Academic Integrity

We can ensure academic integrity not by insisting that AI work with teaching as we've always known it, but by devising new ways of teaching for a world with AI.

Example: We can shift from traditional reading to write to written to reading: Students use AI "deep research" mode to generate a paper. They then disassemble and reconstruct the paper as they read and discuss the resources that it cites and references. This process requires students to evaluate sources, synthesize information, and develop original insights. Ultimately, whether individually or collaboratively, students present a new iteration of the paper that reflects their additions, omissions and redirections, demonstrating authentic engagement with content and critical analysis skills.

Thought Checking

We can develop students' critical thinking not by limiting ourselves to warning them against AI errors, but by establishing practices for evaluating the consistency and validity of both their own reasoning and AI-generated responses.

Example: We can move from narrow questioning of outputs to interrogating interactions: Students maintain ongoing reflection protocols where they regularly pause to ask "How is my thinking changing through this conversation?" and "What assumptions is the AI making that I should question?" They document and discuss moments when AI challenges their perspectives productively, when it reinforces biases they hold, and when its reasoning seems flawed or incomplete. Such metacognitive awareness strengthens critical thinking about information sources and reasoning processes.

Project-Based Learning

We can enhance project-based learning not by limiting AI's role to research assistance, but by integrating it as a collaborative partner that enables students to tackle more complex, authentic challenges.

Example: We can move from projects about the world to projects with the world: Students identify a real community problem, then work with AI to model potential solutions, simulate outcomes, and engage with multiple stakeholder perspectives. AI serves as thinking partner, devil's advocate, and expert consultant as students iterate through design cycles. The result is deeper engagement with authentic complexity and more sophisticated problem-solving skills that prepare students for collaborative work in their future careers and civic engagement.

Oral Literacy ("Oracy")

We can strengthen students' oral literacy not by treating speaking and listening as secondary to writing, but by recognizing that in an AI-enhanced world, the distinctly human capacity for dialogue becomes increasingly valuable. "Oracy"—a chiefly British term—emphasizes the balance of speaking, listening, and thinking about both.

Example: We can move from writing to demonstrate thinking to speaking to compose thinking: Students develop a new rhetoric for the AI age where public speaking becomes the primary product, with writing serving as aide to oral composition. They practice composing their thoughts aloud through AI-guided conversations that help them structure arguments, explore counterpoints, and refine their voice. This oral composing process develops real-time critical thinking and communication skills that remain fundamentally human.

Multimodality

We can expand students' communicative capabilities not by teaching discrete skills in isolation, but by helping them understand how different modes of expression—visual, audio, textual, oratorical, dramatic/gestural, etc.—work together to create meaning in our interlayered multimedia landscape.

Example: We can move from single and dual-mode assignments to integrated multimodal products: Students create projects where they write to enhance their speaking, game to deepen their reading, and listen/view to strengthen their writing. AI serves as both production assistant and design coach, helping students understand how different modes work together. Students learn to select, mix and remix modes, developing the multi-literacies needed to communicate effectively across diverse platforms and contexts in our multimedia world.

Assessment

We can create more meaningful assessment by developing ongoing, performative approaches that capture authentic learning as it unfolds over time in AI-enhanced environments.

Example: We can move from snapshots of knowledge to demonstrations of thinking-in-development: Students maintain portfolios of their evolving work, documenting not just final products but reasoning processes, revisions, and breakthroughs occurring during their learning. They regularly present and defend their thinking through discussions, peer reviews, presentations and exhibitions, showing how their understanding is deepening and changing. AI supports this performative assessment by helping students reflect on their growth patterns and identify areas for continued development, building lifelong learning skills.