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Keith J. Topping; Ed Gehringer; Hassan Khosravi; Srilekha Gudipati; Kaushik Jadhav; Surya Susarla – International Journal of Educational Technology in Higher Education, 2025
This paper surveys research and practice on enhancing peer assessment with artificial intelligence. Its objectives are to give the structure of the theoretical framework underpinning the study, synopsize a scoping review of the literature that illustrates this structure, and provide a case study which further illustrates this structure. The…
Descriptors: Peer Evaluation, Artificial Intelligence, Grades (Scholastic), Feedback (Response)
Matthew Landers – Higher Education for the Future, 2025
This article presents a brief overview of the state-of-the-art in large language models (LLMs) like ChatGPT and discusses the difficulties that these technologies create for educators with regard to assessment. Making use of the 'arms race' metaphor, this article argues that there are no simple solutions to the 'AI problem'. Rather, this author…
Descriptors: Ethics, Cheating, Plagiarism, Artificial Intelligence
Fatih Yavuz; Özgür Çelik; Gamze Yavas Çelik – British Journal of Educational Technology, 2025
This study investigates the validity and reliability of generative large language models (LLMs), specifically ChatGPT and Google's Bard, in grading student essays in higher education based on an analytical grading rubric. A total of 15 experienced English as a foreign language (EFL) instructors and two LLMs were asked to evaluate three student…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computational Linguistics
Angxuan Chen; Yuyue Zhang; Jiyou Jia; Min Liang; Yingying Cha; Cher Ping Lim – Journal of Computer Assisted Learning, 2025
Background: Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments. Objectives:…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Assisted Testing, Language Tests
Kangkang Li; Chengyang Qian; Xianmin Yang – Education and Information Technologies, 2025
In learnersourcing, automatic evaluation of student-generated content (SGC) is significant as it streamlines the evaluation process, provides timely feedback, and enhances the objectivity of grading, ultimately supporting more effective and efficient learning outcomes. However, the methods of aggregating students' evaluations of SGC face the…
Descriptors: Student Developed Materials, Educational Quality, Automation, Artificial Intelligence
Jan Gunis; L'ubomir Snajder; L'ubomir Antoni; Peter Elias; Ondrej Kridlo; Stanislav Krajci – IEEE Transactions on Education, 2025
Contribution: We present a framework for teachers to investigate the relationships between attributes of students' solutions in the process of problem solving or computational thinking. We provide visualization and evaluation techniques to find hidden patterns in the students' solutions which allow teachers to predict the specific behavior of…
Descriptors: Artificial Intelligence, Educational Games, Game Based Learning, Problem Solving
Reese Butterfuss; Harold Doran – Educational Measurement: Issues and Practice, 2025
Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…
Descriptors: Alignment (Education), Academic Standards, Content Analysis, Concept Mapping
Jinglei Yu; Shengquan Yu; Ling Chen – British Journal of Educational Technology, 2025
Video-based teacher online learning enables teachers to engage in reflective practice by watching others' classroom videos, providing peer feedback (PF) and reviewing others' work. However, the quality and reliability of PF often suffer due to variations in teaching proficiency among providers, which limits its usefulness for reviewers. To improve…
Descriptors: Artificial Intelligence, Peer Evaluation, Feedback (Response), Reflection
Yifeng Hu – Communication Teacher, 2025
This assignment is integrated into the generative AI unit of the Emerging Communication Technologies course. It includes step-by-step designs and reflective examples from students, highlighting the evolution of their perceptions of generative AI. The assignment uniquely focuses on understanding and raising awareness of stereotypes present in…
Descriptors: Artificial Intelligence, Stereotypes, Communications, Student Attitudes
Lori L. Montalbano; Sharon Stoerger – Assessment Update, 2025
The post-pandemic expectations of today's students require greater innovation in teaching and learning. Rapidly changing technologies and software applications will drastically change how higher education is structured and disseminated. In this article, the authors examine the use of micro-credentialing, the potential and challenges of Artificial…
Descriptors: Artificial Intelligence, Teaching Methods, Evaluation Methods, Educational Trends
Barbara Bordalejo; Davide Pafumi; Frank Onuh; A. K. M. Iftekhar Khalid; Morgan Slayde Pearce; Daniel Paul O'Donnell – International Journal of Educational Technology in Higher Education, 2025
This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing…
Descriptors: Artificial Intelligence, Writing (Composition), Quality Control, Writing Evaluation
Rahyuni Melisa; Ashadi Ashadi; Anita Triastuti; Sari Hidayati; Achmad Salido; Priska Efriani Luansi Ero; Cut Marlini; Zefrin Zefrin; Zaki Al Fuad – Educational Process: International Journal, 2025
Background: Artificial Intelligence has become an invaluable tool in academia, offering instant feedback, personalized learning experiences, and support for various academic tasks. This study investigates the impact of ChatGPT, an AI-driven language model, on developing critical thinking, evaluation, and independent judgment skills among higher…
Descriptors: Critical Thinking, Artificial Intelligence, Influence of Technology, Higher Education
Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
Leen Adel Gammoh – Education and Information Technologies, 2025
This qualitative study examines the risks educators in Jordan face with the integration of ChatGPT, an emerging AI technology, into academic settings. While considerable attention has been given to risks affecting university students, there remains a gap in understanding the specific challenges encountered by educators themselves. Through…
Descriptors: Foreign Countries, Artificial Intelligence, Educational Technology, Technology Integration
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing