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Konstantinos Gavriil; Ioannis Giannikos – Education Policy Analysis Archives, 2025
This paper presents a model for automatically selecting and allocating secondary education teachers to schools while considering various factors such as the diversity of sections and lessons, school distances, teacher specializations, teaching workloads, and other constraints. This poses a complex challenge that educational authorities in…
Descriptors: Foreign Countries, Secondary School Teachers, Teacher Placement, Teacher Distribution
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Ngoc My Bui; Jessie S. Barrot – Education and Information Technologies, 2025
With the generative artificial intelligence (AI) tool's remarkable capabilities in understanding and generating meaningful content, intriguing questions have been raised about its potential as an automated essay scoring (AES) system. One such tool is ChatGPT, which is capable of scoring any written work based on predefined criteria. However,…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Automation
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Rebecca L. Pharmer; Christopher D. Wickens; Benjamin A. Clegg – Cognitive Research: Principles and Implications, 2025
In two experiments, we examine how features of an imperfect automated decision aid influence compliance with the aid in a simplified, simulated nautical collision avoidance task. Experiment 1 examined the impact of providing transparency in the pre-task instructions regarding which attributes of the task that the aid uses to provide its…
Descriptors: Accountability, Automation, Compliance (Psychology), Task Analysis
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Xiaomei Wang – Education and Information Technologies, 2025
Automated writing evaluation (AWE) provides an instant and cost-effective alternative to human feedback in assessing student writing, and therefore is widely used as a pedagogical supportive tool in writing instruction. However, studies on how students perceive the usage of AWE as a surrogate writing tutor in out-of-class autonomous learning are…
Descriptors: Student Attitudes, Automation, Writing Evaluation, Undergraduate Students
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Andreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2025
Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations…
Descriptors: Automation, Computer Assisted Testing, Multiple Choice Tests, Natural Language Processing
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William J. Fassbender – Learning, Media and Technology, 2025
Recent advancements in generative Artificial Intelligence (GenAI) were accompanied by both hype and fear regarding the ways in which such technologies of automation would replace human labor in various fields, including education. Rather than focusing on the replacement of humans in teaching, this piece uses new materialist thought [Barad, Karen.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Automation, Educational Change
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Efe Bozkir; Christian Kosel; Tina Seidel; Enkelejda Kasneci – International Educational Data Mining Society, 2025
Teachers' visual attention and its distribution across the students in classrooms can constitute important implications for student engagement, achievement, and professional teacher training. Despite that, inferring the information about where and which student teachers focus on is not trivial. Mobile eye tracking can provide vital help to solve…
Descriptors: Eye Movements, Attention, Automation, Human Body
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Wesley Morris; Langdon Holmes; Joon Suh Choi; Scott Crossley – International Journal of Artificial Intelligence in Education, 2025
Recent developments in the field of artificial intelligence allow for improved performance in the automated assessment of extended response items in mathematics, potentially allowing for the scoring of these items cheaply and at scale. This study details the grand prize-winning approach to developing large language models (LLMs) to automatically…
Descriptors: Automation, Computer Assisted Testing, Mathematics Tests, Scoring
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Alex Goslen; Yeo Jin Kim; Jonathan Rowe; James Lester – International Journal of Artificial Intelligence in Education, 2025
The development of large language models offers new possibilities for enhancing adaptive scaffolding of student learning in game-based learning environments. In this work, we present a novel framework for automatic plan generation that utilizes text-based representations of students' actions within a game-based learning environment, Crystal…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Game Based Learning
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Smitha S. Kumar; Michael A. Lones; Manuel Maarek; Hind Zantout – ACM Transactions on Computing Education, 2025
Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…
Descriptors: Automation, Computer Science Education, Programming, Feedback (Response)
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Halim Acosta; Seung Lee; Haesol Bae; Chen Feng; Jonathan Rowe; Krista Glazewski; Cindy Hmelo-Silver; Bradford Mott; James C. Lester – International Journal of Artificial Intelligence in Education, 2025
Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging.…
Descriptors: Game Based Learning, Artificial Intelligence, Technology Uses in Education, Cooperative Learning
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Paraskevi Topali; Carla Haelermans; Inge Molenaar; Eliane Segers – British Educational Research Journal, 2025
The introduction of artificial intelligence (AI) into education holds promise for supporting and augmenting teaching and learning-related activities. Yet, despite its potential, there is limited empirical research on the use of AI in K-12 settings exploring the pedagogical grounding, impact and implications of the technological solutions. The…
Descriptors: Literature Reviews, Artificial Intelligence, Automation, Elementary Secondary Education
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Yufeng Qian; Ragia Hassan – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
Over the past few decades, technology-enhanced learning has positioned instructional design as a crucial catalyst for academic innovation within higher education. The emergence and rapid integration of artificial intelligence (AI) are now reshaping instructional design practices. This review study reveals a rapidly expanding ecosystem of AI tools…
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Higher Education
Sungbok Shin – ProQuest LLC, 2024
Data visualization is a powerful strategy for using graphics to represent data for effective communication and analysis. Unfortunately, creating effective data visualizations is a challenge for both novice and expert design users. The task often involves an iterative process of trial and error, which by its nature, is time-consuming. Designers…
Descriptors: Artificial Intelligence, Computer Simulation, Visualization, Feedback (Response)
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Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
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