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Direct linkJonathon Love; Quentin F. Gronau; Gemma Palmer; Ami Eidels; Scott D. Brown – Cognitive Research: Principles and Implications, 2024
With the growing role of artificial intelligence (AI) in our lives, attention is increasingly turning to the way that humans and AI work together. A key aspect of human-AI collaboration is how people integrate judgements or recommendations from machine agents, when they differ from their own judgements. We investigated trust in human-machine…
Descriptors: Artificial Intelligence, Man Machine Systems, Trust (Psychology), Decision Making
Florent Vinchon; Todd Lubart; Sabrina Bartolotta; Valentin Gironnay; Marion Botella; Samira Bourgeois-Bougrine; Jean-Marie Burkhardt; Nathalie Bonnardel; Giovanni Emanuele Corazza; Vlad Glaveanu; Michael Hanchett Hanson; Zorana Ivcevic; Maciej Karwowski; James C. Kaufman; Takeshi Okada; Roni Reiter-Palmon; Andrea Gaggioli – Journal of Creative Behavior, 2023
With the advent of artificial intelligence (AI), the field of creativity faces new opportunities and challenges. This manifesto explores several scenarios of human--machine collaboration on creative tasks and proposes "fundamental laws of generative AI" to reinforce the responsible and ethical use of AI in the creativity field. Four…
Descriptors: Artificial Intelligence, Creativity, Man Machine Systems, Ethics
Feng Hsu Wang – IEEE Transactions on Learning Technologies, 2024
Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in…
Descriptors: Learning Processes, Models, Man Machine Systems, Cooperative Learning
Laura E. Matzen; Zoe N. Gastelum; Breannan C. Howell; Kristin M. Divis; Mallory C. Stites – Cognitive Research: Principles and Implications, 2024
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and…
Descriptors: Artificial Intelligence, Error Patterns, Decision Making, Models
Yuan Liu; Shuaifei Huang; Weiguo Xu; Zhuang Wang; Dong Ming – npj Science of Learning, 2024
Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned…
Descriptors: Man Machine Systems, Robotics, Brain Hemisphere Functions, Psychomotor Skills
Debora Weber-Wulff; Alla Anohina-Naumeca; Sonja Bjelobaba; Tomáš Foltýnek; Jean Guerrero-Dib; Olumide Popoola; Petr Šigut; Lorna Waddington – International Journal for Educational Integrity, 2023
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for…
Descriptors: Artificial Intelligence, Identification, Man Machine Systems, Accuracy
George Veletsianos; Shandell Houlden; Nicole Johnson – TechTrends: Linking Research and Practice to Improve Learning, 2024
Much of the literature on artificial intelligence (AI) in education imagines AI as a tool in the service of teaching and learning. Is such a one-way relationship all that exists between AI and learners? In this paper we report on a thematic analysis of 92 participant responses to a story completion exercise which asked them to describe a classroom…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Interaction
Yung-Ming Cheng – Interactive Technology and Smart Education, 2024
Purpose: The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals' learning engagement (LE) can affect their learning…
Descriptors: MOOCs, Academic Persistence, Medical Education, Learner Engagement
Daniel J. Carragher; Daniel Sturman; Peter J. B. Hancock – Cognitive Research: Principles and Implications, 2024
The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or…
Descriptors: Automation, Human Body, Man Machine Systems, Accuracy
De Ruyck, Olivia; Conradie, Peter; Van Hove, Stephanie; All, Anissa; Baccarne, Bastiaan; De Marez, Lieven; Saldien, Jelle – International Journal of Technology and Design Education, 2023
Interactions between humans and smart products (i.e. digital components integrated in physical Internet of Things devices) are becoming more complex and less visible. Yet designers lack tools to capture these interactions and incorporate them into their design. In this paper we present the Human-Computer-Context Interaction (HCCI) tool that helps…
Descriptors: Man Machine Systems, Industrial Arts, Design, Internet
Crescenzi-Lanna, Lucrezia – Journal of Research on Technology in Education, 2023
This paper presents a systematic literature review of artificial intelligence (AI)-supported teaching and learning in early childhood. The focus is on human-machine cooperation in education. International evidence and associated problems with the reciprocal contributions of humans and machines are presented and discussed, as well as future…
Descriptors: Artificial Intelligence, Programming, Educational Technology, Man Machine Systems
Francisco Tigre Moura; Chiara Castrucci; Clare Hindley – Journal of Creative Behavior, 2023
This paper presents a study analyzing the perception of artistic products created by or with the support of artificial intelligence (AI). The research builds on previous studies revealing that the output of artificial creativity processes can indeed rival human-made products, satisfy consumer expectations, and derive enjoyment. However, in…
Descriptors: Creativity, Artificial Intelligence, Art, Automation
Paladines, José; Ramírez, Jaime; Berrocal-Lobo, Marta – Interactive Learning Environments, 2023
This paper addresses the challenge of integrating a dialog system with an ITS created for supporting procedural training in a 3D virtual environment. To this end, we first describe the desired features of the dialog to be provided to students in such system. Then, we explain some technical issues of our proposal such as the architecture; the…
Descriptors: Intelligent Tutoring Systems, Dialogs (Language), Man Machine Systems, Computer Simulation
Han, Zhongmei; Tu, Yaxin; Huang, Changqin – IEEE Transactions on Learning Technologies, 2023
The education metaverse (Edu-Metaverse), as a simulated extension of the real world, is an infinite virtual space where learners can build their relationships with others and create interactive content. However, preparing learners to engage fully with Edu-Metaverse remains challenging. As technologies on Edu-Metaverse are new to learners, there is…
Descriptors: Technology Uses in Education, Computer Simulation, Learner Engagement, Interaction
Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
