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Birks, Daniel; Clare, Joseph – International Journal for Educational Integrity, 2023
This paper connects the problem of artificial intelligence (AI)-facilitated academic misconduct with crime-prevention based recommendations about the prevention of academic misconduct in more traditional forms. Given that academic misconduct is not a new phenomenon, there are lessons to learn from established information relating to misconduct…
Descriptors: Artificial Intelligence, Cheating, Student Behavior, Prevention
Sarah W. Beck; Sarah Levine – Reading Research Quarterly, 2024
In "Parable of the Sower," Octavia Butler (1993) wrote: "Any Change may bear seeds of benefit. Seek them out. Any Change may bear seeds of harm. Beware" (p. 116). In this paper, we apply this command to a speculative examination of the consequences of text-based generative AI (GAI) for adolescent writers, framing this…
Descriptors: Artificial Intelligence, Writing (Composition), Student Behavior, Writing Processes
Ash Tea; Dax Ovid – CBE - Life Sciences Education, 2024
Informed by social science fields including psychology and public health, we propose a Model for Emotional Intelligence to advance biology education research in affective learning. The model offers a shared discourse for biology education researchers to develop and assess evidence-based strategies to perceive, use, understand, and manage emotions…
Descriptors: Undergraduate Students, Science Instruction, Biology, Emotional Intelligence
Efren de la Mora Velasco; Matthew Moreno – Educational Technology Research and Development, 2025
The measurable effects of music in online learning remains a topic of extensive debate, largely due to inconsistent findings within existing literature. Many of these inconclusive results stem from research methodologies that focus on singular perspectives, often overlooking a balance between cognitive challenges and emotional benefits of…
Descriptors: Music, Acoustics, Electronic Learning, Cognitive Processes
Omar Albaloul; Risto Marttinen; Chad Killian – Journal of Physical Education, Recreation & Dance, 2024
The recent emergence of artificial intelligence (AI) tools has significantly influenced different fields, including education. One notable example is ChatGPT, an AI-driven large language model (LLM) developed by OpenAI. This tool holds potential for supporting both teachers and students in the teaching and learning process. While some fields of…
Descriptors: Physical Education, Artificial Intelligence, Natural Language Processing, Man Machine Systems
Madison A. Pollino; Elliot A. Powell; Melissa L. McCormick – Communication Teacher, 2025
This article offers a semester-long approach to using generative AI in the public-speaking course. Using critical communication pedagogy, the authors provide practices to navigate the turbulence that has followed the emergence of publicly available generative AI tools. These tools have received negative attention because of their potential to…
Descriptors: Artificial Intelligence, Natural Language Processing, Public Speaking, Technology Uses in Education
Pedro Isaias; Tania Hoque; Paula Miranda – International Association for Development of the Information Society, 2024
While the higher education sector is continuously searching for innovative technologies, the use of chatbots requires extensive research and careful consideration of their pedagogical value. The lessons learned from lecturers who experiment with chatbots can constitute important evidence to support their use. This paper presents a chatbot…
Descriptors: Instructional Material Evaluation, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Jalal, Ahmad; Mahmood, Maria – Education and Information Technologies, 2019
Rapid growth and recent developments in education sector and information technologies have promoted E-learning and collaborative sessions among the learning communities and business incubator centers. Traditional practices are being replaced with webinars (live online classes) E-Quizes (online testing) and video lectures for effective learning and…
Descriptors: Information Technology, Cognitive Processes, Student Behavior, Electronic Learning
Irene Picton; Christina Clark – National Literacy Trust, 2024
Recent developments in technology have accelerated the influence of artificial intelligence (AI) on our lives. The ability of generative-AI tools such as ChatGPT, Gemini and Claude to both 'write' and 'read' texts in a human-like manner means they are set to play an increasingly important role in the literacy lives of children, young people and…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Schultz, Robert Arthur – Roeper Review, 2018
This article focuses on (a) distinctions between the profoundly gifted individual and others, and (b) a chart describing tendencies and/or behaviors associated with the profoundly gifted student in common, mixed-ability educational settings. The intent is to provide educators, parents, and policymakers with preliminary awareness about the unique…
Descriptors: Academically Gifted, Student Behavior, Mainstreaming, Heterogeneous Grouping
Sarah Elaine Eaton – Online Submission, 2023
If you think academic integrity is only about student conduct, you may be living in the past. In this opening keynote, Dr. Sarah Elaine Eaton, provides insights from the latest research around the world that shows how academic and research integrity include, and extend student conduct. She'll bring insights from the "Handbook of Academic…
Descriptors: Guides, Ethics, Integrity, Plagiarism
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Redding, Sam; Twyman, Janet – Center on Innovations in Learning, Temple University, 2018
Teachers are accustomed to gathering and analyzing data that show what a student knows, has learned, and has not yet learned. With this information, aggregated for the class, the teacher may alter her lesson plans to accelerate her pace or slow down to reteach. She may make notes to alter the lesson design for the next time she teaches the lesson.…
Descriptors: Student Behavior, Behavior Patterns, Learning, Individualized Instruction
Brackett , Marc A.; Simmons, Dena – Educational Leadership, 2015
When students chronically misbehave and act disengaged in school, how do we know what they're really feeling? In this article, Marc A. Brackett and Dena Simmons of the Yale Center for Emotional Intelligence describe how understanding the science of emotions can help both students and teachers take charge of their emotions to achieve their goals.…
Descriptors: Student Behavior, Behavior Problems, Emotional Response, Emotional Intelligence
Fiorello, Catherine A.; Jenkins, Tiffany K. – Communique, 2018
This article is an overview of identification of intellectual disabilities (ID), with a focus on meeting legal and ethical requirements when assessing children from culturally and linguistically diverse backgrounds and those living in poverty. Specific procedures and recommended instruments will be reviewed.
Descriptors: Intellectual Disability, Disability Identification, Best Practices, Legal Responsibility
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