exploring their relationships with executive functioning and academic achievement by Klarin et al. (2024). The article explores adolescents’ frequency of use and perceived usefulness of generative AI chatbots for schoolwork, focusing on their relationship with executive functioning (EF) and academic achievement. Two studies were conducted with adolescents. Findings indicate that older students use Generative AI tools as more frequently. Also, students facing more EF challenges perceive Generative AI tools as more useful for completing assignments. However, no significant link was found between the use of Generative AI and academic achievement. Future work involves exploring additional Generative AI issues such as potential gender differences, implications for academic equity and the impact on adolescent cognitive development.Using Generative AI in education: the case for critical thinking by Lee and Low (2024). This opinion article makes the case for focusing the use of Generative AI in enhancing students’ critical thinking and human interactions. The authors describe two case studies: (a) teaching communication skills and (b) teaching data structures and algorithms with AI chatbots. The two cases illustrate the potential use of Generative AI to enhance teaching and learning. The authors discuss the benefits of AI-based personalized feedback in improving student engagement and fostering strategic and critical use of AI tools. The article encourages the ethical and responsible use of generative AI in education with potential implications for the workforce.Using large language models to support pre-service teachers' mathematical reasoning—an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry by Herrmann and Dilling (2024). LLMs can be a great source to extract knowledge. It thus appears natural to expect them to generate the texts of classical mathematical proofs. The authors, Marc Herrmann and Frederik Dilling, explore how pre-service teachers employ them to produce proofs. Using the lens of instrumental genesis, their study shows a variety of usage patterns with limited knowledge about the inner workings of the models. It sketches the road to become a teacher support instrument.Large language models for whole-learner support: opportunities and challenges by Mannekote et al. (2024) examines the transformative potential of LLMs in education through the...