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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
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
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)
Aaron Wolf – Educational Theory, 2025
Much has been written about how to improve the fairness of AI tools for decision-making but less has been said about how to approach this new field from the perspective of philosophy of education. My goal in this paper is to bring together criteria from the general algorithmic fairness literature with prominent values of justice defended by…
Descriptors: Algorithms, Artificial Intelligence, Technology Uses in Education, Educational Philosophy
Stephanie Moore; Amir Hedayati-Mehdiabadi; Victor Law; Sung Pil Kang – TechTrends: Linking Research and Practice to Improve Learning, 2024
Early hype cycles surrounding new technologies may promote simplistic binary options of either adoption or rejection, but socio-historical analyses of technologies illuminate how they are worked into shape by human actors. Humans enact agency through many choices that result in adaptations and contextual variations. In this piece, we argue that…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Ethics
Eric Ortega González; Jairo Jiménez – Educational Philosophy and Theory, 2025
This article examines contemporary educational practices within the rapidly evolving landscape of Artificial Intelligence. We do so by analysing the relationship between artificiality and naturalness in education. Education, often characterized as a human and thus natural-historical phenomenon, now appears increasingly shaped by artificial…
Descriptors: Artificial Intelligence, Educational Practices, Man Machine Systems, Data Analysis
Ruixun Dai; Matthew Krehl Edward Thomas; Shaun Rawolle – Australian Educational Researcher, 2025
Education has always been in a state of flux because of technological disruption. As schools head towards a present in which digital technology is normalised as part of the fabric of everyday society, a post-digital paradigm, Artificial Intelligence (AI) is changing the educational administration and leadership. It is crucial to find ways to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Administration, Decision Making
Max Kailler Smith; Amelia R. Kracinovich; Brandon J. Schrom; Timothy L. Dunn – Cognitive Research: Principles and Implications, 2025
As automation becomes increasingly integrated into complex military tasks, its role in supporting human performance under fatigue warrants careful evaluation. A specific military use case in which automatic target cuing (ATC) is integrated is undersea threat detection (UTD). These types of tasks demand sustained vigilance, accurate classification,…
Descriptors: Fatigue (Biology), Performance, Metacognition, Cues
Wang, Yinying – Journal of Educational Administration, 2021
Purpose: Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership. Design/methodology/approach:…
Descriptors: Artificial Intelligence, Instructional Leadership, Decision Making, Man Machine Systems
Ryan Wesslen – ProQuest LLC, 2021
In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five experiments to measure the role of anchoring bias, confirmation bias, belief bias, and myopic loss…
Descriptors: Decision Making, Visual Aids, Online Systems, Bias
Junhong Xiao; Aras Bozkurt; Mark Nichols; Angelica Pazurek; Christian M. Stracke; John Y. H. Bai; Robert Farrow; Dónal Mulligan; Chrissi Nerantzi; Ramesh Chander Sharma; Lenandlar Singh; Isak Frumin; Andrew Swindell; Sarah Honeychurch; Melissa Bond; Jon Dron; Stephanie Moore; Jing Leng; Patricia J. Slagter van Tryon; Manuel Garcia; Evgeniy Terentev; Ahmed Tlili; Thomas K. F. Chiu; Charles B. Hodges; Petar Jandric; Alexander Sidorkin; Helen Crompton; Stefan Hrastinski; Apostolos Koutropoulos; Mutlu Cukurova; Peter Shea; Steven Watson; Kai Zhang; Kyungmee Lee; Eamon Costello; Mike Sharples; Anton Vorochkov; Bryan Alexander; Maha Bali; Robert L. Moore; Olaf Zawacki-Richter; Tutaleni Iita Asino; Henk Huijser; Chanjin Zheng; Sunagül Sani-Bozkurt; Josep M. Duart; Chryssa Themeli – TechTrends: Linking Research and Practice to Improve Learning, 2025
Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Trends, Trend Analysis
Wilson, Cristina G.; Qian, Feifei; Jerolmack, Douglas J.; Roberts, Sonia; Ham, Jonathan; Koditschek, Daniel; Shipley, Thomas F. – Cognitive Research: Principles and Implications, 2021
How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore…
Descriptors: Hypothesis Testing, Data Collection, Information Seeking, Decision Making
Download full textJu, Song; Zhou, Guojing; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making…
Descriptors: Decision Making, Reinforcement, Artificial Intelligence, Man Machine Systems
Ilker Cingillioglu – Studies in Higher Education, 2024
This study provides an empirical approach to utilizing an Artificial Intelligence (AI)-based system for identifying students' university choice factors that impact their matriculation decision. We created an AI-based chatbot that gathered both qualitative and quantitative data from nearly 1200 participants worldwide. The entire human-AI…
Descriptors: Admission (School), Decision Making, Student Attitudes, College Choice
