Artificial Intelligence Resource Center

The big idea behind today’s powerful AI is something called “pattern matching at superhuman scale”:

• Show the computer millions or billions of examples.

• Let it find super complicated patterns in those examples.

• Then ask it to make a very educated guess about new things.

It doesn’t understand things the way you and I do.

It doesn’t have feelings, goals, curiosity, or common sense.

It is incredibly good at imitating patterns it has seen before — sometimes so good that it feels like magic.

Google Gemini

Google Gemini is Google's advanced generative AI assistant, integrated across Google Workspace tools like Gmail, Docs, Sheets, and Slides. It helps users brainstorm ideas, draft and refine content, summarize information, take meeting notes, and boost productivity while maintaining enterprise-grade security and data protection. All Antioch staff, faculty, and administrators are eligible to use Gemini through their Antioch Google Workspace account.

To request access, simply submit a ticket to the Antioch IT Helpdesk with your name, role, and Google Workspace email address. Once reviewed and approved, the Gemini features will be enabled for your account.

 Once reviewed and approved, the Gemini features will be enabled for your account. For guidance on getting started and best practices, refer to the official resources below:

• Gemini Apps Help Center – Tips, tutorials, and answers to common questions.

• Google Workspace with Gemini documentation – How to use Gemini directly in Workspace apps.

• How to find, rename, and delete your conversations with Gemini  (YouTube video) – Learn how to find and manage your Gemini conversation history while working in Docs, Drive, Gmail, Sheets, and Slides.

• Gemini overview and getting started – General introduction to capabilities and features.

'AI Ready' Training from the Council of Independent Colleges

"AI in the Flow of Work"

Week One Summary

Week One focused on integrating AI into the daily flow of administrative work, moving campus leaders from "tool anxiety" to values-aligned practice. Speakers established a human-centered division of labor where AI handles routine repetition, drafts, organization, and pattern recognition, while humans provide judgment, institutional knowledge, ethical oversight, and final accountability. The session traced higher education’s journey from early experimentation toward intentional campus adoption, emphasizing that AI should support the work rather than become the point of it.

The group explored practical guardrails and frameworks for safe implementation. Presenters shared two foundational safety moves before prompting: anonymizing inputs by removing identifying details and using approved institutional tools to protect sensitive data. Participants also reviewed the "AI Integrated Scale"—a progressive roadmap moving from basic generation and iterative critique to integrated and orchestrated workflows. Practical campus builds, including a contract evaluator and a handbook tool, demonstrated how institutions can apply these principles directly to reduce friction in real administrative tasks.

The session closed with a clear call to action: start with friction, add a safeguard, and measure whether it helped. Rather than attempting massive leaps, leaders were encouraged to identify worthwhile opportunities and take one safe, incremental step up the integration scale. AI can handle raw data and administrative patterns, but institutional values and human discernment should guide the work.

Week One Links to Resources

Articles and Tools:

• Participant Handout

• Basic Guide to Generative AI

• The Impact of AI on Work in Higher Education

• Synthetic Practice Data Set

• 100 Ways Your Peers are Using AI

• From Friction to Impact: A Practical Framework for Identifying Worthwhile AI Opportunities in Higher Education 

Week Two Summary

Week Two centered on shifting campus AI adoption from isolated, ad hoc wins toward a shared, institutional decision making framework. Building on foundational safety and workflows, the session combined practitioner demonstrations with the "Strategic Horizons" model to help academic leaders systematically evaluate which AI opportunities are genuinely worth pursuing. The discussion highlighted that valuable AI adoption requires both a worthwhile problem and a prepared, capable team equipped with trustworthy baseline data, clear ownership, and the ability to recognize faulty outputs.

Participants explored five core evaluation criteria: Value, Readiness, Risk, Repeatability, and Relational Return. Presenters underscored a vital principle: strong performance across four dimensions never cancels unacceptable risk in the fifth. To govern implementation safely, teams were urged to match checkpoints to the consequence of error—limiting sensitive data, establishing recovery paths, and reserving high-discernment decisions for humans. Furthermore, repeatable workflows must yield a clear relational return, ensuring that time reclaimed from routine tasks (such as search and drafting) is intentionally reinvested into high-touch advising, mentoring, and student presence.

The session culminated by guiding institutions to map AI initiatives into distinct action tiers: "Now" (bounded pilots with safeguards and baseline metrics), "Next" (promising ideas needing prerequisites), "Later" (immature evidence or capacity), and "Not Now" (unacceptable risk or low value, paired with defined revisit triggers). Rather than deploying autonomous solutions in sensitive student-facing areas, leaders were prompted to reflect on where their top ideas belong today, ensuring that efficiency gains directly protect and amplify human connection across campus.

Week Two Links to Resources

• Participant Handout

• Basic Guide to Generative AI

• AI Opportunity Framework

• Strategic Horizons Slide Deck

• Strategic Horizons: Talking Points for Deciding What is Worth Pursuing with AI

• Sample AI Policy from Brenau University's Theatre Department (thank you to Pam Workman for sharing!)