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An assignment remains meaningful when its evidence matches the learning outcome. Start by deciding what students must be able to do, then ask for a small, purposeful record of the decisions that demonstrate that ability: a plan, source evaluation, draft, feedback response, revision, or explanation. State exactly whether generative AI is prohibited, limited, permitted with disclosure, or required.
This approach does not try to make an assignment “AI-proof.” It makes the work authentic by assessing disciplinary judgment and development rather than treating a polished final paragraph as the only evidence of learning.
Why process-centered assessment matters
A final answer can hide how a student reached it. Two submissions may look similar while representing very different levels of understanding: one may reflect careful evidence selection and revision, while the other may be copied from an unverified generated response. Conversely, a weak final product can conceal valuable reasoning that needs targeted feedback.
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Process evidence solves that mismatch only when it serves the course outcome. Requiring every student to submit every note, prompt, draft, and message creates paperwork, not proof. The useful question is: Which observable decisions would convince a reasonable reader that the student has practiced and can perform the intended skill?
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TU Dublin’s assessment guidance captures the principle: “Authenticity can be supported by the design of the task and assessment process, rather than primarily through surveillance or restriction.” A well-designed task can remain valid even when students have access to AI, while still allowing an instructor to set boundaries where independent performance is the goal.
Start with the learning outcome, not with AI detection
Write an observable outcome
Replace a vague aim such as “understand climate policy” with an action and a standard. For example: “Evaluate two policy options using the course’s cost-benefit framework, identify the assumptions that drive the result, and defend a recommendation for a specified audience.” The verbs—evaluate, identify, defend—tell you what evidence to collect and grade.
Ask three questions before choosing a format:
- What knowledge, method, or judgment should a successful student demonstrate?
- What would a competent performance look like in this discipline?
- Which parts must the student do personally, and which tools may support the work?
The assignment purpose should be visible to students. Boston University guidance recommends connecting the task to a course learning goal and to a possible future use. A brief explanation such as “You will practice making a defensible recommendation from incomplete evidence, a skill used in policy and consulting work” helps students understand why the stages exist.
Define evidence before choosing a format
If the outcome is source evaluation, collect an annotated source set and a rationale for inclusion. If it is oral argument, a live defense or recorded explanation may be more direct than a long essay. If it is technical implementation, a versioned prototype, test log, and explanation of trade-offs may matter more than prose. The final artifact is one piece of evidence, not automatically the best one.
Choose the assignment’s relationship with AI
Decide the permitted relationship before writing the prompt. TU Dublin describes four useful approaches; a course can use more than one across its assessments.
| Approach | What it means | Good fit | Evidence to request |
|---|---|---|---|
| Robust | The task is designed to work when AI is unavailable. | Foundational fluency or an independently performed skill that students must demonstrate directly. | In-class or otherwise controlled performance, plus an explanation of method where appropriate. |
| Resilient | The task remains valid even though students may have AI access. | Work requiring contextual judgment, personal observation, local data, or an evolving argument. | Context-specific evidence, source decisions, drafts, feedback response, and a concise rationale. |
| Integrated | AI is allowed at defined stages and becomes part of the workflow. | Assignments that teach verification, editing, prompt critique, or responsible tool use. | AI output, checking record, accepted and rejected suggestions, and the student’s revised work. |
| Enabled | AI makes a new kind of task possible and is central to the learning design. | Comparing model outputs, testing limitations, or designing a human-AI process. | Task configuration, outputs, evaluation criteria, and reflection on human decisions. |
Do not choose the “robust” category merely to make cheating harder. If the outcome is collaborative design or critical evaluation of generated text, forbidding AI would remove part of the intended practice. Conversely, if the outcome is unaided calculation, allowing a generator to perform that calculation defeats the assessment.
Write a student-facing brief that removes ambiguity
Students should not have to infer the rules from a syllabus, a general institutional statement, or what classmates say. Put the following elements in the assignment itself.
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| Element | What to specify |
|---|---|
| Task and audience | The product, its intended reader or viewer, and the situation it addresses. |
| Purpose | The course outcome and why the task is useful beyond the exercise. |
| Success criteria | The qualities that distinguish strong work, expressed in discipline-specific language. |
| AI boundaries | What is prohibited, allowed, required, or allowed only at named stages. |
| Disclosure | What students must record about tools, prompts or inputs, output use, checking, and changes. |
| Evidence package | Exactly which plans, sources, drafts, feedback responses, or demonstrations are submitted. |
| Dates and format | Checkpoint deadlines, file types, length or time limits, and how feedback will be provided. |
The University of Michigan recommends explaining the assignment’s purpose and its relationship to knowledge, skills, and values. A short “why this matters” paragraph often prevents students from treating checkpoints as bureaucratic hurdles.
Four plain-language AI policies
- Prohibited: “Do not use generative AI to create, rewrite, translate, solve, or evaluate any part of this submission. You may use the spelling and accessibility tools listed in the course policy.”
- Limited: “You may use AI to brainstorm questions and to identify possible counterarguments. Do not submit generated wording or unverified claims. Include your prompt, the output you considered, and what you changed.”
- Allowed with disclosure: “You may use AI during planning, drafting, or editing. You remain responsible for accuracy and citations. Attach a short use statement describing the tool, purpose, material used, checks performed, and substantial revisions.”
- Required: “Use the specified tool to produce an initial response, then test its claims against course sources and revise it. Your grade is based on the quality of verification and judgment, not on agreement with the initial output.”
Tell students what they must do without AI as well. “Use AI responsibly” is too vague to grade; “verify every factual claim against two course-approved sources and explain one rejected suggestion” is observable.
Add a small number of purposeful checkpoints
A checkpoint earns its place when it gives students practice or gives you evidence tied to the outcome. Boston University recommends process steps and deadlines; Berkeley guidance suggests a sequence such as brainstorming, outlining, drafting, peer feedback, and reflection. Select only the stages that answer a real instructional question.
| Checkpoint | Use it when you need to see | Student submission |
|---|---|---|
| Question or proposal | Whether the problem is focused and feasible. | A working question, audience, intended claim, and brief plan. |
| Source set or research log | How evidence is found, evaluated, and selected. | Annotated sources, search decisions, or a log of evidence that changed the plan. |
| Outline or prototype | Whether reasoning or technical structure is developing before polish. | Headings with claim-and-evidence notes, a wireframe, or an early build. |
| Draft or first output | Where misconceptions and weak assumptions appear. | An identifiable version, including AI output if the assignment integrates AI. |
| Feedback response | Whether the student can interpret and act on critique. | Two or three consequential changes, with reasons for accepting or rejecting feedback. |
| Revision rationale | Whether the student can explain disciplinary choices. | A concise note linking changes to evidence, criteria, or audience needs. |
Do not require a checkpoint simply to increase the number of submissions. A one-page log that records a decision and its consequence can be more informative than a ten-page diary. Decide in advance which artifacts are graded, which are completion-only, and which are used for feedback.
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Assignment patterns that make thinking visible
Scaffolded writing or project
Students submit a question or plan, a small set of evaluated sources or evidence, a draft, a response to feedback, and a final version with a concise revision rationale. This pattern works for essays, design proposals, lab reports, and software projects. Keep the rationale focused on consequential changes rather than a chronological account of every keystroke.
AI-output critique
Give students an AI-generated answer or require them to produce one. They identify unsupported claims, missing context, weak sources, and reasoning errors; then they create a corrected answer. Grade the checking method and justification. The initial output is not evidence of mastery by itself—the student’s diagnosis and repair are.
Contextual case task
Ask students to apply course ideas to a specific case, local situation, dataset, object, or experience. Require them to explain which contextual facts changed their decision. A generic response can be generated easily; a defensible judgment tied to supplied details is harder to fake and more closely aligned with applied learning.
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Process portfolio
Students select artifacts such as outlines, notes, readings, drafts, feedback, and tests, then explain what each reveals about a choice or learning change. The portfolio can accompany the final product or receive a smaller share of the grade. Provide a maximum length and a selection rule so students are not rewarded for volume.
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Oral or multimodal demonstration
Debates, proposals, role plays, podcasts, posters, and short films can elicit communication, synthesis, and audience awareness that a generic written response may miss. Choose the mode because it matches the outcome, not because it is presumed to deter AI. Supply a script, rehearsal opportunity, captions, alternative formats, and an equivalent accessible route where needed.
Research-process reflection
Ask students to describe how they evaluated sources, how evidence changed their thinking, and what problem they encountered and solved. This is especially useful when the final product is collaborative or polished enough that individual reasoning would otherwise be hard to see.
Build a rubric around consequential thinking
Publish the rubric before students begin. Weight criteria according to the outcome, not according to the amount of documentation produced.
| Criterion | What strong evidence looks like | What not to reward |
|---|---|---|
| Reasoning and evidence | Claims follow from relevant, checked evidence; assumptions are identified. | Length, citation count, or confident tone without support. |
| Disciplinary method | Course concepts, procedures, or conventions are applied appropriately to the case. | Generic terminology pasted into an unrelated answer. |
| Contextual judgment | The student adapts the method to the audience, constraints, and particulars of the task. | One-size-fits-all recommendations. |
| Response to feedback | Feedback leads to specific, justified improvements or a reasoned decision not to change something. | Cosmetic edits made only to show activity. |
| Final communication | The finished work is accurate, coherent, and fit for its stated audience and format. | Polish treated as a substitute for understanding. |
| AI and source transparency | Required disclosures are complete, and generated or borrowed material is checked and attributed as required. | A disclosure box that earns points regardless of the quality of decisions. |
Make the scoring language concrete. “Excellent critical thinking” is difficult to apply consistently; “compares two plausible explanations, tests each against the case evidence, and states what would change the conclusion” gives both students and markers a usable standard.
Make AI disclosure useful rather than punitive
When AI is permitted or required, ask students to document contribution in a compact, consistent format. A useful statement answers:
- Which tool or feature was used and at what stage?
- What did the student ask it to do?
- Which output was accepted, rejected, checked, or substantially changed?
- How were factual, mathematical, code, citation, or privacy risks checked?
- Which final decisions belong to the student?
Provide a field such as: Tool and stage: …; purpose: …; material used: …; checks performed: …; changes made: …. If a full prompt history is educationally important, request selected excerpts rather than an unmanageable export. Students should never be penalized for rejecting an AI suggestion when the rejection is well justified.
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Align these instructions with institutional policy. Cardiff University advises staff not to upload student submissions or identifiable student work to external generative-AI tools and states that academic staff remain responsible for grades, standards, and feedback decisions. That is Cardiff-specific guidance; instructors should check their own institution’s privacy, data-protection, and assessment rules before naming a tool or asking students to upload material.
Check access, accommodations, and workload
Process assessment can improve fairness, but some formats create new barriers. Timed oral work, handwritten tasks, compulsory live defenses, and frequent synchronous deadlines may disadvantage students with disabilities, anxiety, caring responsibilities, limited connectivity, or different language needs. Berkeley guidance flags disadvantages that can result from timed oral and handwritten assessments, and King’s guidance cautions that oral assessment can cause anxiety.
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- Does this measure the intended skill or merely speed, confidence, handwriting, or familiarity with a particular device?
- Is there an equivalent accessible way to demonstrate the same outcome?
- Can students receive the necessary preparation, captions, assistive technology, extra time, or alternative response mode?
- Can the instructor provide meaningful feedback and grade the artifact within the available workload?
Use a small set of high-value checkpoints, shared templates, and completion-based review where detailed scoring is unnecessary. An ambitious evidence system that cannot be read or returned in time undermines learning.
A practical design sequence
- Name the outcome and purpose. Write the observable capability, the audience, and the reason the task matters.
- Choose the AI relationship. Decide whether the task is robust, resilient, integrated, or enabled, and state the choice in student-facing language.
- Draft the brief. Include task, audience, purpose, success criteria, boundaries, disclosure, evidence, format, and dates.
- Select checkpoints. Add only the proposal, evidence record, draft, feedback response, demonstration, or reflection that produces useful evidence for the outcome.
- Build and publish the rubric. Grade reasoning, evidence, method, judgment, response to feedback, final communication, and transparency as appropriate.
- Check access and feasibility. Provide equivalent routes and confirm that the marking and privacy demands are sustainable.
- Review after use. Examine student work and feedback. Remove steps that produced little insight, clarify confusing instructions, and keep stages that improved decisions or learning.
Example: a process-centered policy brief
The following is an adaptable model, not a prescribed subject-specific solution.
Outcome
Students will evaluate competing policy options with a course framework, verify evidence, and defend a recommendation for a defined public audience.
Brief
“Prepare a 1,200-word brief for a city committee deciding between two options in the supplied case. State a recommendation, apply the course framework, identify assumptions, and address the strongest counterargument. You may use AI to brainstorm questions and test counterarguments, but you must verify every factual claim against the supplied sources and disclose use.”
Evidence package
- A 150-word question, audience statement, and initial plan.
- Three to five annotated sources, including one source the student rejects and why.
- A draft with claim-and-evidence labels.
- A peer or instructor feedback response identifying two consequential changes.
- The final brief and a 200-word revision rationale.
- An AI-use statement, if applicable, listing the tool’s role, checks, and changes.
Assessment emphasis
Most marks should follow the stated outcome: quality of evaluation, appropriate use of the framework, evidence and source judgment, and a defensible recommendation. A smaller share can recognize the feedback response and transparency statement. The number of pages in the process file should not determine the grade.
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Common failure modes and repairs
“Make it AI-proof” becomes the objective
Symptom: The task relies on surveillance, unusual formatting, or artificial restrictions rather than the course skill.
Repair: Return to the outcome and design evidence of judgment, application, or explanation. No assignment can guarantee that AI was never consulted.
Too many checkpoints
Symptom: Students submit repetitive logs and instructors spend more time administering than teaching.
Repair: Keep stages that change decisions or enable feedback; combine low-value records into one bounded artifact.
AI rules are hidden or contradictory
Symptom: The syllabus permits one use while the prompt implies another.
Repair: Put the allowed relationship and disclosure format in the brief, and align it with institutional policy.
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Symptom: Students earn points for long prompt transcripts or multiple drafts regardless of quality.
Repair: Grade the reasoning shown by the artifact: what was checked, changed, and justified.
Oral work is used as a universal authenticity test
Symptom: A live defense creates anxiety or access barriers unrelated to the outcome.
Repair: Scaffold preparation, offer equivalent accessible modes, and use oral evidence only when communication or defense is itself part of the outcome.
Privacy is treated as an afterthought
Symptom: Students are told to paste identifiable work or sensitive case information into an external tool.
Repair: Minimize data, use approved systems, state what may be uploaded, and provide a non-AI route where required.
How to improve the assignment after it runs
Review a sample of process packages alongside final work. Look for whether the checkpoints revealed misconceptions early, whether feedback led to substantive changes, and whether students understood the AI boundaries. Ask students which evidence helped them make a better decision and which felt performative. Compare the time required to read and respond with the learning value obtained.
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Further reading
For a book-length treatment, Johns Hopkins University Press lists Teaching with AI: A Practical Guide to a New Era of Human Learning, second edition, by José Antonio Bowen and C. Edward Watson. The paperback was published December 2, 2025 (ISBN 9781421453392), and includes a chapter titled “Designing Assignments and Assessments for Human Effort.”
The practical test is simple: can a student who understands the outcome explain the choices that produced the final work, and can your rubric recognize those choices without rewarding paperwork for its own sake? If yes, the assignment is assessing learning rather than merely policing tools.
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