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Students work in pairs to analyze their social media feeds for 15 minutes. They document what content appears first, identifying patterns in the types of posts shown. Following this, they switch devices with their partner and compare feeds, discussing how algorithms create different experiences for different users. Conclude with a class discussion about content personalization and its implications.

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Students create their own basic 'AI news curator' by establishing a set of rules for selecting and prioritizing news stories. They write down 5-7 criteria they would program into an algorithm (e.g., credibility of source, publication date, topic relevance). Then, given a set of 15 news articles, they apply their 'algorithm' to rank them. Compare different student algorithms and discuss potential biases in their selection criteria.

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Students become digital detectives in this hands-on activity. Provide them with 3-4 news articles (mix of real and fabricated) about AI technology. Using a fact-checking toolkit (including reverse image search, source verification, and cross-referencing), students work in groups to determine which articles are legitimate and which contain misinformation. Each group presents their findings and methodology to the class.

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Students conduct a week-long experiment documenting their YouTube recommendations. They start by watching specific types of content for 10 minutes daily, then observe how their recommendations change. They maintain a digital journal of changes in their recommended videos and create a visual representation of their 'filter bubble.' Conclude with a class discussion about the implications of these algorithmic recommendations.

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Provide students with pairs of articles - one written by a human and one by AI (e.g., ChatGPT). In groups, they analyze and identify distinguishing characteristics of each, creating a checklist for spotting AI-generated content. Discussion focuses on the implications of AI-generated news and content in our information ecosystem.

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How to Spot AI-generated Content: Is It Fact or Fiction? | Capitol Technology University

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In an age of AI-generated content, how do we spot the fakes? Here are a few tips and tricks to help.

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Students physically act out how a recommendation algorithm works. Divide the class into 'users,' 'content pieces,' and 'algorithm workers.' Algorithm workers must match content to users based on given preferences and interaction history. This kinesthetic learning activity helps students understand how algorithmic recommendations function in real-world applications.

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Students create visual maps of their own potential echo chambers using mind-mapping tools. They identify their primary sources of news and information, analyzing how these sources might reinforce existing beliefs. They then brainstorm strategies to diversify their information sources and break out of their echo chambers.

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Create a 'bias detection leaderboard' where students earn points for identifying different types of bias in news articles (confirmation bias, selection bias, political bias, etc.). Provide articles about AI and technology from various sources. Students work individually to analyze articles and present their findings, with peers voting on the accuracy of their bias detection.

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Organize a structured debate about AI in news and information. Assign students to groups representing different stakeholders: AI developers, journalists, social media companies, and consumers. Each group researches their perspective and participates in a moderated debate about responsibilities in preventing misinformation and ensuring ethical AI use in media.

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Students design a hypothetical 'ethical algorithm' for a news platform. They must consider and document: 1) What factors their algorithm would consider when showing news to users, 2) How they would prevent the spread of misinformation, 3) How they would ensure diverse viewpoints are represented, and 4) How they would maintain user engagement while being responsible. Students create flowcharts to visualize their algorithm's decision-making process.

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Students maintain a semester-long digital portfolio documenting their encounters with AI and algorithms in daily life. They collect screenshots, write reflections, and analyze how AI influences their information consumption. The portfolio includes examples of identified misinformation, algorithm effects, and strategies they've developed for maintaining informed digital citizenship. End the semester with portfolio presentations and peer feedback sessions.