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AI has changed my teaching less by replacing lectures than by changing what I can reasonably count as evidence of learning. Fluent essays, working code, outlines, and explanations can now be produced in minutes, so I have had to redesign assignments, state acceptable uses clearly, and spend more time examining students’ reasoning. I still use human discussion, feedback, and judgment as the center of the course. The practical shift is from policing a finished product to making the learning process visible, teaching students to verify AI output, and choosing an AI policy that fits each learning objective.
What changed first when generative AI entered my courses?
Assessment changed first because it is where a tool’s speed most directly collides with a course’s purpose. If the objective is merely to produce grammatically correct paragraphs or routine code, an AI system can often create a convincing submission before a student has understood the material. That does not make every AI-assisted assignment invalid; it means the assignment must show the analysis, choices, and revisions that the course is meant to develop.
A 2024 study of 30 teaching staff found that almost half reported using AI in their teaching roles, with assessment modification the most common design change. Fewer than one quarter felt their university had adequately equipped them, while more than three quarters wanted support (Lee, Arnold, Srivastava, Plastow, Strelan, Ploeckl, Lekkas, and Palmer, 2024). Those figures describe that sample, not every institution, but they match what I see in faculty conversations: the immediate pressure is practical, while institutional guidance often arrives later.
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How do I redesign an assignment for generative AI?
I start with the learning objective, then decide what kind of evidence would demonstrate it even when a student has access to an AI tool. The following sequence keeps the redesign tied to pedagogy rather than to a particular product.
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- Name the capability being assessed. Write down whether students must recall information, interpret evidence, evaluate competing claims, create something original, perform a procedure, or explain a decision. A prohibition is easiest to justify when AI would perform the very capability being assessed.
- Test the old prompt yourself. Run several versions through an AI system and inspect where it gives a plausible but wrong answer, fabricates a citation, misses local context, or produces generic code. This reveals which parts of the prompt invite output substitution.
- Add process evidence. Require a proposal, source annotations, a draft with revision notes, a short oral explanation, a demonstration, or a reflection that connects choices to course concepts. Use only the pieces that illuminate the objective; extra paperwork is not automatically better assessment.
- Make the context harder to outsource. Ask students to apply a theory to a local case, a class experiment, a supervised observation, or a personal design constraint. Authentic context does not make AI use impossible, but it requires students to supply information and judgment the system does not possess.
- Specify and document AI use. Tell students whether they may brainstorm, translate, generate practice questions, edit prose, analyze data, or produce assessed text or code. Require a brief disclosure naming the tool, purpose, and material changes, and explain how they remain responsible for accuracy and attribution.
- Check alignment and workload. Before publishing the assignment, confirm that every required artifact will be read and graded. A ten-minute conversation or a one-page process memo may reveal more than a second long essay, while taking less grading time.
Evidence from 680 students and 87 educators at universities in Australia, Cyprus, and the United States found that educators favored assessments adapted for AI and critical thinking; essay and coding tasks were viewed as especially affected (Kizilcec and colleagues, 2024). A separate mixed-methods survey of 318 educators proposed changes focused on learning with AI, higher-order thinking, ethical values, learning processes, and face-to-face relational learning (Bower and colleagues, 2024).
Output-only and process-visible assessment
| Design | What the student submits | Strength | Risk or cost |
|---|---|---|---|
| Output-only | Final essay, answer set, or program | Efficient to assign and grade | Hard to distinguish understanding from generated work; weak evidence of revision and judgment |
| Process-visible | Final work plus drafts, source notes, decisions, or a brief explanation | Shows reasoning and makes feedback more actionable | Requires a clear rubric and time to review artifacts |
| Performance or conversation | Demonstration, oral defense, studio critique, or live troubleshooting | Tests transfer and personal understanding directly | Scheduling and accessibility need deliberate planning |
Should I prohibit ChatGPT or allow it with disclosure?
There is no responsible single rule for every course. I choose among prohibition, limited permission with disclosure, and deliberate incorporation according to the capability being assessed, the enforceability of the rule, student access, privacy, and the time I can devote to feedback.
| AI role | Use it when | Minimum safeguards | Trade-offs |
|---|---|---|---|
| Prohibited for a task | The task measures unaided recall, foundational practice, or an individual performance that AI would replace | State the boundary and acceptable alternatives; provide an equitable way to complete the work without a commercial account | Detection is uncertain, and a ban may be unrealistic outside supervised settings |
| Permitted with disclosure | AI is a support for planning, language access, debugging, or formative practice, while judgment remains the learning goal | Require a use statement, verification of claims and citations, and responsibility for the submitted work | Students need explicit instruction in what counts as meaningful assistance |
| Required and critiqued | AI literacy, evaluation, ethics, or human–computer collaboration is itself an objective | Provide a common tool or an offline alternative; grade the critique, comparison, and revisions rather than the raw output | Outputs can contain bias or fabricated facts and must be checked in class |
Jisc reports that institutions are updating guidance, in some cases permitting planning support while requiring disclosure and prohibiting AI-generated final text. I explain the rule in the assignment itself, not only in a syllabus paragraph: what is allowed, what is not, what must be disclosed, how to verify claims, and what happens when use is undisclosed. Students should not have to infer policy from an accusation after submission.
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AI literacy is a judgment skill, not simply the ability to write a clever prompt. Students need to recognize that a fluent response can be factually wrong, biased by its training data, unsafe to paste confidential information into, or inappropriate when the work is meant to build unaided expertise.
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I ask students to trace important claims to a reliable source, open the cited document, check whether the source actually supports the sentence, and mark uncertainty. Fabricated references are a useful teaching example because they expose the difference between polished language and evidence.
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Bias, privacy, and authorship
Students should know what data a tool may retain, why personal or confidential material should not be entered without authorization, and how generated wording can reproduce stereotypes or erase a community’s voice. A disclosure should identify the student’s contribution, not pretend that a tool is a co-author with human accountability.
Knowing when not to automate
Automation can remove productive struggle. I ask students to explain whether using AI helped them learn the target skill or merely shortened the task, and to identify situations—such as sensitive advising, high-stakes decisions, or an exercise designed for memory—where avoiding it protects the learning objective.
EDUCAUSE’s 2023 Horizon Report describes ethical and responsible interaction with generative AI as a higher-education responsibility. Its 2026 assessment report, informed by 438 faculty and staff, likewise identifies growing use alongside uncertainty and a need for clearer guidance and AI literacy for students, faculty, and staff.
Is AI making students think less critically?
It can, when students accept a plausible answer without checking it or outsource the first attempt before forming their own position. It can also create a demanding object of critique: students must compare outputs with evidence, identify omissions, test code, and defend revisions. The outcome depends on the task and the feedback, not on the mere presence of a chatbot.
A 2025 AAC&U/Elon survey reported strong faculty concern about overreliance and declining critical thinking. The sample was non-scientific and not generalizable to all college faculty, so its percentages should be read as the perceptions of respondents rather than a population estimate. A peer-reviewed study has found “a lack of any homogenous sentiment around AI in HE,” which is a useful warning against claiming that professors have reached one view.
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In practice, I require an independent first step before assistance on tasks intended to build reasoning. Students might write a provisional claim, solve a problem, or sketch an algorithm, then use AI to challenge or extend it. Their grade rests on the comparison and justified revision, not on the tool’s initial answer.
What should a professor do about suspected ChatGPT cheating?
I treat an AI detector score as a lead, never as proof. Detectors can produce false positives, and a generated-looking style is not a reliable basis for a high-stakes finding. I follow the institution’s academic-integrity procedure, preserve the submitted work and assignment instructions, and give the student a chance to explain their process.
- Check whether the submission conflicts with earlier drafts, in-class work, code history, or the student’s stated sources.
- Ask neutral, specific questions about a claim, calculation, design choice, or revision rather than demanding that the student “prove” authorship.
- Apply the same standard to every student and provide accommodations required by disability, language, or access policies.
- Document the evidence and refer the case through the published process instead of imposing an improvised penalty.
Prevention is more defensible than surveillance. Clear permissions, staged work, short explanations, and authentic applications reduce the value of submitting an unexamined generated answer while giving an honest student a fair way to demonstrate learning.
How has AI changed my day-to-day faculty workflow?
Preparing a course
I test the week’s prompts, examples, and rubric against likely AI outputs. I note where the tool is persuasive but wrong, then turn those failures into class exercises. I also review whether an assignment requires a paid account, exposes student data, or creates an access problem for students who cannot or should not use the tool.
Teaching in class
Short, face-to-face explanations matter more when a final paragraph can be generated instantly. I use discussion, whiteboard reasoning, live debugging, and peer critique to hear how students think. When we examine an AI response, students annotate claims, locate evidence, and rewrite the answer for a real audience.
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Giving feedback
Process artifacts let me comment on a misconception while it is still changeable. A focused note on a draft or a five-minute conference can be more educational than extensive correction of a finished text whose reasoning is invisible.
Working with colleagues
Shared exemplars prevent every professor from solving the same problem alone. Departments can review prompts, compare disclosure language, schedule calibration sessions, and update policies as tools and institutional rules change. This is also where accessibility, privacy, library support, and academic-integrity expertise belong.
Michael Webb, Jisc’s Director of AI, puts the immediate task plainly: “The immediate action is for all staff to engage with generative AI and try it themselves, learning how their assessments will be impacted.” I interpret that as structured experimentation, not a requirement to adopt a tool. Trying the tool reveals both the shortcuts students may encounter and the errors I need them to learn to detect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the unresolved equity and institutional problems?
Access is uneven: students may have different devices, paid tiers, language support, or comfort with disclosure. A policy that assumes one service is available to everyone can turn a learning exercise into an economic test. I offer a no-tool route when AI is optional, avoid requiring students to upload sensitive work, and explain how disability and language accommodations interact with the policy.
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Institutional readiness remains a constraint. The Lee et al. sample found a large gap between the number of staff wanting support and the number who felt equipped. EDUCAUSE’s 2026 findings similarly point to the need for guidance, examples, and time to experiment. Without those supports, inconsistent rules across courses can create confusion and make enforcement appear arbitrary.
What does responsible AI-enabled teaching look like now?
For me, it means using AI neither as a forbidden shortcut nor as an automatic improvement. I design for the capability I want students to leave with, make their reasoning visible, teach verification and privacy, and preserve the human conversations in which judgment develops. The evidence is recent and heterogeneous—different countries, disciplines, samples, and definitions of “use”—so I revisit policies and assignments as tools and campus guidance change.
The professor’s role has therefore expanded. I am still a subject expert and teacher, but I am also an assessment designer, an interpreter of uncertain evidence, and a guide to responsible tool use. AI has not removed the need for teaching; it has made the purpose of each task harder to hide and more important to explain.
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Frequently Asked Questions
Can students use AI to brainstorm ideas for a college paper?
They can when the course policy permits planning support, but brainstorming should not replace the student’s own claim, evidence search, or interpretation. A short disclosure naming the tool and describing what changed keeps the boundary clear.
Are AI detectors accurate enough to prove plagiarism?
No single detector result establishes authorship. Treat it as a prompt to review drafts, sources, revision history, and the student’s explanation under the institution’s established process.
How can I protect student privacy when using an AI tool in class?
Do not require students to paste identifiable, confidential, or unpublished material into a service without approved safeguards. Offer de-identified examples or a non-AI alternative, and explain retention and sharing expectations before any activity.
What is a fair AI policy for multilingual students?
Separate language assistance from outsourcing the intellectual work: you may permit translation or copyediting while requiring students to supply the analysis and disclose substantial assistance. Apply the same learning standard and provide approved accommodations rather than treating fluent English as proof of independent work.
How often should a course AI policy be updated?
Review it at least each term and whenever institutional guidance, privacy terms, or assessment conditions change. Keep a dated version in the syllabus and assignment pages so students know which rule governed their submission.
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