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Lydia P. Gleaves; David A. Broniatowski – Cognitive Research: Principles and Implications, 2024
As they become more common, automated systems are also becoming increasingly opaque, challenging their users' abilities to explain and interpret their outputs. In this study, we test the predictions of fuzzy-trace theory--a leading theory of how people interpret quantitative information--on user decision making after interacting with an online…
Descriptors: Intervention, Automation, Decision Making, Internet
Jessy Hsieh – ProQuest LLC, 2024
This dissertation proposes a strategy, or set of decision-making principles, for the education of adults. It explores two related questions: What is the purpose of andragogy and why does this work matter? This philosophical inquiry is situated within a broader context of the automation of knowledge work. As advances in information and…
Descriptors: Adult Education, Andragogy, Decision Making, Information Technology
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
Transparency Improves the Accuracy of Automation Use, but Automation Confidence Information Does Not
Monica Tatasciore; Luke Strickland; Shayne Loft – Cognitive Research: Principles and Implications, 2024
Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence…
Descriptors: Automation, Access to Information, Information Technology, Bias
Yan Xiong; Guo Xinya; Junjie Xu – Education and Information Technologies, 2024
Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstrated that there is a direct…
Descriptors: Learner Engagement, Learning Processes, Automation, Artificial Intelligence
Wallace N. Pinto Jr.; Jinnie Shin – Journal of Educational Measurement, 2025
In recent years, the application of explainability techniques to automated essay scoring and automated short-answer grading (ASAG) models, particularly those based on transformer architectures, has gained significant attention. However, the reliability and consistency of these techniques remain underexplored. This study systematically investigates…
Descriptors: Automation, Grading, Computer Assisted Testing, Scoring
Nesrine Mansouri; Mourad Abed; Makram Soui – Education and Information Technologies, 2024
Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…
Descriptors: Undergraduate Students, Decision Making, Majors (Students), Specialization
Pankaj Chejara; Luis P. Prieto; Yannis Dimitriadis; Maria Jesus Rodriguez-Triana; Adolfo Ruiz-Calleja; Reet Kasepalu; Shashi Kant Shankar – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the…
Descriptors: Learning Analytics, Attribution Theory, Acoustics, Artificial Intelligence
Jessie S. Barrot – Technology, Knowledge and Learning, 2025
Significant advancements in artificial intelligence (AI) technologies have led to the development of Google Gemini, which can be used to provide automated writing assistance. Within higher education, this feature extends to research writing. However, skepticism is particularly evident as students, teachers, and researchers in universities explore…
Descriptors: Artificial Intelligence, Computer Software, Computer Uses in Education, Educational Research
Johan Syahbrudin; Edi Istiyono; Moh. Khairudin; Anita Anggraini; Indah Urwatin Wusqo; Metta Mariam; Yenni Muflihan – Contemporary Educational Technology, 2025
Computer-based assessment (CBA) is a top-rated tool for conducting assessments, mapping learning outcomes, and selecting new candidates. Research that examines the development and use of CBA is also increasing from year to year, so without bibliometric analysis, it would be quite challenging to keep up with all of these studies. This study aims to…
Descriptors: Computer Assisted Testing, Educational Research, Educational Trends, Bibliometrics
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