Information literacy

EducationOriginal

Ensuring Human-Centered AI EdTech: Inclusive Design and Evolving Information, Digital, Media, and Algorithmic Literacies

Emerging technologies increasingly impact the design of and access to education. Current research in higher education and educational technology argues the benefits (e.g., time-saving, personalization, scalability) and concerns (e.g., academic integrity, accessibility, data reliability, ethics, privacy) of students using artificial intelligence in education. Though these pro and con lists may be valid and growing, a perspective is often missing from conversations about AI in education: accessibility and people with disabilities. This article first reviews the importance of understanding relevant literacies—information, digital, media, and algorithmic—and describes examples of educational technologies (EdTech) that highlight learning objectives of using and creating knowledge and content with those tools. Then, inclusive and human-centered design principles are discussed as a foundational construct to design human-centered AI and use cases for integrating AI in learning design.

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EducationFeatured

Stepping Up to BAT: Inspiration for a Research Process Model

Wouldn’t it be great that at the same time you were learning to read chapter books and basic informational texts, you could learn a research process that could carry you right through post-secondary studies? As M. E. Marland, a member of the UK Schools Council, asserted in 1981, from elementary school to PhD studies, in research, the questions and processes remain fundamentally the same. To find out if that was true for the Canadian context, for my PhD dissertation study I decided to observe the information behaviours of grade-three students as they worked on a class project.

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FeaturedTranslation

What Is Infrastructural Meaning-Making and Why Do We Need It?

Not only is it becoming increasingly difficult to distinguish fact from opinion, but also to understand why certain content ends up in our feeds, recommendations or search results in the first place. Yet it’s more important than ever to understand it. This is where infrastructural meaning-making comes into play, and it’s something that the datafied society needs to understand.

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