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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
Emmanuel Senior Tenakwah; Gideon Boadu; Emmanuel Junior Tenakwah; Michael Parzakonis; Mark Brady; Penny Kansiime; Shannon Said; Sarah Eyaa; Raymond Kwojori Ayilu; Ciprian Radavoi; Alan Berman – Knowledge Management & E-Learning, 2025
The development and introduction of AI language models have transformed the way humans and institutions interact with technology, enabling natural and intuitive communication between humans and machines. This paper conducts a competence-based analysis of ChatGPT's task responses to provide insights into its language proficiency, critical analysis…
Descriptors: Higher Education, Evaluation Methods, Artificial Intelligence, Computer Software
Reem Jalal Eddine; Claudio Mulatti; Francesco N. Biondi – Cognitive Research: Principles and Implications, 2024
The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this…
Descriptors: Motor Vehicles, Attention Control, Artificial Intelligence, Eye Movements
Bahar Radmehr; Adish Singla; Tanja Käser – International Educational Data Mining Society, 2024
There has been a growing interest in developing learner models to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted representations of tasks, thereby limiting the agent's ability to generalize skills across tasks. In this…
Descriptors: Reinforcement, Artificial Intelligence, Educational Environment, Natural Language Processing
Elisabeth Bauer; Constanze Richters; Amadeus J. Pickal; Moritz Klippert; Michael Sailer; Matthias Stadler – British Journal of Educational Technology, 2025
This study explores whether AI-generated adaptive feedback or static feedback is favourable for student interest and performance outcomes in learning statistics in a digital learning environment. Previous studies have favoured adaptive feedback over static feedback for skill acquisition, however, without investigating the outcome of students'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Feedback (Response), Statistics Education
Alexis Danielle Bolick; Rafael Leonardo da Silva – TechTrends: Linking Research and Practice to Improve Learning, 2024
This article explores the potential impact of Artificial Intelligence (AI) tools on Instructional Design (ID) workflows and organizations from a systems thinking perspective (Meadows, 2008). We provide an in-depth analysis of how three AI tools, ChatGPT, Midjourney, and Descript, can enhance efficiency in instructional design content creation…
Descriptors: Artificial Intelligence, Instructional Design, Task Analysis, Ethics
Umut Zeki; Tolgay Karanfiller; Kamil Yurtkan – Education and Information Technologies, 2024
The developmental, characteristics and educational competencies of students who need special education are developing slowly in compared to their agemates. This is because their expressive language is different. In order to overcome these challenges, assistive technologies can be used under the supervision of the teachers. In this paper, a person…
Descriptors: Special Education, Expressive Language, Assistive Technology, Artificial Intelligence
Rebecca J. Collie; Andrew J. Martin – Social Psychology of Education: An International Journal, 2025
Educational bodies are weighing up the extent to which generative artificial intelligence (genAI) is embedded within educational settings. Although researchers have examined how (generative) AI can be used for effective teaching and learning, less is known about how genAI was being integrated within teachers' practice shortly after the wide-scale…
Descriptors: Teaching Methods, Learning Processes, Artificial Intelligence, Computer Software
Shari Cavicchi; Abdulaziz Abubshait; Giulia Siri; Magda Mustile; Francesca Ciardo – Cognitive Research: Principles and Implications, 2025
Cognitive load occurs when the demands of a task surpass the available processing capacity, straining mental resources and potentially impairing performance efficiency, such as increasing the number of errors in a task. Owing to its ubiquity in real-world scenarios, the existence of offloading strategies to reduce cognitive load is not new to…
Descriptors: Robotics, Psychological Patterns, Cognitive Processes, Computer Software
Min Tang; Sebastian Hofreiter; Christian H. Werner; Aleksandra Zielinska; Maciej Karwowski – Journal of Creative Behavior, 2025
Recent research suggests that working with generative artificial intelligence (AI), such as ChatGPT, can produce more creative outcomes than humans alone. However, does AI retain its creative edge when humans have access to alternative information sources, such as another human or the internet. We explored this question in a between-group…
Descriptors: Creative Thinking, Man Machine Systems, Interaction, Internet
Jie Zhang; Ali Derakhshan – European Journal of Education, 2025
Grounded in self-determination theory (SDT), this longitudinal study examined the potential of GPT-4o, an artificial intelligence (AI) tool, in K-12 school contexts by assessing its long-term impact on high school students' self-regulated learning (SRL) and task engagement. Furthermore, the study tracked the developmental trajectory of these…
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Independent Study
Nicolas Loiseau; Adrien Bruni; Pierre Puigpinos; Jean-Christophe Sakdavong – International Association for Development of the Information Society, 2024
This paper explores the concept of self-efficacy and its impact on individual performance on a mobile learning application. Self-efficacy refers to one's belief in their ability to achieve their goals and is a key factor in everyday life. To investigate the relationship between self-efficacy and performance, we conducted an experiment with 104…
Descriptors: Self Efficacy, Telecommunications, Handheld Devices, Computer Software
Yamauchi, Taisei; Flanagan, Brendan; Nakamoto, Ryosuke; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki – Smart Learning Environments, 2023
In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and…
Descriptors: Mathematics Instruction, Classification, Algorithms, Barriers
Xavier Ochoa; Xiaomeng Huang; Yuli Shao – Journal of Learning Analytics, 2025
Generative AI (GenAI) has the potential to revolutionize the analysis of educational data, significantly impacting learning analytics (LA). This study explores the capability of non-experts, including administrators, instructors, and students, to effectively use GenAI for descriptive LA tasks without requiring specialized knowledge in data…
Descriptors: Learning Analytics, Artificial Intelligence, Computer Software, Scores
Matsuda, Noboru – International Journal of Artificial Intelligence in Education, 2022
This paper demonstrates that a teachable agent (TA) can play a dual role in an online learning environment (OLE) for learning by teaching--the teachable agent working as a synthetic peer for students to learn by teaching and as an interactive tool for cognitive task analysis when authoring an OLE for learning by teaching. We have developed an OLE…
Descriptors: Artificial Intelligence, Teaching Methods, Intelligent Tutoring Systems, Feedback (Response)

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