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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
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
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
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
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)
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
Why Explainable AI May Not Be Enough: Predictions and Mispredictions in Decision Making in Education
Mohammed Saqr; Sonsoles López-Pernas – Smart Learning Environments, 2024
In learning analytics and in education at large, AI explanations are always computed from aggregate data of all the students to offer the "average" picture. Whereas the average may work for most students, it does not reflect or capture the individual differences or the variability among students. Therefore, instance-level…
Descriptors: Artificial Intelligence, Decision Making, Predictor Variables, Feedback (Response)
Download full textDragica Ljubisavljevic; Marko Koprivica; Aleksandar Kostic; Vladan Devedžic – International Association for Development of the Information Society, 2023
This paper delves into statistical disparities between human-written and ChatGPT-generated texts, utilizing an analysis of Shannon's equitability values, and token frequency. Our findings indicate that Shannon's equitability can potentially be a differentiating factor between texts produced by humans and those generated by ChatGPT. Additionally,…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Writing (Composition)
Okan Bulut; Tarid Wongvorachan; Surina He; Soo Lee – Discover Education, 2024
Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school…
Descriptors: High School Students, Dropouts, Identification, Man Machine Systems
Adiguzel, Tufan; Kaya, Mehmet Haldun; Cansu, Fatih Kürsat – Contemporary Educational Technology, 2023
Artificial intelligence (AI) introduces new tools to the educational environment with the potential to transform conventional teaching and learning processes. This study offers a comprehensive overview of AI technologies, their potential applications in education, and the difficulties involved. Chatbots and related algorithms that can simulate…
Descriptors: Artificial Intelligence, Educational Technology, Barriers, Man Machine Systems
Muller, Ashley Elizabeth; Ames, Heather Melanie R.; Jardim, Patricia Sofia Jacobsen; Rose, Christopher James – Research Synthesis Methods, 2022
Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…
Descriptors: Artificial Intelligence, Man Machine Systems, Automation, Literature Reviews
