SUNY Potsdam Jan 2026 Workshop AI as Tool, Not Cheat: Faculty Development for AI Literacy

Workshop Structure

This 2.5-hour workshop guides faculty through hands-on experience with AI as a cognitive partner. The structure emphasizes process over product, with rapid iteration and shared documentation.

Session 1: Experiencing AI Literacy (11:00-11:40)

Introduction & Toolkit Demo (11:00-11:20, 20 minutes)

Welcome and workshop goals
Introduction to the toolkit: LLMs (ChatGPT, Claude, Gemini), browser exporters, Google Drive
Superfast demo: four-prompt sequence from start to export
Q&A on technical setup

Faculty Exercise: Reading, Thinking, Writing with AI (11:20-11:40, 20 minutes)

Faculty work through the four-prompt sequence documented on the Shared Prompts page.

Shared Prompt 1: Initial Dialogue About Thinking (5 minutes)

Navigate to Shared Prompts page
Copy and paste Prompt 1 into chosen LLM
Engage in dialogue: answer 5 AI questions, then ask AI 5 questions
Complete without generating summary

Upload Readings (2 minutes)

Access the three readings from Workshop Readings page
Upload or paste all three readings into LLM conversation

Shared Prompt 2: Progressive Synthesis (4 minutes)

Copy and paste Prompt 2 into LLM conversation
AI generates four summaries at increasing word counts (7/15/25/100 words)
Review summaries

Shared Prompt 3: Encyclopedia Entry (4 minutes)

Copy and paste Prompt 3 into LLM conversation
AI generates 5-sentence encyclopedia entry on thinking
Review entry

Shared Prompt 4: Format for Export (3 minutes)

Copy and paste Prompt 4 into LLM conversation
Provide name/identifier when asked
AI generates structured summary with markers
Verify formatting

Export and Share (2 minutes)

Use browser extension to export conversation to markdown
Upload markdown file to shared Google Drive folder

Break (11:40-11:55, 15 minutes)

What happens during the break:

While faculty take a break, facilitators will:

Collect uploaded transcripts from Google Drive
Concatenate transcripts into single document
Upload to facilitator's LLM
Run three Analysis Prompts from Shared Analysis Prompts page:
- Analysis Prompt 1: Pattern Identification
- Analysis Prompt 2: Meta-Process Reflection
- Analysis Prompt 3: Discussion Preparation
Prepare findings and discussion questions for Session 2

Session 2: Examining Process and Building Archives (11:55-1:30)

Analysis Discussion (11:55-12:15, 20 minutes)

Share analysis results: What patterns emerged in how faculty think about thinking?
Highlight key findings from the three Analysis Prompts:
- Common themes and definitional patterns
- Distribution of positions on whether AI thinks
- Quality of dialogue and synthesis
Discussion: What did the collective transcripts reveal?
Connect findings to pedagogical implications

Process Review: RTW with TTTS (12:15-12:40, 25 minutes)

Review the specific four-prompt sequence and toolkit, showing how each step involved Reading, Thinking, and Writing (RTW) with Tools, Techniques, and Technological Systems (TTTS):

Reading with AI:

Tool: Large Language Model (ChatGPT, Claude, or Gemini)
Technique: Uploading source texts and directing AI to process them
System: The four-prompt structure that scaffolded engagement
Action: Faculty directed AI to ingest and synthesize the three readings

Thinking with AI:

Tool: Conversational interface enabling dialogue
Technique: Structured questioning (5+5 questions, progressive synthesis)
System: The prompts as cognitive scaffolding
Action: Faculty explored concepts through sustained exchange, not one-off queries

Writing with AI:

Tool: AI as compositional partner
Technique: Constrained writing tasks (7/15/25/100 words, then 5 sentences)
System: Progressive refinement from broad to precise
Action: Faculty generated text through AI-mediated composition

Discussion questions:

Where did you feel most "literate" with AI—reading, thinking, or writing?
What made this "literacy" rather than just "tool use"?
How does this connect to what we ask students to do?

Archive as OER (12:40-1:05, 25 minutes)

Demonstrate the shared archive of transcripts and how it functions as open educational resource:

The Archive Itself:

All transcripts in shared Google Drive folder
Structured with markers for easy parsing: <<PARTICIPANT_NAME>>, <<CONVERSATION_STATISTICS>>, <<GENERATED_PARAGRAPH>>
Available for re-analysis, querying, reading as primary sources

Working with the Archive:

Show how transcripts can be re-uploaded to LLMs for further analysis
Demonstrate querying for specific patterns or insights
Discuss reading transcripts as documentation of thinking processes

OER Principles:

This workshop itself is openly documented
All prompts are reusable and adaptable
Archive serves as both product and process documentation
Faculty can take this model to their own contexts

Discussion questions:

How could you create similar archives in your courses?
What value does the archive add beyond individual transcripts?
How does making process visible change learning?

AI Literacy Framework (1:05-1:30, 25 minutes)

Connect workshop experience to SUNY General Education Information Literacy competency
Framework: Reading, Thinking, Writing (RTW) with Tools, Techniques, and Technological Systems (TTTS)
How AI literacy fits within Information Literacy requirement
Framework applies across disciplines and course levels
Discussion: Applications to your own teaching contexts
Closing reflections and next steps

Materials Checklist

Pre-workshop:

Shared Google Drive folder created and link ready
Three readings uploaded to workshop page
Shared Prompts page complete with all four prompts
Shared Analysis Prompts prepared

During workshop:

Slide deck for introduction and demos
Workshop page URL clearly displayed
Toolkit page accessible
Backup: printed copies of readings (optional)
Backup: pre-loaded conversations in case of tech issues

Collaboratively Produced by the SUNY AI Fellows for the Public Good
Steve Schneider (SUNY Polytechnic Institute) & Michelle Malinovsky (SUNY Onondaga)