Beyond the Bot: Rebooting Curiosity and Critical Thinking in the AI Classroom

Outline

0–5 min | Welcome & Connection

Quick live poll or show of hands: “How do you feel about generative AI in the classroom?” (Excited, Curious, Wary, Avoiding).

Audience engagement: participants share one word in the chat or aloud.

Establish norms for respectful dialogue and curiosity across perspectives.

5–15 min | Framing the Challenge

Present the tension: “When AI can do the thinking, how do we keep students curious?”

Interactive discussion: attendees briefly share examples of AI use or hesitations from their own settings.

Introduce the CARE Framework (Clarity, Accuracy, Relevance, Ethics) through quick participant predictions (“What do you think each letter stands for?”).

15–30 min | Classroom Examples & Guided Analysis

Walk through two classroom examples (AI Chemistry Detectives and Operation Mole Fallout).

Small-group analysis: participants review excerpts of student–AI dialogue (via shared slide or handout).

Prompt: “What thinking did the AI support? Where did students rely too heavily?”

Device-based activity: participants test one provided AI prompt and evaluate the response using CARE.

30–45 min | Collaboration & Lesson Design Sprint

Collaborative task: in pairs or small groups, attendees choose a content area (science, ELA, or social studies).

They use a shared lesson template to design one short “AI thinking partner” moment.

Peer-to-peer feedback: groups swap ideas and discuss how they’ll teach students to question AI responses.

45–55 min | Reflection & Discussion

Whole-group reflection: facilitated discussion—“How can we foster curiosity instead of shortcuts?”

Participants write one action step they’ll take to implement CARE or reflective prompting in their classrooms.

Volunteers share aloud or in chat for collective idea-building.

55–60 min | Closing & Takeaways

Recap: “Three ways to keep humans at the center of AI learning.”

Provide digital resource link (CARE checklist, templates, reflection stems).

Exit reflection prompt: “One way I’ll help students think with AI, not through it…”

Outcomes

Design inquiry-based lessons that integrate generative AI as a thinking partner to support student reasoning and reflection.

Apply the CARE framework (Clarity, Accuracy, Relevance, Ethics) to teach students how to evaluate and ethically use AI-generated information.

Facilitate classroom discussions and reflections that help students remain active, critical thinkers rather than passive AI users.

Adapt provided templates, prompts, and reflection stems to create their own AI-supported learning activities across subjects and grade levels.

Supporting research

Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570.
DOI: https://doi.org/10.1111/ejed.12533
Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL Institute of Education Press.
UNESCO. (2023). AI and Education: Guidance for Policy Makers and Practitioners. UNESCO Publishing.
https://unesdoc.unesco.org/ark:/48223/pf0000376709
Dede, C., & Richards, J. (2020). The 60-Year Curriculum: New Models for Lifelong Learning in the Digital Economy. Routledge.
Stanford Graduate School of Education. (2024). “Teaching AI Literacy: Helping Students Think Critically About AI.”
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
Crompton, H., & Burke, D. (2024). “Artificial Intelligence in Education: Implications for Teaching and Learning.” Computers & Education: Artificial Intelligence.
Keene, B. (2024). AI Optimism: A Guide to Redefining Artificial Intelligence in Education. DBC Books.