Common questions

No. Northeastern's AI standards leave that decision to each instructor, and prohibiting AI is allowed. The one exception: if you teach from a shared syllabus that includes AI, follow its learning outcomes.

Keeping a course AI-free can still build AI readiness. Teaching students when not to use AI, and why, is part of using it responsibly.

Pick one assignment, not your whole course.

• Run the quick check to see if it's AI-vulnerable.
• Add a syllabus statement and an AI label so students know where you stand.
• Or try something from CATLR's One Thing to Try.

If you'd like someone to do the thinking with you, request a CATLR consult.

Yes, just differently. In the humanities and qualitative social sciences, AI work often looks like:

• Critiquing an AI's argument, sources, or reading of a text
• Discussing the ethics of AI in your field
• Comparing AI analysis with close reading or fieldwork
• Role-play and perspective-taking activities

Northeastern's AI Readiness Framework includes ethical and responsible use and human creativity alongside technical skills. CSSH's NULab DITI has critical AI literacy materials built for our fields.

Where possible, point students to university-supported tools that IT has vetted for security and privacy, such as the NU Claude Portal. That also means no student needs a paid account. For questions about approved tools, contact Academic Technologies.

It can, when an assignment can be completed without the learning it's meant to measure. Northeastern's faculty guide Assessment in an AI-Enabled World treats this as an assessment design problem, not only a policing problem.

Small changes help: grading process as well as product, anchoring work to class discussion, adding a short in-class checkpoint, or a brief oral conversation about the work. The Assignment Toolkit has options by assignment type.

The university doesn't recommend them. AI detectors are unreliable and produce false positives, and using any detection tool requires prior approval from the AI Review Committee. See the rules to know.

Start with a conversation: ask the student to walk you through their process or how they reached their answer. If you still have an academic-integrity concern, follow the standard process with OSCCR.

Using generative AI to grade open-ended work, such as essays or multimodal projects, requires AI Review Committee review first. Closed-form items like multiple choice aren't covered by that rule. Either way, the standards encourage telling students when and how you use AI in assessment.

It's a fair concern. Used as a shortcut, AI can let students skip the practice that builds skill. That's why the approaches on this site put students' own thinking first:

• Do it yourself first, then with AI, and compare.
• Argue with the AI: find its errors instead of trusting it.
• Know when not to use it, and say why in your course.

The goal is judgment, not dependence. See Build AI in.

More than knowing how to use a chatbot. Northeastern's AI Readiness Framework describes four dimensions: understanding AI and data, critical thinking and judgement, ethical and responsible use, and human creativity (also called creativity and innovation). In practice that means knowing when a tool helps, checking what it produces, using it ethically, and adding human value on top.

The framework describes two levels: literacy (foundational knowledge and critical thinking about AI) and fluency (practical skill using AI tools effectively). No single course is expected to cover all of it; readiness builds across a program.

This site is part of Northeastern's Curricular Transformation Initiative, which focuses on AI readiness. Each college has directors; in CSSH they are Richeng Piao (AI Teaching Innovation) and Linda Kowalcky (AI Curricular Transformation).

Our role is support, not enforcement. You decide how AI fits your courses, and we help you do it well.

• CATLR is the university's teaching center. Go there for teaching consultations, workshops, and self-paced courses.
• NULab DITI is CSSH's Digital Integration Teaching Initiative, which helps bring digital methods and tools into courses.
• The CSSH AI directors build a faculty community around AI in teaching: the faculty cohort, events, course redesign in CSSH, and connecting you with the right resource.

We work together, so you won't get bounced around.

• Assessments that measure what students actually learned
• Ready-made syllabus language, templates, and assignment designs that save prep time
• Students better prepared for co-op and careers where AI is part of the work
• A community of colleagues, and a chance to showcase your teaching

For a teaching consult, CATLR. For CSSH-specific questions, see who to email. To join the faculty cohort, use the sign-up form.