Information Verification & Media Literacy

Something 'real' is something that actually happened in the world the way it is shown; something 'made up' is something a person imagined or created for a story, game, or joke.+1 more

Things I see on a screen are not automatically true — people make them up sometimes, and checking with a grown-up is a skill, not a sign I don't know.

I can complete a rubric-assessed short oral or written task in which I am shown an age-appropriate piece of digital content, say whether I think it is real or made up, explain my reasoning in a sentence, and name a trusted adult I would ask if I wasn't sure.

Every piece of digital content — a video, a post, an article, a picture — has a maker: a named person, an organisation, or an account. Finding the maker is the first step in deciding whether to trust the content.+2 more

Where something came from matters at least as much as what it says — and the maker of a claim is information I can look for, not a mystery.

I can complete a rubric-assessed short written task, given two pieces of digital content on the same topic, in which I identify the maker of each, state whether each maker is from a 'trusted place' and why, and explain why I do or do not believe each piece of content.

Lateral reading is the practice of opening a new tab and searching for information about the source before deciding whether to trust what it says — rather than reading only within the source itself.+2 more

A claim on its own tells me less than the same claim compared to other sources — and deception usually leaves specific signs I can learn to spot, not a vague feeling.

I can complete a rubric-assessed written comparison of two sources making the same claim in which I apply lateral reading to find out who is behind each source, name specific manipulation signs present or absent in each, and justify which source I trust more with at least two named criteria.

A verification process (e.g. SIFT: Stop, Investigate the source, Find better coverage, Trace claims to the original context) is a named sequence of steps that can be applied to any claim, not just ones already flagged as suspicious.+2 more

What I believe, what I share, and what I act on are three different decisions about the same claim — and treating them as separate is what keeps me from accidentally amplifying something I haven't actually checked.

I can complete a rubric-assessed written analysis of a digital claim in which I apply a named verification process (e.g. SIFT) to the claim, identify the origin, the evidence behind it, and the motivation of the source, and justify my separate conclusions about what I would believe, what I would share, and what I would act on.

Recommendation algorithms on major platforms are typically optimised for engagement — time on platform, clicks, reactions — not for accuracy or user wellbeing; content that provokes strong emotional reactions is amplified more than neutral content.+4 more

A claim does not arrive in my feed by accident — the system that chose to show it to me, and the mental system that receives it, both have biases I can learn to read.

I can complete a rubric-assessed written evaluation of a contested claim in which I weigh evidence across multiple sources using named criteria, identify at least two specific algorithmic, platform, or production-incentive factors that shaped how the claim reached me, name the self-bias most likely to affect my interpretation, and justify my conclusion with explicit awareness of that bias.

Synthetic media: AI-generated images, video, and audio can now pass casual perception in ways earlier manipulation could not; detection methods include reverse image search, metadata inspection, and specialised detectors, but no detection method is reliable on all cases.+3 more

At the highest level of media literacy, knowing what I cannot know — and being specific about the boundary — is a more reliable guide than any verification tool, because the tools can be defeated but the honest acknowledgement of limits cannot.

I can complete a rubric-assessed written analysis of a real-world contested claim involving synthetic, AI-generated, or sophisticated disinformation, in which I apply detection and verification methods, compare my verification process against a recognised critical-appraisal framework, and construct a reasoned conclusion that states what I believe, what I would share, what I would act on, and — specifically — what I cannot know and why.