CHAPTERS
- 0:00 – 0:09
Students’ first impressions: excitement, uncertainty, and ‘is it good or bad?’
A group of students reacts to AI as something new and still hard to evaluate. They express uncertainty about its overall impact and what it means for learning.
- •AI feels too new to fully understand
- •Unclear whether AI is ultimately beneficial or harmful
- •Early reactions are exploratory rather than settled opinions
- 0:09 – 0:45
Schools in denial: cheating fears and the ‘everyone knows it’s used’ reality
Students describe a tension in schools where some teachers see AI as undermining education and enabling cheating. Others quietly ignore it, creating an unspoken agreement where AI use is widespread but not openly addressed.
- •Teacher concerns center on cheating and academic integrity
- •Some educators ‘look the other way’ despite knowing AI is being used
- •A culture of hiding/denial forms around AI use
- •AI is present in classrooms even when policy and practice lag behind
- 0:45 – 1:11
A ‘tsunami of change’: educators and students adapting together
The conversation reframes AI as a broader shift affecting everyone at once. The idea is that both educators and students are navigating the same disruptive transition and must invent new approaches.
- •Change is described as rapid and unavoidable
- •Teachers and students are simultaneously learning how to adapt
- •The need for new methods replaces attempts to revert to old norms
- 1:11 – 1:39
Launching a pilot: teachers stepping into ‘project manager’ roles
Two longtime colleagues describe being invited to help run a pilot program. Their collaboration and openness to experimentation sets the tone for structured, school-led exploration of AI.
- •Invitation to participate in a pilot program
- •Teachers take on leadership/project management responsibilities
- •Collaboration is a key motivator and working style
- •Longstanding co-teaching relationship supports change efforts
- 1:39 – 2:05
Teachers become learners: using AI to create, but redesigning lessons takes effort
One teacher explains that adopting AI flips the usual dynamic—educators become students learning new tools. While AI enables creative outputs like building games, it doesn’t automatically save time because courses and materials must be reworked.
- •Teachers must learn AI tools even without programming backgrounds
- •AI can enable new creative artifacts (e.g., games)
- •Adoption requires significant instructional redesign
- •Time savings aren’t immediate; effort shifts to transformation work
- 2:05 – 2:34
AI as study support vs. the irreplaceable role-model teacher
A student describes AI as helpful for understanding coursework, but argues a teacher’s human role—motivating and inspiring—is not something AI can replicate. The value of seeing a real person to emulate is emphasized.
- •AI helps explain concepts and support studying
- •Teachers inspire in ways AI cannot
- •Role-modeling and human presence are core to education
- •AI is framed as assistance, not a substitute for mentorship
- 2:34 – 3:42
Vocational education: pragmatic adoption to work faster and produce quality materials
A vocational-school perspective highlights AI’s usefulness for speeding up work and improving materials. In hands-on fields, AI is treated as a tool to increase productivity—especially for drawings and interactive learning content.
- •Vocational school spans many trades (cars, construction, electrical, nature, hairdressing)
- •A ‘work smarter’ mindset drives AI uptake
- •AI helps produce quality instructional materials more quickly
- •Practical departments report heavier AI usage than others
- 3:42 – 4:12
The ‘AI slop’ critique: easy shortcuts, lost learning, and the threat to creativity
A critical view argues many people use AI to avoid learning and generate low-quality output. The concern is that over-reliance can reduce originality and undermine the “most human” aspects of creative effort.
- •AI is often used as an ‘easy way out’ on projects
- •Overuse can reduce learning and skill-building
- •Concerns about uncreative, low-quality generated content (‘slop’)
- •Creativity is framed as a core human value worth protecting
- 4:12 – 4:47
A better model: treat AI like a tutor, not an answer machine
The discussion turns toward norms for responsible use: AI should help you get started, find sources, and learn—rather than delivering complete answers. This frames AI as guidance that preserves student agency and effort.
- •Use AI for scaffolding: brainstorming, starting points, explanations
- •Leverage AI to locate sources and helpful references
- •Avoid end-to-end answer generation for tests/assignments
- •Maintain student responsibility for thinking and producing work
- 4:47 – 5:50
Public anxieties and family boundaries: Terminator fears and parenting challenges
Personal reflections show a spectrum: some fear AI takeover narratives, others don’t think about it. A parent describes the real-world challenge of setting boundaries when teenagers default to AI before reading assigned materials.
- •Some people are suspicious and influenced by dystopian narratives
- •Others are disengaged or unconcerned
- •Parents notice teens skipping learning steps to go straight to AI
- •Boundary-setting becomes part of digital-age parenting
- 5:50 – 6:47
From fear to possibility: returning to school to learn AI, and the need for guidance
One speaker recounts initially believing AI would ‘ruin everything,’ then changing their mind after receiving education on how to use it. The takeaway is optimism paired with a strong need for clear guidance and literacy.
- •Initial fear: loss of independent thinking
- •Education/training reveals practical possibilities
- •Guidance and norms are necessary for healthy adoption
- •Learning to use AI can shift attitudes from panic to pragmatism
- 6:47 – 8:10
Icelandic optimism and open-ended outcomes: ‘We’ll figure it out’
The closing reflections emphasize uncertainty about the future, but also confidence in human adaptability—especially in Iceland’s context of resilience and optimism. The group lands on a pragmatic stance: keep taking steps and adjust as the future unfolds.
- •No one claims to know exactly how AI will shape the future
- •Teachers feel they’re taking reasonable steps, but outcomes remain unknown
- •Cultural framing: optimism and survival instincts in a harsh environment
- •Conclusion: experimentation and adaptation—‘we’ll figure it out’
