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#07 - Educating with AI: Lucas Ames (GOA) on Curiosity, Cheating, and What Schools Must Do Next

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Manage episode 518317735 series 3692450
Kandungan disediakan oleh James Wagenheim. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh James Wagenheim atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

How does AI transform learning without killing curiosity? In this episode, I sit down with Lucas Ames of Global Online Academy (GOA) to unpack what actually works—and what doesn’t—when bringing AI into K-12 classrooms.

*Core themes*

Curiosity first: Tech should increase student curiosity. If tools switch off active thinking, you’ve gone too far.

From rote to higher-order: Like calculators, AI shifts what we assess—less memorization, more evidence, analysis, and synthesis.

Cheating isn’t the real problem: The goal isn’t “gotcha” detection; it’s ensuring students still learn the skills that matter.

Detectors and watermarking: Current AI detectors are unreliable; watermarking text isn’t a silver bullet.

Policies that breathe: One size won’t fit all. Department-level “green / yellow / red” guidance beats blanket bans. GOA de-identifies student data before AI use and avoids over-policing while the field evolves.

AP vs. IB signals: Early AI stances differed; the deeper story is how content-heavy vs. application-heavy models adapt.

Teachers need empathy—and time: Start AI as a teacher’s “intern” (draft emails, map curriculum, find gaps) to free time and improve practice.

Personalized tutoring that works: Tools like Khanmigo can level access when paired with a human teacher who sets the course and culture.

Wellness with care: Use early signals (attendance, notes, behavior) to start human conversations, not trigger automated punishment.

Micro-schools and iteration: Expect faster learning from smaller, experimental models that can adapt quickly.

Humans are the differentiator: Relationships—teacher↔student and student↔student—predict good experiences more than any tool. Teachers who adopt AI thoughtfully will outpace those who don’t.

Why watch:

If you’re a teacher, school leader, parent, or edtech builder, this conversation gives practical, classroom-level ways to use AI that protect curiosity, reduce busywork, and keep the human center intact.

Key topics:

AI in education, personalized learning, cheating and assessment, standardized testing, policy design, teacher workload, AI tutors, student wellness signals, micro-schools, GOA practices.

Hashtags:

#AIinEducation #EdTech #PersonalizedLearning #K12 #GlobalOnlineAcademy #llm #chatgpt

  continue reading

9 episod

Artwork
iconKongsi
 
Manage episode 518317735 series 3692450
Kandungan disediakan oleh James Wagenheim. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh James Wagenheim atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

How does AI transform learning without killing curiosity? In this episode, I sit down with Lucas Ames of Global Online Academy (GOA) to unpack what actually works—and what doesn’t—when bringing AI into K-12 classrooms.

*Core themes*

Curiosity first: Tech should increase student curiosity. If tools switch off active thinking, you’ve gone too far.

From rote to higher-order: Like calculators, AI shifts what we assess—less memorization, more evidence, analysis, and synthesis.

Cheating isn’t the real problem: The goal isn’t “gotcha” detection; it’s ensuring students still learn the skills that matter.

Detectors and watermarking: Current AI detectors are unreliable; watermarking text isn’t a silver bullet.

Policies that breathe: One size won’t fit all. Department-level “green / yellow / red” guidance beats blanket bans. GOA de-identifies student data before AI use and avoids over-policing while the field evolves.

AP vs. IB signals: Early AI stances differed; the deeper story is how content-heavy vs. application-heavy models adapt.

Teachers need empathy—and time: Start AI as a teacher’s “intern” (draft emails, map curriculum, find gaps) to free time and improve practice.

Personalized tutoring that works: Tools like Khanmigo can level access when paired with a human teacher who sets the course and culture.

Wellness with care: Use early signals (attendance, notes, behavior) to start human conversations, not trigger automated punishment.

Micro-schools and iteration: Expect faster learning from smaller, experimental models that can adapt quickly.

Humans are the differentiator: Relationships—teacher↔student and student↔student—predict good experiences more than any tool. Teachers who adopt AI thoughtfully will outpace those who don’t.

Why watch:

If you’re a teacher, school leader, parent, or edtech builder, this conversation gives practical, classroom-level ways to use AI that protect curiosity, reduce busywork, and keep the human center intact.

Key topics:

AI in education, personalized learning, cheating and assessment, standardized testing, policy design, teacher workload, AI tutors, student wellness signals, micro-schools, GOA practices.

Hashtags:

#AIinEducation #EdTech #PersonalizedLearning #K12 #GlobalOnlineAcademy #llm #chatgpt

  continue reading

9 episod

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