Working Paper · AI Pedagogy · Practitioner Framework

AI Project Canvas

From AI literacy and fluency to AI pedagogy: a practitioner-designed planning tool for thoughtful, ethical, and pedagogically responsible artificial intelligence use in education.

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Overview

Moving from AI adoption to AI justification

The AI Project Canvas is a one-page planning artifact designed to help educators make visible the pedagogical, ethical, and practical reasoning that should accompany the use of artificial intelligence in education. Rather than beginning with a tool, prompt, or platform, the canvas begins with the educational project itself: what is being attempted, why it matters, who is involved, and what kind of learning or work is intended.

The central argument behind the canvas is that AI literacy and AI fluency are necessary but insufficient for meaningful AI integration. Educators may understand AI systems and interact with them effectively, yet still lack a clear pedagogical process for deciding whether AI should be used, what role it should play, what risks it introduces, and how its influence on learning should be evaluated.

The AI Project Canvas responds to this design problem by helping educators and learners move from asking, “How can I use AI?” toward the more pedagogically important question: “Why should AI be used here, and what educational purpose does it serve?”

Conceptual distinction

Literacy, fluency, and pedagogy

The AI Project Canvas is grounded in a distinction between understanding AI, interacting with AI, and designing meaningful learning with AI.

AI Literacy

AI literacy refers to the knowledge needed to engage with AI systems in informed and responsible ways. It includes understanding AI capabilities, limitations, risks, bias, opacity, privacy, authorship, fairness, and accountability.

AI Fluency

AI fluency moves from awareness toward skillful interaction. It includes prompting, refining, evaluating, comparing outputs, adapting responses, and using AI systems through an iterative process of human judgment.

AI Pedagogy

AI pedagogy asks why, when, and how AI should support learning. It connects AI use to educational purpose, human agency, ethical judgment, assessment, reflection, and responsibility for meaning-making.

Problem of practice

Educators need a planning structure, not only AI encouragement

As generative AI becomes more available across educational contexts, educators are often encouraged to experiment with AI tools before they have developed a clear pedagogical rationale for using them. This can make AI integration reactive, tool-driven, or performative.

The issue is not simply whether educators can access AI tools or learn to operate them. The more important issue is whether they have a structured way to determine why AI should be used in a particular learning context, what human responsibilities must remain visible, and how the effects of AI use on learning should be evaluated.

The canvas was designed to consolidate these decisions into a compact planning process that can be used in faculty development, student projects, assignment design, course planning, and institutional conversations about AI.

Canvas structure

What the AI Project Canvas asks users to consider

The canvas gathers the major decisions involved in AI-supported learning into a single planning space.

Project overview

Defines the project, its purpose, the learning or work context, and the people involved or affected. This anchors AI use in educational purpose before any tool decision is made.

AI use justification

Asks why AI is appropriate for the project and what value the human–AI partnership adds. This helps prevent AI from being used merely because it is available, novel, or efficient.

Desired outcomes

Identifies what will be created, discovered, demonstrated, improved, or learned. This helps distinguish between task completion and evidence of meaningful learning.

Human–AI collaboration

Clarifies what the human contributes and what AI contributes. This makes responsibility, authorship, judgment, interpretation, and decision-making visible.

Risks and mitigations

Identifies potential harms or limitations, including inaccuracy, bias, privacy, overreliance, inequitable access, plagiarism, and the outsourcing of intellectual work.

Ethical compass

Prompts reflection on whether the planned AI use is responsible, transparent, respectful, creative, fair, and accountable to the learning community.

Metrics and learning reflections

Encourages evaluation of both process and product. Users consider how understanding, revision, judgment, transparency, and growth will be made visible.

AI mindset plan

Asks what is gained, lost, practiced, outsourced, and newly accessed through AI use. This helps users examine the trade-offs of AI-supported work.

Design principles

Principles embedded in the canvas

Begin with educational purpose rather than tool functionality.

AI-supported design should start with what learners are expected to understand, create, practice, evaluate, or demonstrate.

Justify AI use rather than assuming it.

The canvas asks users to explain why AI is appropriate and how it supports learning rather than simply accelerating production.

Make the human–AI distribution of responsibility explicit.

Learners and educators should be able to explain what remained human-led, what was AI-supported, and where judgment occurred.

Integrate ethics and risk from the beginning.

Privacy, bias, authorship, access, transparency, and verification should be treated as design considerations rather than afterthoughts.

Evaluate both process and product.

In AI-supported learning, polished final outputs may not be sufficient evidence of student understanding, judgment, or growth.

Reflect on how AI changes learning and work.

AI may increase speed, access, and variation, but it may also reduce productive struggle, obscure authorship, or encourage overdependence.

Use cases

Where the canvas can be used

The AI Project Canvas can support multiple educational contexts. In faculty development, it can structure workshops where educators redesign assignments, examine AI-supported learning activities, and clarify the pedagogical rationale for AI use.

In course and assignment design, the canvas can help instructors determine what forms of AI use are permitted, required, limited, or prohibited. It can also be used as a student-facing planning document to support transparency and reflection.

At the institutional level, the canvas can function as a shared artifact for conversations about innovation, academic integrity, assessment, access, policy, risk, and educational responsibility.

Research status

Limitations and future research

The AI Project Canvas is currently presented as a practitioner-designed planning artifact, not as an empirically validated instrument. Its contribution is the articulation of a design response to a pedagogical problem: educators need a compact structure for connecting AI use to purpose, responsibility, ethics, outcomes, and reflection.

Future research could examine how educators use the canvas in faculty development, how it shapes assignment redesign, whether it improves the quality of AI-use justification, and how students interpret its prompts when planning AI-supported projects.

Design-based research may be especially appropriate because the canvas can be implemented, studied, revised, and adapted across multiple educational contexts.

Collaboration

Interested in using or discussing the AI Project Canvas?

I welcome conversations about faculty development, student-facing AI planning, institutional AI guidance, assignment redesign, and research collaboration connected to AI pedagogy.

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