Where the Answers Actually Come From

Where the Answers Actually Come From

In this episode we explore the surprising relationship between Wikipedia, AI chatbots, and the way students learn to trust information online. We look at how Wikipedia’s commitment to verifiability can actually make it more disciplined than many AI-generated responses. We also explore how teachers can use Wikipedia’s gaps as a powerful classroom demonstration of the difference between a grounded answer and a fluent one. Topics covered: * Why Wikipedia still matters in the age of AI * How AI tools may rely on public, well-sourced information * What happens when a chatbot encounters gaps in its source material * The difference between confidence and verification * How educators can teach students to question AI-generated responses * What “verifiability” means for research and writing instruction * How Wikipedia-based assignments can make student writing more authentic * A simple classroom activity for comparing Wikipedia and chatbot responses Source: https://aiedusimplified.substack.com/p/i-can-put-all-the-statements-i-want

How Kids Really Use AI Outside the Classroom

In this episode we explore a surprising finding from Common Sense Media’s new census on AI use by tweens and teens. Topics covered: * How tweens and teens are actually using AI * Why entertainment use may matter more than cheating * What students misunderstand about AI accuracy and truth * Why AI literacy is a comprehension issue, not just a rules issue * The role of teachers, librarians, and counselors in helping students evaluate AI * Why elementary and middle school students need age-appropriate AI guidance * How teachers can start better classroom conversations about AI use Source: https://www.commonsensemedia.org/research/a-comprehensive-report-on-teens-tweens-and-ai

When the Friend Who Listens Is a Bot

In this episode we explore how young people are using AI when adults are not watching — not just for homework help, but for emotional support, relationship advice, and navigating moments of loneliness, conflict, or uncertainty. We look at new research from The Rithm Project on youth, AI, and relationships, and ask what educators should understand before students return to school in the fall. Topics covered: * How students are using AI during unstructured time outside of school * Why AI use among young people is not just an academic integrity issue * Findings from The Rithm Project’s report, Youth, AI, and the Relationships That Shape Them * Why some students turn to AI because they feel like a burden to others * The difference between AI “companions” and everyday emotional support use * How AI may help students avoid the friction of real relationships * Why productive struggle matters in both learning and human connection * What teachers might ask students when they return in September Sources: https://www.therithmproject.org/research https://www.the74million.org/article/survey-young-people-turn-to-ai-to-be-their-real-unfiltered-selves/

AI Detectors, Teacher Training, and the Real Cheating Problem

In this episode, we cover three timely AI-in-education stories that all point to the same challenge: schools are moving fast, but the systems around AI policy, teacher training, and academic integrity are still catching up. Topics Covered * Why Wake County is moving to ban AI detectors in schools * The case of Eleanor Canina and the risks of false AI accusations * Why AI detection tools may unfairly flag multilingual and neurodivergent students * What Microsoft’s 2026 AI in Education report says about teacher AI use * Why many educators are using AI without formal training * Academic integrity as a top concern for both teachers and students * New survey data on cheating among Harvard seniors * A hopeful student perspective on AI cheating Sources: https://www.wral.com/news/education/whats-in-wake-schools-new-ai-policy-draft-june-2026/ https://news.microsoft.com/source/2026/06/24/microsofts-new-ai-in-education-report-highlights-widespread-adoption-and-increasing-demand-for-support/ https://fortune.com/2026/06/23/harvard-cheating-academic-integrity-ai-detection/

What Eye-Tracking Research Shows About Spotting AI Deepfakes

In this episode we explore how students learn to spot AI-generated deepfakes, and why confidence may be one of the biggest risks in AI literacy. We look at a new study from researchers in Germany that used eye-tracking technology to examine where students look when trying to decide whether an image is real or AI-generated. Topics covered: * How students visually inspect AI-generated images * What eye-tracking research reveals about deepfake detection * The difference between gut-level pattern recognition and systematic scanning * Why AI-generated images often fail at the edges * How critical thinking instruction can change students’ attention habits * Why students may overestimate their ability to spot deepfakes * The limits of one-off AI literacy lessons Sources: https://www.sciencedirect.com/science/article/pii/S0360131525002982