In this ChatEDU Check-In: Make Them Read!, Liz explores the systemic decline of long-form reading in classrooms and the rise of the "excerpt culture." This episode examines how the shift toward clips and samples over the last fifteen years has eroded student attention spans and basic reading skills.

Key Takeaways:

The transition from full-length novels to abbreviated samples reflects a broader cultural shift that prioritizes the "reel" and the "clip" over deep literary engagement.

Requiring students to commit to long-form texts acts as a strategic resistance to the monetization of attention, allowing them to reclaim their focus from digital distractions.

Authentic comprehension and original voice are best developed through "flash essays" and unassisted, timed tasks that remove AI bumpers and force students to confront intellectual uncertainty.

Liz’s Two Cents: There is a profound tension between "meeting students where they are" and the pedagogical necessity of waiting for them to catch up to the heights of great literature. If educators treat declining attention spans as a terminal condition rather than a challenge to be met with more rigorous engagement, they risk making the "end of reading" a self-fulfilling prophecy.

Article Link:

https://www.theatlantic.com/ideas/2026/02/youth-reading-books-professors/685825/?taid=69814c1dbe49b700014af753&utm_campaign=the-atlantic&utm_content=edit-promo&utm_medium=social&utm_source=twitter

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In this ChatEDU Check-In: The Silicon Gaze: Uncovering ChatGPT’s Hidden Biases, Liz explores how researchers used forced-choice comparisons to reveal deep-seated stereotypes within AI models. By bypassing standard safety filters, the study demonstrates how millions of automated responses reflect geographic and demographic prejudices.

Key Takeaways:

Researchers used a forced choice method to extract millions of subjective rankings, revealing that ChatGPT consistently mirrors internet tropes regarding cleanliness, friendliness, and intelligence across different locations.

The episode highlights that the model’s training data links racial and economic demographics to negative attributes, such as ranking states with higher Black populations lower on work ethic and beauty.

The silicon gaze creates a facade of neutrality that can subtly influence users' perceptions of career paths and neighborhoods, making these hidden biases difficult for the average user to challenge.

Liz’s Two Cents: The perpetuation of quiet biases in AI data is deeply concerning as these models become integrated into everyday tasks. For school leaders, this reinforces the urgent need for professional learning and student-facing curriculum that focuses on identifying and questioning the inherent prejudices embedded in the technology we often treat as neutral.

Article Link:

https://geoffreyfowler.substack.com/p/chatgpt-bias

Sponsored by: Eduaide Eduaide.ai where good ideas become great lessons. Take advantage of our special offer: 50 percent off at eduaide.ai