My Presentation, Consultation, and AI Development Services

🤵 Who I Am

I, Dr. Graham Clay, am a philosopher who now works primarily on AI-education integration.

Beyond the AutomatED newsletter and our internal webinars, I provide clients three services:

1. external presentations + webinars

2. AI pedagogy consultations

3. AI workflows and AI solutions

As a teacher, I have taught 600 students across 7 courses (17 sections) at the University of North Carolina at Chapel Hill, the University of Notre Dame, and Fort Lewis College. I have taught in-person courses, hybrid courses, online synchronous courses, and online asynchronous courses.

As a researcher, I have published 8 peer-reviewed articles in leading philosophy journals, including on AI ethics; presented 16 papers at 22 conferences in 8 countries; and received more than $120,000 in grants for my own projects.

Via the buttons below, you can email me or schedule a free 30-minute exploratory Zoom with me to discuss how I can help you with AI-education integration:

Or you can scroll further to read more about the three services noted above…

📣 Presentations & Webinars

Here are some stats from my recent presentations:

• 95% of attendees reported that I communicated information clearly and was organized

• 90% reported that I kept them engaged

• 100% reported that I answered their questions

• 81% reported that they can apply information I provided in their work/research

• despite 76% having minimal knowledge of tech and AI issues

Over the past year, I have worked with and presented to a range of audiences from institutions like Furman University, UNC-Chapel Hill, and Kent State University.

Here are some recent and upcoming presentation titles/topics:

• “Personalization, Plagiarism, and Productivity: Helping Educators Leverage AI”

• “Using AI to Multiply Your Teaching and Time”

• “Train Your Students to Use AI”

• “‘Train the Trainer’: Copilot Building Workshop”

• “Using AI to Analyse Student Grades and Course Evaluations”

• “Improve and Accelerate Feedback with Microsoft Copilot”

💭 AI Pedagogy Consultations

In a range of contexts, I provide expert guidance to faculty, departments, and institutions about their inclusion and exclusion of AI in their pedagogy.

I coach graduate students on AI pedagogy, help professors upgrade their syllabuses, and advise chairs on how to approach AI in their departmental context.

Here are some options:

1. I help you revise your strategy for discouraging AI plagiarism or misuse and for addressing cases of suspected AI plagiarism, thereby increasing the effectiveness, credibility, and fairness of your pedagogy.

   • The result: A learning environment that encourages originality and earnest engagement, as well as one that properly holds students accountable.

2. I examine the materials for one of your courses — from your syllabus to your assignments to your lesson plans to your final project — with an eye to ways to better reach your pedagogical goals by incorporating more technology, including training students in the use of AI tools.

   • The result: An enriched teaching experience, and students who are more engaged and better prepared for the digital age.

3. I present an AI primer to your department or team, targeted to your context.

   • The result: Your team is better positioned to have internal meetings about the direction you want to take to deal with the opportunities and challenges of AI.

🤖 AI Workflows and AI Solutions

I develop a range of custom technology solutions for individuals, primarily faculty.

For instance, I can help you set up a range of AI-powered software — project management, voice transcription, and time management — and integrate it with Canvas, Zoom, and your email client. As a result, you save valuable time on administrative tasks so that you can focus on what truly matters: teaching (and perhaps your research, too).

Another option is to help you with your grading and feedback processes. This is a common request from my clients.

I offer a range of solutions — or “AI workflows” — that can be built or tailored to your specific needs and preferences.

Some key dimensions to consider are:

1. Student Work Sharing: This ranges from full sharing of student work (including names) with AI systems to no sharing at all. Intermediate options include anonymization/pseudonymization or obtaining explicit student consent.

   • I have developed bespoke anonymization/de-anonymization tools that can be deployed in your own IT environment, keeping student data safe from any AI tools used.

2. Student Work Analysis by AI: At one end, the AI system conducts most or all of the analysis based on rubrics, criteria, or past examples. At the other end, you as the professor evaluate the work entirely, with the AI providing no analysis.

   • I generally recommend a mixed approach that varies according to the assignment (multiple-choice quizzes are easier to analyze with AI without oversight, while complex multimedia projects are hard).

3. Final Output Production by AI: This spans from the AI's output being given directly to students, to the AI offering suggestions that you modify before sharing with students.

Your position on each of these spectrums may vary depending on the specific assignment.

We can work together to find the right balance that aligns with ethical requirements, your teaching philosophy, course needs, and institutional policies.

Four pre-built workflow types that I offer are the following:

1. Automated Grader (and Feedback Generator):

2. AI Administrative Assistant/Secretary:

   • No access to student work.

   • Access to your rubrics, your old feedback, and your rough notes (dictated, if desired) on new student work.

   • Accelerates the production of professional and cohesive feedback.

   • Spend more time focused on student submission content, and less on how you package it to them — let the AI handle that while you handle what only you, the expert, know.

3. Category-Based Feedback Generator:

   • No access to student work.

   • You select categories of strengths and weaknesses (that you designate in advance as options)

   • AI generates feedback based on your old feedback samples that align with each of these categories

   • Feedback is synthesized in the format you desire based on your selections and your old feedback samples, in order to create unique responses for new students while maintaining core meaning from old feedback

   • You can then customize the feedback, both by editing it and by adding to it, before sending to students

4. Option 2 or 3 + Customization:

   • Similar to the above, but with some limited access to (anonymized) student work

   • Enables AI to further tailor category-based feedback or your notes to specific aspects of new student submissions that the AI has access to