It devastates instructors, professors, and department heads who are on the front lines, trying to figure out how to adapt their teaching to a world with generative AI. If I see one more article promising a single silver-bullet policy, I will sigh. There is hope, though—and not because a new detection tool will save us. Real progress comes from comparing realistic options, reshaping learning goals, and protecting students' development without collapsing under additional labor.
3 Key Factors When Choosing How to Respond to Generative AI in Your Courses
What actually matters when you evaluate different strategies for adapting courses? Start with three interdependent factors.
- Learning outcomes and evidence - What do you want students to know or be able to do, and what kind of student work best demonstrates that? If the outcome is "compose an original argumentative essay," a private, timed exam may be appropriate. If it is "synthesize research into a project," then a draft-revision process and oral defense may be better. Faculty and institutional capacity - How much time can faculty spend redesigning assignments? Does your department have technical support, shared repositories, or teaching-and-learning staff? A brilliant redesign that is feasible for one instructor may be impossible to scale across a department. Equity and student access - Who benefits from a chosen approach? Bans and high-stakes proctoring can unfairly penalize students with limited quiet spaces, unstable internet, or disability accommodations. Meanwhile, permissive policies may advantage students with more familiarity or better access to AI tools.
Ask: Which of these three priorities are non-negotiable in my context, and where is there room to compromise?
Why strict bans and intensified proctoring often fail
Many departments default to traditional responses: tighten honor codes, ban AI in the syllabus, use remote proctoring, or require handwritten work. These tactics are familiar, straightforward to state, and sometimes effective for specific assessment types. Yet they carry clear trade-offs.
Pros of the traditional approach
- Clarity: Students know the rules and consequences. Short-term deterrent effect: Some students will avoid using AI tools if they fear detection or penalties. Fits existing assessment models: Exams and essays can be preserved without rethinking learning goals.
Cons and hidden costs
- False sense of security: Detection tools are imperfect, and outright bans encourage covert workarounds. Labor and stress: Remote proctoring and manual reviews significantly increase instructor workload and emotional labor. Equity harms: Students without private, quiet spaces or with certain disabilities face disproportionate penalties from timed, surveilled exams. Opportunity cost: Time spent policing can crowd out time for instruction, feedback, and curriculum design.
In contrast to policies that simply forbid AI, redesign approaches shift the emphasis from policing to assessment validity. That shift is not easier at first, but it can reduce ongoing policing costs while better aligning work with learning goals.
How integrating AI into teaching transforms assignments and assessment
What happens when you treat generative AI as a tool students can use, much like calculators or library databases? Instead of resisting, you redesign assignments to demand visible, assessable student thinking. That change requires rethinking prompts, evidence of authorship, and standards for acceptable assistance.
Core design moves
- Require process artifacts - Ask for drafts, annotated outputs, version histories, or chat logs showing prompts and subsequent edits. These artifacts reveal the student's reasoning, choices, and learning trajectory. Use iterative, scaffolded tasks - Break large projects into smaller deliverables with regular feedback. Low-stakes checks reduce the incentive to outsource entire assignments. Create prompts that resist generic completion - Localize prompts with course-specific readings, class discussions, datasets, or personal reflection. How would you synthesize last week's seminar debate into a policy memo for a named stakeholder? Include oral or viva components - Short reflections or defenses make it hard to submit work without grasping the underlying ideas. Teach prompt literacy and ethical use - Spend class time on how to query, evaluate, and edit AI outputs. Students need framework and judgment as much as technical know-how.
Similarly, when instructors build assessment tasks that assume AI will be available, the work they assign tends to privilege higher-order cognitive skills: framing problems, critiquing evidence, integrating perspectives, and communicating to specific audiences. In contrast to bans, this approach leans into the technology while preserving the intellectual work that demonstrates learning.
Practical alternatives you can combine: detection tools, contract agreements, and portfolio assessment
Redesign is powerful, but no single solution will work everywhere. Departments usually need a mixed strategy. Below are additional viable options, with comparative strengths and weaknesses.
Approach What it does well Principal limitations AI-detection software Provides a quick signal; useful for flagging concerns False positives and negatives; arms-race with model improvements; privacy concerns Syllabus contracts and explicit AI policies Sets expectations; makes grading consistent across sections Requires enforcement; cannot by itself reveal misuse Portfolio or process-based grading Shows student growth; reduces high-stakes cheating incentives More grading time up front; needs clear rubrics Oral exams and in-class presentations Directly tests understanding; scalable with small groups Logistics for large classes; requires training for consistent scoring Shared department question banks and scaffold templates Spreads workload; preserves consistency across course sections Requires coordination and upfront investmentOn the other hand, combining some of these approaches can offset their individual weaknesses. For instance, a policy that permits AI when accompanied by required process artifacts and spot oral checks balances flexibility with accountability.
Choosing the right strategy for your department and courses
How do you decide among these options? There is no single correct answer, but a decision sequence can help.
Map outcomes to assessment types. Which assessments genuinely need closed-book conditions? Which can be authentic, open-resource tasks? Estimate capacity. How many hours can instructors reasonably dedicate to redesign? Can the department pool resources for shared rubrics, question banks, or templates? Prioritize equity checks. Who will be disadvantaged by stricter enforcement? What accommodations will be needed for students with disabilities and for those with limited technology? Choose hybrid measures. Mix redesigned tasks, minimal detection, and teachable AI-literacy moments. What combination gives you acceptable fidelity without unsustainable labor? Iterate and measure. Pilot changes in one course or section, collect student feedback and grading time data, and refine before scaling.Ask yourself: Which assessments are signal-rich for the learning outcome? Where can we be pragmatic? Departments that answer these questions collectively often produce more coherent and sustainable policies than those that leave each instructor to fend for themselves.
Practical implementation: concrete steps for the next semester
Want a checklist you can act on in the coming term?
- Create a one-page AI policy template for your syllabus that explains acceptable uses, required documentation (like chat logs), and consequences. Redesign one major assignment per course into a scaffolded project with a draft, peer review, and oral reflection. Train TAs on what process evidence looks like and how to evaluate prompt logs or revision histories. Form a departmental working group to build a shared prompt bank and rubric library to reduce duplicated effort. Survey students mid-semester about access to AI tools, internet reliability, and their understanding of ethical use.
In contrast to sweeping mandates, incremental pilots let you gather data on grading time, student learning, and fairness. That evidence will make subsequent scaling easier and more defensible.
What faculty leaders must watch out for
Department heads and program directors face pressures different from those of an individual instructor. Which risks should they manage?
- Faculty burnout - Large-scale redesign projects can be rewarding but also exhausting. Funding time and offering course-release or summer stipends matters. Policy inconsistency - Students take multiple courses; inconsistent rules create confusion. Coordinate at the department or program level so messages are aligned. Student trust - Heavy-handed surveillance erodes trust. Transparent communication and teaching AI literacy build a more constructive relationship. Legal and privacy issues - Some detection tools collect student writing or submit work to third-party servers. Legal review and informed consent are necessary.
How will you balance academic standards with support for faculty and students? Departments that commit resources to training and shared materials typically see better outcomes than those that demand change without support.
Summary: A comparative path forward
Faced with generative AI, the instinct to ban and police is understandable. It is also incomplete. The more productive path contrast two broad options: attempt to preserve existing assessment formats through prohibition and surveillance, or rework assignments and pedagogy so that student learning is visible, authentic, and resilient to AI assistance. Each approach has trade-offs.
Strict enforcement can deter misuse but increases workload, risks inequity, and may only slow an ongoing technological shift. Integration and redesign require initial investment, departmental coordination, and new grading strategies, but they better protect learning outcomes and student development over time. Detection software, syllabus contracts, portfolio assessment, and oral defenses are practical tools you can combine depending on course https://blogs.ubc.ca/technut/from-media-ecology-to-digital-pedagogy-re-thinking-classroom-practices-in-the-age-of-ai/ goals and capacity.
Ask questions often: Which tasks truly measure what we value? How much redesign time can faculty spare? Who might be harmed by stricter policies? A mixed, evidence-driven approach that prioritizes learning, equity, and faculty support will serve departments better than any single rule.
Finally, remember this unconventional but practical truth: adapting to generative AI is not primarily a technical problem to solve. It is a curricular and human problem. When departments treat it as such - aligning outcomes, protecting equity, and sharing the workload - they create teaching practices that survive the next wave of tools as well as this one.