Higher education certificate
A faculty certificate on assignment-level AI expectations, assessment design, academic integrity, research, privacy, equity, transparency, and preserving meaningful learning.
Who it is for
This higher-education learning experience is clearly separated from K–12 training and designed for the faculty or leadership audience named in the course.
The focus is institutional judgment, academic values, transparent practice, and responsibility that remains with people.
Course map
Students encounter generative AI in dedicated chatbots, search engines, office suites, coding environments, note-taking tools, accessibility products, and discipline-specific platforms. Faculty encounter the same technologies in course preparation, research, communication, and administrative work. A policy that assumes AI appears only when someone opens a ch
Students should not have to guess which parts of an assignment may involve AI. Expectations become clearer when faculty define the permitted level of involvement for each major task.
Generative AI can produce polished outputs that exceed a student's independent understanding. That means final products alone may provide weaker evidence of learning than they once did. Faculty need to distinguish supported performance from what students can explain, transfer, and do independently.
Faculty may use AI to brainstorm examples, draft quiz questions, prepare explanations, create case studies, summarize course materials, draft correspondence, generate feedback, or support grading. The ethical question is not whether faculty may use tools. It is whether the use preserves accuracy, confidentiality, fairness, professional judgment, and trust.
The central question is whether the submitted work truthfully represents the student's permitted process and learning. AI complicates authorship because a student may type every final sentence while AI generated the ideas, evidence, structure, or analysis.
AI may support literature discovery, coding, language editing, data analysis, visualization, manuscript preparation, or research administration. These uses can create risks involving validity, reproducibility, confidentiality, intellectual property, authorship, and disclosure.
Faculty should know which tools are institutionally approved, what information may be entered, how data are retained or reused, and what contractual protections exist. Student records, accommodations, advising information, health information, assessment results, and personal circumstances deserve particular care.
1. What are the course's most important learning outcomes?
What learners receive
Ready when you are