AI in learning : Preparing grounds for future learning

Show full item record



Permalink

http://hdl.handle.net/10138/334130

Citation

Niemi , H 2021 , ' AI in learning : Preparing grounds for future learning ' , Journal of Pacific Rim Psychology , vol. 15 , 18344909211038105 . https://doi.org/10.1177/18344909211038105

Title: AI in learning : Preparing grounds for future learning
Author: Niemi, Hannele
Contributor organization: Department of Education
Date: 2021-08
Language: eng
Number of pages: 12
Belongs to series: Journal of Pacific Rim Psychology
ISSN: 1834-4909
DOI: https://doi.org/10.1177/18344909211038105
URI: http://hdl.handle.net/10138/334130
Abstract: This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher's role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.
Subject: 516 Educational sciences
artificial intelligence
human learning
machine learning
deep learning
explainability and explicability
SCIENCE
Peer reviewed: No
Rights: cc_by_nc
Usage restriction: openAccess
Self-archived version: publishedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
18344909211038105.pdf 275.6Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record