Stop Policing AI. Start Teaching for Academic Integrity.

It's Friday morning. You're reviewing student essays when a familiar question enters your mind:

Did my student actually write this?

You run the essay through an AI detector. It flags the writing. You try another detector and get a different result. Then another.

Now you've spent more time investigating the essay than evaluating the student's ideas, and you still don't have a reliable answer.

There has to be a better way.

Welcome to Adult Education: AI Ethics & Academic Integrity Foundations, a research-informed course designed to help educators move beyond AI detection and build learning experiences that promote academic integrity, critical thinking, and responsible AI use.

Instead of asking only whether students used AI, this course helps you answer more meaningful questions:

How can I make student thinking visible?
When does AI support learning, and when does it replace it?
How can I redesign assignments using manageable, practical strategies?
How can I protect student voice, especially for multilingual writers?
How can I respond to suspected AI misuse fairly and confidently?

What You Will Learn

Across 10 learning modules, 44 lectures, and nearly six hours of video content, you'll develop practical, research-informed strategies to:

Move Beyond AI Detection

Understand the AI detection arms race, the limitations of detection tools, and how to respond to suspected AI use without treating detection scores as proof of misconduct.

Build Critical AI Literacy

Understand how large language models (LLMs) work, evaluate AI-generated claims, verify sources, recognize unreliable information, and design productive struggle into learning.

Establish Responsible AI Expectations

Apply the AI Assessment Scale, use student decision-making frameworks, implement the 6Cs Protocol, and facilitate a Class AI Agreement that encourages shared responsibility.

Make Student Thinking Visible

Use process-based writing, revision justification, reflective activities, and evidence trails to better understand how students develop their ideas and make writing decisions.

Redesign Assessments for the AI Era

Apply the two-lane principle, audit existing assignments, explore the three-phase TBLT-AI framework, and incorporate interactive oral assessments that provide additional evidence of student learning.

Teach Students to Learn With AI

Design prompts that encourage questioning, reflection, metacognition, and independent thinking rather than simply generating answers.

Support Multilingual Writers and Student Voice

Examine linguistic bias, develop AI scaffolds for language learning, teach academic writing as rhetorical design, and help students maintain ownership of their writing.

Protect Human Judgment

Teach responsible research practices, source verification, critical evaluation of AI-assisted analysis, and consistent standards for educators and students.

Build Sustainable Academic Integrity Practices

Understand why students may misuse AI, develop institutional advocacy strategies, and plan a manageable semester-long approach to responsible AI teaching.

Learn Through Practice and Build Your Teaching Toolkit

This is not simply a theoretical course about AI ethics. You'll actively apply what you learn through quizzes, practical activities, and five interactive role-plays involving realistic teaching situations.

You'll practice:

Responding to suspected AI use fairly and constructively.
Facilitating classroom conversations about responsible AI use.
Conducting brief oral checks to better understand student thinking.
Helping students move from AI dependence toward independent judgment.

Throughout the course, you'll work with practical frameworks, including:

The AI Assessment Scale: Establish clear, assignment-specific expectations for AI assistance.
The 6Cs Protocol: Move from disclosure alone toward accountable AI use.
The Class AI Agreement: Involve students in establishing shared expectations and responsibilities.
The Process-Weighted Rubric: Give greater attention to the writing process and evidence of learning.
The Revision Justification Model: Help students explain their revisions and demonstrate ownership of their decisions.

Your Final Capstone: The Responsible AI Teaching Toolkit

You'll bring your learning together by developing an Agreement Facilitation Plan, a Redesigned Assessment, and an AI-Literacy Lesson Plan, accompanied by a short Evidence Note explaining the research and course concepts informing your choices.

You'll finish with practical resources you can adapt to your own teaching context.

Research-Informed and Grounded in Educators' Experiences

This course was developed around challenges educators are already facing.

Its content draws on 133 core dilemmas collected from language educators, including questions such as:

"How can we move from policing AI use to building academic integrity?"

"What happens when a strong writer is falsely accused of using AI?"

"How can we determine whether AI is supporting learning or replacing student thinking?"

The course is also informed by a systematic literature review in which our research team screened more than 2,000 studies examining generative AI, academic integrity, writing pedagogy, and assessment. The review is currently being prepared for publication.

Rather than offering another collection of AI tools, this course connects educational research with practical teaching strategies, particularly for educators working with adult, multilingual, and international learners.

Who Should Enroll?

Academic Writing and English Instructors who want to move beyond AI detection and develop more meaningful approaches to writing assessment.

ESL, EFL, and Multilingual Educators concerned about linguistic bias, equitable assessment, and preserving student voice.

Higher Education and High School Faculty seeking practical strategies for responsible AI integration, classroom expectations, and assessment redesign.

Adult Educators and Teacher Educators interested in critical AI literacy, student agency, and research-informed teaching practices.

Department Chairs and Academic Administrators looking for an evidence-based foundation for academic integrity policies and faculty development.

No programming experience or advanced technical knowledge is required.

From Policing to Pedagogy

You don't need to spend your weekends comparing AI detection scores or treating every polished essay with suspicion.

You can design learning experiences where students explain their decisions, demonstrate their progress, question AI-generated information, and take responsibility for their writing.

The goal is not to create AI-proof classrooms. It is to build learning environments where human thinking, judgment, and student voice remain central, even when AI is readily available.

We cannot police our way to academic integrity. But we can design learning experiences that support it.

Join the course and start building a more confident, equitable, and research-informed approach to teaching writing in the age of AI.