Recent advancements in artificial intelligence make its use in education more likely. In fact, existing learning systems already utilize it for supporting students' learning or teachers' judgments. In this perspective article, we want to elaborate on the role of humans in making decisions in the design and implementation process of artificial intelligence in education. Therefore, we propose that an artificial intelligence-supported system in education can be considered a closed-loop system, which includes the steps of (i) data recording, (ii) pattern detection, and (iii) adaptivity. Besides the design process, we also consider the crucial role of the users in terms of decisions in educational contexts: While some implementations of artificial intelligence might make decisions on their own, we specifically highlight the high potential of striving for hybrid solutions in which different users, namely learners or teachers, are provided with information from artificial intelligence transparently for their own decisions. In light of the non-perfect accuracy of decisions of both artificial intelligence-based systems and users, we argue for balancing the process of human- and AI-driven decisions and mutual monitoring of these decisions. Accordingly, the decision-making process can be improved by taking both sides into account. Further, we emphasize the importance of contextualizing decisions. Potential erroneous decisions by either machines or humans can have very different consequences. In conclusion, humans have a crucial role at many stages in the process of designing and using artificial intelligence for education.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453250PMC
http://dx.doi.org/10.3389/fpsyg.2022.956798DOI Listing

Publication Analysis

Top Keywords

artificial intelligence
24
intelligence education
16
decisions
9
artificial
8
crucial role
8
intelligence
6
education
5
process
5
closing loop
4
loop human
4

Similar Publications

Intelligent Analgesia Management System in Postoperative Pain Management: A Retrospective Analysis.

J Perianesth Nurs

January 2025

Department of Nursing, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.

Purpose: This study aimed to explore the effect of an intelligent analgesia management system on postoperative pain management and the working mode of acute pain service.

Design: This is a retrospective cohort study.

Methods: A total of 584 patients who underwent laparoscopic abdominal surgery under general anesthesia and voluntarily received intravenous patient-controlled analgesia (PCA) between January 2018 and April 2020 at our hospital were selected.

View Article and Find Full Text PDF

Demographic-Based Personalized Left Ventricular Hypertrophy Thresholds for Hypertrophic Cardiomyopathy Diagnosis.

J Am Coll Cardiol

December 2024

Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom.

Background: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death. Current diagnosis emphasizes the detection of left ventricular hypertrophy (LVH) using a fixed threshold of ≥15-mm maximum wall thickness (MWT). This study proposes a method that considers individual demographics to adjust LVH thresholds as an alternative to a 1-size-fits-all approach.

View Article and Find Full Text PDF

Soybean, the fourth most important crop in the world, uniquely serves as a source of both plant oil and plant protein for the world's food and animal feed. Although soybean production has increased approximately 13-fold over the past 60 years, the continually growing global population necessitates further increases in soybean production. In the past, especially in the last decade, significant progress has been made in both functional genomics and molecular breeding.

View Article and Find Full Text PDF

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!