🎨 AI, Authenticity & Creativity: Educator Reflections

Intentionality is key: Educators believe students will use these tools anyway. Schools must intentionally teach their responsible and effective use.
Fit-for-purpose use: The tool should match the learning goal—e.g., AI is useful for ideation and discussion but should not replace skill-building exercises like drawing or writing when foundational literacy or artistic techniques are the goal.
Creativity is redefined:
- AI is seen as a way to “lift the floor” for students with lower technical skills (e.g., drawing) by enabling them to express ideas.
- However, it risks “capping the ceiling” for highly creative individuals if they rely too much on AI and don’t develop deeper skills.

Prompts as new literacy: The skill of crafting effective prompts is being recognized as a form of creative and cognitive work.
Artistic direction vs. execution: There was discussion over whether prompting AI to generate art constitutes real creativity—comparing it to directing rather than drawing.
Modern art parallels: AI use in creativity echoes debates in modern art about authorship, originality, and collaboration.

Tiered skill development: Teach traditional skills first (e.g., sketching) before introducing AI tools for higher-order tasks.
“A + 1” rule: Students should not use AI outputs wholesale—they must add value or modify them meaningfully.
Credit and attribution: Students should be taught to acknowledge AI-generated content sources, similar to citing in essays.

Educators questioned whether students can feel genuine pride and ownership over AI-generated work.
The group noted that authentic emotional connection often stems from manual effort and the creative process, which may be lost when AI is overused.

Core skill: discernment: Students must be taught how to:
- Check sources and provenance.
- Evaluate whether content is genuine or AI-generated.
- Understand AI’s limitations and biases (e.g., hallucinations).
Cross-comparison: Encouraged as a method to verify facts—comparing across multiple sources before accepting something as truth.

Authenticity risks: Deepfakes, altered speeches, and AI-generated “historical” content can easily deceive students (and adults), especially if used persuasively.
Bias reinforcement: AI systems may mix and match real content out of context, altering meaning and manipulating perception.

Many educators admitted:
- Critical media literacy is not explicitly taught across all subjects.
- Lessons often only touch on source evaluation (e.g., in Social Studies or Cyber Wellness) but don’t go deep enough for real-world application.
Proposal: Integrate more explicit cross-subject instruction on media literacy and AI discernment, not just relegate it to one-off lessons.

Explain the why: Teachers should explain why AI tools are or aren’t permitted in assignments—clarifying learning goals and expected student input.
Teach ethical use: Include ethical dimensions (plagiarism, intellectual property, sincerity) in lessons on AI use.
Model critical questioning: Encourage students to ask, “Where did this come from? Is it trustworthy? What’s the context?”

Mental effort: Critical verification of AI content is cognitively taxing and students may prefer quick acceptance over deep evaluation.
Educators must train habits of mind early (e.g., “pause and verify” heuristics).

Redefining creativity: AI challenges traditional notions of creativity, raising the question—Is creativity in the idea or in the execution?
Tools vs. human uniqueness: While AI excels at blending existing ideas, true human novelty and emotion are still irreplaceable.
Process over product: The group emphasized the importance of valuing learning processes, iterative thinking, and emotional investment—beyond polished outcomes.
Ethical fluidity: The boundaries of plagiarism, copyright, and authenticity are becoming blurrier. Guidance and reflection are essential.

Practice	Purpose
Teach prompt engineering	Build creativity and clarity in AI communication
Require A+1 modifications	Promote originality and deeper learning
Include attribution norms	Reinforce ethical scholarship
Use peer feedback cycles	Support reflective refinement
Model media analysis	Build habits of questioning and verification
Integrate AI literacy across subjects	Embed critical thinking systematically