Amplify for Animals 12-Week AI Training Program

This program is designed for animal advocates who want to start and/or strengthen the way they create impact with AI — including those who are curious and eager to explore, as well as those who may feel a little cautious. There is no need for a technical background. We’ll start from the beginning and focus on practical, responsible ways to use AI to save time, boost advocacy, and advance animal protection.

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Practical, Not Technical
Hands-on training designed for advocates, with real workflows you can use straight away.
Your Personal AI Playbook
Walk away with tailored prompts, tools, and time-saving strategies built for your work.
Community + Support
Learn alongside peers, with live sessions, side labs, and full access to Team-GPT during the program.

Our mission

What you’ll walk away with:

A clear understanding of what AI can do, an introduction to key tools, and a strong foundation for using them effectively
A personal AI Playbook (a living document of prompts, workflows, and quick wins tailored to your work)
A roadmap for continued learning after the program ends
Full access to Team-GPT during the program — an easy to use AI workspace where you can try out leading AI models (like GPT, Claude, Gemini, and Perplexity) in one place, without needing separate accounts or paying fees
A certificate of completion for those who attend most core learning modules

Program overview:

Core program:
- 12 weekly learning modules will serve as the main learning track
  Sessions will cumulatively build upon one another and will be recorded.
  (60 min + optional 30 min extension for Q&A, hands-on practice, or deeper dive depending on the week)
- Each learning module will have complementary practice activities
  These quick exercises will help you apply and deepen your learning
Optional sessions:
- Weekly Side Labs & Peer Learning Meetups
  These optional 60-min deep-dive sessions will focus on role- or tool-specific applications. The planned content may change based on group needs and registrations. To encourage more open sharing, these sessions will not be recorded.

Office Hours & Playground
An informal space to drop-in for questions, troubleshooting, and experimentation. To encourage more open sharing, these sessions will not be recorded.

A special thank you to the two groups who sponsored this program and provided access to Team GPT: The Navigation Fund and Stray Dog Institute.

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Schedule

Program begins September 22nd, 2025

To meet you where you (physically) are, each session will be offered in two time zones each week. Option 1 is kept on EST for groups in the Americas and for EMEA staff who prefer evening sessions. Option 2 is catered to those in the APAC region and for EMEA staff who prefer morning sessions.

Core Learning Modules (60 min + 30 min optional)
Option #1 – Americas/EMEA evenings: Tuesdays 12:00pm ET
Option #2 – APAC/EMEA mornings: Wednesdays 12:30pm IST / 2:00pm ICT

Side Labs / Peer Learning Meetups (Optional: 60 min)
Option #1 – Americas/EMEA evenings: Wednesdays 12:00pm ET
Option #2 – APAC/EMEA mornings: Thursdays 12:30pm IST / 2:00pm ICT

Office Hours (Optional: 60 min)
Option #1 – Americas/EMEA evenings: Thursdays 11:00am ET
Option #2 – APAC/EMEA mornings: Tuesdays 12:30pm IST / 2:00pm ICT

Note: For option 1, the timings shown will remain consistent on Eastern Time as noted above. When daylight savings ends on November 2, some regions may notice the session times move back by one hour. (Option 2 will not shift since APAC generally does not observe daylight savings)

Weekly Curriculum

Each week builds on the last, moving from curiosity → confidence → impact.

Phase 1: Foundations

Week 1 – What’s Possible with AI: Why it Matters for Animal Advocates

What you’ll learn: A clear, non-technical overview of what AI is, where it’s heading, and why it matters for advocacy. You’ll see inspiring, practical demos, from instantly summarizing a research report to generating campaign visuals, that highlight both opportunities and limits. You’ll see how AI can save you time while keeping you firmly in control of the decisions that matter.

Example in-session activity: Live demo of an AI tool that drafts a supporter email in 2 minutes.
Example practice at home: Try two versions of an AI model (e.g. Gemini Flash vs. Pro) on the same advocacy question and notice differences. Start your personal AI Playbook.
By the end of this week: You’ll have tested two AI tools, started your personal AI Playbook, and seen how AI can shave time off routine advocacy tasks like supporter emails.
Optional Side Lab: Getting Set Up — creating accounts, trying dictation/voice-to-text.

Week 2 – Responsible AI: Align Your Use with Your Values

What you’ll learn: How to use AI safely and ethically in your advocacy. We’ll cover privacy, bias, environmental footprint, and risk mitigation. You’ll learn to balance speed with responsibility, and find out if your organization already has an AI policy.

Example in-session activity: Risk checklist exercise — identify and discuss real scenarios.
Example practice at home: Find the privacy setting for your preferred AI model; read and understand your organization’s AI policy (if you have one).
By the end of this week: You’ll have the information you need to use AI in a way that aligns with your values and your organization’s AI policy.
Optional Side Lab: Collaborative sprint exploring sample policies: expand your own draft if you want, or adapt an existing template you could share with colleagues or peers.

Week 3 – How AI Works: Clear and Simple

What you’ll learn: The inner workings of large language models — why they sometimes get things wrong, and how to write prompts that work. We’ll demystify jargon like “tokens,” “context windows,” and “hallucinations,” so you understand AI’s strengths and limits.

Example in-session activity: “Good prompt / bad prompt” critique — see how small changes matter.
Example practice at home: Test the same three prompts in two different tools; also try multiple prompt variations for the same task to compare quality and usefulness of outputs.
By the end of this week: You’ll understand how AI gets things wrong and how to write prompts that get you clearer, more reliable answers.
Optional Side Lab: Compare model outputs with simple, visual tools.

Phase 2: Core Skills

Week 4 – Don’t Be Fooled: AI Error Spotting and Fact Checking

What you’ll learn: How to make sure AI’s answers are correct before you use them. Techniques include multi-AI cross-checks,”grounding” using internet search features or file upload, and quick verification steps. Especially important for advocacy where accuracy and credibility are critical.

Example in-session activity: “Error hunt” — catch and correct live mistakes in AI answers.
Example practice at home: Prompt AI for everyday questions, then deliberately fact-check for errors.
By the end of this week: You’ll be able to spot AI mistakes quickly and verify information, so you can trust outputs before sharing them publicly.
Optional Peer Learning Meetups: Fundraising, Marketing, Communications, Social Media

Week 5 – AI as Your Coach & Teacher: Think Better, Learn Faster

What you’ll learn: How to use AI to learn faster — whether it’s quizzing you on campaign strategy, simplifying technical language, or explaining climate science.

Example in-session activity: “Explain like I’m 10” challenge on a complex topic.
Example practice at home: Pick a subject you know little about; use AI for a back-and-forth learning dialogue.
By the end of this week: You’ll know how to use AI to learn a brand-new topic or skill, and how to check that it’s teaching you correctly.
Optional Peer Learning Meetups: Programs, Campaigns, Investigations, Research

Week 6 – Beyond ChatGPT: A Treasure Chest of AI Tools

What you’ll learn: Explore the vast world of AI tools beyond chat-based assistants. You’ll discover platforms for design, writing, research, video, data, and automation — and learn how to choose the right tool for the job. We’ll focus on how these tools can amplify your advocacy work, save time, and unlock new creative possibilities.

Example in-session activity: Guided tour of 10–12 standout tools — try one from each category (text, image, video, data, workflow). Quick “speed demos” show what’s possible in 90 seconds.
Example practice at home: Pick one new tool you’ve never used before and test it on a real project or campaign idea. Note how it changes your process or outcomes.
By the end of this week: You’ll have a personalized mini-toolkit of AI apps that go far beyond ChatGPT — and know how to keep exploring new ones with confidence and discernment.
Optional Side Lab: AI Tools Playground — hands-on session to experiment with different creative and productivity tools and share your discoveries.

Phase 3: Applied Practice

Week 7 – Build Your Own AI Assistant: Create a Partner for Your Advocacy

What you’ll learn: How to create an AI assistant that can be customized with resources you have access to (like handbooks, reports, articles, or your own notes).

Example in-session activity: Guided build of a strategic “thought partner” using resources like your organization’s strategic plan, a campaign handbook, or sample docs.
Example practice at home: Upload a resource and test how well your assistant answers.
By the end of this week: You’ll have a working AI assistant built on a resource you chose, ready to act as a thought partner in your advocacy work.
Optional Side Lab: Bring your own doc — guided clinic for building assistants.

Week 8 – Smarter Decisions with AI: From Research to Strategy

What you’ll learn: Using AI for research, data analysis, and strategic decision-making. You’ll learn how to feed AI your own data for more grounded answers.

Example in-session activity: Generate a fully cited AI research report on a topic you care about. Compare an AI answer with and without your own context.
Example practice at home: Apply AI to a real work question and see the difference.
By the end of this week: You’ll know how to use AI as a research assistant, helping you analyze reports, summarize findings, or compare options more quickly.
Optional Peer Learning Meetups: HR, Operations, Legal

Week 9 – Create Multi-Media Campaigns: From Draft to Publish

What you’ll learn: Using AI to generate text, images, videos, audio, and slides. You’ll design practical workflows (draft → edit → publish) for advocacy campaigns and outreach.

Example in-session activity: Draft and revise a social media post in real time, including the copy, images, and more.
Example practice at home: Build a 3-step workflow you’ll actually use next week.
By the end of this week: You’ll have a workflow for creating advocacy content (email, social, or visuals) that saves you time and keeps you in control.
Optional Side Lab: Experiment with image/video tools like Ideogram or Midjourney.

Week 10 – Automate the Boring Stuff: Save Time, Reduce Burnout

What you’ll learn: Intro to automation and AI agents. How to connect tools like Gmail, spreadsheets, and calendars to cut repetitive busywork.

Example in-session activity: Brainstorm automation candidates together.
Example practice at home: Map one repetitive task as a plain-language workflow. Bonus: build it.
By the end of this week: You’ll have mapped at least one repetitive task in your work into an automation, and know which tools could handle it for you.
Optional Peer Learning Meetups: IT, Data, Web Dev

Phase 4: Integration & Next Steps

Week 11 – Bringing It All Together: Powerful End-to-End AI Workflows

What you’ll learn: How to combine multiple AI skills and tools into end-to-end workflows (e.g., research → draft → verify → publish) that save hours and produce more consistent results. You’ll also learn how to measure those gains and share processes with peers or your team to spread effective practices.

Example in-session activity: Workflow swap — discuss and improve your AI workflows with a peer.
Example practice at home: Combine two or more AI tools to solve a problem you face every week in your advocacy.
By the end of this week: You’ll have tested a full end-to-end workflow (research → draft → verify → publish) and measured how much time it saves.
Optional Side Lab: Walkthrough of two real workflows (writing-heavy and analysis-heavy).
Optional Peer Learning Meetups: Managers, Directors, Coordinators

Week 12 – Keep Growing: Build Your Personal AI Roadmap

What you’ll learn: Recap and next steps. How to set realistic 30-day goals, where to find the best resources and communities, and how to keep your skills fresh without burning out. Connect with next-step opportunities like the AI Impact Hub and Code for Compassion hackathons.

Example in-session activity: Resource fair — guided tour of top tools and learning hubs.
Example practice at home: Draft your 30-day plan with 1–2 measurable goals.
By the end of this week: You’ll have a 30-day AI learning plan, a personal Playbook of workflows, and (if you’ve participated in most core learning modules) a certificate recognizing your progress. You’ll also have a clear map of where to plug into broader initiatives like AI Impact Hub and Code for Compassion.
Optional Side Lab: Coding clinic + AI Resource Fair.
Optional Peer Learning Meetup: Senior Leadership Roundtable — for Executive Directors, Managing Directors, and other senior leaders.

Recording Policy

Core learning modules: Recorded and shared (so you can catch up). Live attendance is encouraged, and those who attend most core learning modules live will be eligible for a completion certificate.
Side labs & office hours: Not recorded, to encourage a safe space for candid questions and open sharing.

Meet your Instructors

Steven Rouk
Steven Rouk is the founder and executive director of Connect For Animals, a platform for people who want to help end factory farming. He has been in the animal advocacy movement for the last decade, working on data analysis, strategy, community building, and grassroots activism. Steven has used AI extensively since the early days of ChatGPT, integrating it into core operations at Connect For Animals, including the events data pipeline, backend development, and strategic planning, with tools such as Claude Code, Cursor, n8n, and OpenAI’s APIs. He also works to help animal advocates use AI effectively to maximize their impact for animals.
Thomas "Richie" Manandhar Richardson
Thomas “Richie” Manandhar Richardson, PhD, is the Director of Research at Bryant Research, a social science think tank exclusively serving the animal advocacy movement. He is also the lead data consultant at Greener by Default, an organisation helping institutional foodservice to implement plant-forward menus using the latest behavioural science. A trained data scientist, Richie quit his job in tech to bring his data and AI skills to the animal advocacy movement.
Sam Tucker
Sam Tucker is the founder of Open Paws, a nonprofit organisation dedicated to training and deploying animal-aligned AI systems. Through Open Paws, he helps animal-friendly organisations integrate AI into their operations while advocating for the widespread adoption of animal alignment in all AI systems. The organisation’s mission is to create a future where AI respects all sentient life, safeguarding animal welfare while also mitigating existential risks to humanity.
Kyle Behrend
Kyle Behrend is the founder of AI Impact Hub, a platform dedicated to helping nonprofits adopt AI and automation responsibly and effectively. With over 15 years of experience across the nonprofit sector—from animal sanctuaries to foundations—he has seen how better systems can reduce busywork and allow organisations to focus on their mission. Through workshops, tools, and community initiatives, Kyle works to make AI accessible, ethical, and impactful for mission-driven teams.
Tania Luna
Tania Luna is the Program Director at Scarlet Spark, a nonprofit that helps animal advocacy groups become human-friendly workplaces by strengthening their strategy, leadership, and culture. She brings experience as a psychology researcher and co-founder/CEO of LifeLabs Learning, where she skilled up over 350,000 leaders across the world. She also lives with 40 rescue animals.

She has used AI daily since 2022 and regularly encourages advocates to lean on it as a resource to save time and prevent burnout. Scarlet Spark also helps advocates (any time, anywhere) with our AI bot, Sparky.

This program came together thanks in no small part to the efforts of Deep Dhillon, Managing Director at Mercy for Animals. Deep helped bring the instructors together, secure the necessary support, and shape the collaboration that made Amplify for Animals possible.

Amplify for Animals

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FAQ

This document provides in-depth answers and perspectives on key questions and operational considerations for the Amplify for Animals training program. It is intended to facilitate a shared understanding and guide the final stages of program design.

Section 1: Program Goals, Vision, and Success Metrics

What is the core goal of this program, and what does success look like?
The primary goal is to accelerate education and effective adoption of AI tools across the animal protection movement. Success will be measured by both quantitative and qualitative metrics. For participants, success is increased AI literacy and self-reported productivity gains. For the movement, success is a demonstrable increase in the number of organizations using AI strategically, leading to greater overall impact for animals. The long-term vision is to create a self-sustaining ecosystem of AI-enabled advocates.
How will we define and measure success and collect feedback?
We will establish several feedback loops to define and measure success:

Pre- and Post-Program Surveys: To measure changes in AI literacy, confidence, and self-reported productivity.

Use Case Submissions: Encourage participants to submit their own AI use cases, which will serve as a valuable metric for adoption and a source for future content.

Dedicated Feedback Channels: Set up Slack channels or a simple forum for participants to ask questions, provide feedback, and share their progress in real-time.

Section 2: Curriculum and Content Specifics

How will we handle the different platforms (e.g., Gemini vs. ChatGPT) without getting bogged down in tool-specific details?
The program’s foundation will be largely platform-agnostic, focusing on universal concepts like effective prompting, data privacy, and ethical considerations. The core philosophy is that these skills are transferable, and participants can apply them to whatever tool their organization approves or provides. As we get into advanced use cases, there may be tool- and platform-specific trainings if they are relevant for roles or use cases. Team-GPT will be provided to participants for the duration of the training to allow everyone to access common tools for learning purposes.
What does the program offer for more advanced participants?
The program is designed primarily as a foundational training for people newer to AI, so advanced participants may find less brand-new material tailored specifically to them. That said, we expect the majority of the content to be valuable for everyone, regardless of prior experience. For those looking to go deeper after the program, there will be additional opportunities such as AI Impact Hub and the Code for Compassion hackathons. These post-program offerings are where more advanced, project-based exploration and leadership opportunities will happen.
How will we ensure the content is valuable for both junior and senior staff?
The program is structured to provide a strong foundation that applies across all levels. Junior staff will gain practical skills to improve their day-to-day work, while senior staff and leaders will learn how to integrate AI into strategy, decision-making, and organizational culture. We believe it is just as important for leaders to actively use AI as it is for frontline staff, and the content has been designed to reinforce this across the board.

Section 3: Logistics, Operations, and Technical Infrastructure

How will we manage registration and track attendance for the main program?
Registration will be handled through Zoom, with attendance automatically tracked via Zoom’s reporting features. The main entry point for the program will be amplifyforanimals.org, where participants can sign up and access program resources.
Will session recordings be available after the program?
Core workshops will be recorded and shared so participants can catch up or revisit content. Our current plan is to keep these recordings available after the program as an ongoing resource for future staff and hires, though final details are still being worked out. Side Labs, Peer Learning Meetups, and Office Hours will not be recorded, to keep them candid and open. We strongly encourage live attendance at all sessions — we think participants will learn far more in real time, while recordings tend to be watched less often.
What is our plan for a community platform to facilitate peer-to-peer learning and support?
The Hive Slack workspace will serve as the main hub for communication, peer support, and Q&A. Participants will be encouraged to join AI-focused channels — #s-ai-tools, #c-ai-discussion, and #s-ai-creativity — to share tips and resources. Educators and power users will guide discussion, but the aim is a self-sustaining community. We will also encourage organizations to create internal AI channels and add AI check-ins to team meetings, so learning continues within each org. Participants can join Hive at joinhive.org.
How will we manage technical support during the program?
We will not have a dedicated full-time support team; instead, support will be community-based. Participants can post questions on the Hive Slack, where educators and peers will respond. In addition, weekly office hours and side labs (offered in multiple time zones) will give participants live opportunities to troubleshoot and get help. We will also encourage people to use AI tools themselves for technical problem-solving. A comprehensive FAQ will also be provided as a reference.
How will we handle data privacy and confidentiality for the use cases and feedback submitted by participants?
We are still finalizing how the use case library will work — it may require login or it may be publicly accessible. All submissions will include clear privacy guidance at the point of submission, and participants are expected to anonymize sensitive details or get internal approval before sharing. Feedback will be handled separately through survey tools, with responses visible only to the program team; aggregated results will be shared, and specific comments may be highlighted if participants give permission.
How will we ensure the accessibility of the program materials?
We will ensure all materials are accessible. All recorded sessions will include closed captions, and transcripts of core workshops will be made available. Documents and other materials will be designed to meet basic accessibility standards.

Section 4: Financials, Partnerships, and Post-Program Strategy

Will there be any cost to participants?
The training itself will be free for all participants from animal advocacy organizations. This is a core tenet of the collaborative approach, ensuring that resource constraints do not prevent any organization from participating.
What is our relationship to initiatives like the AI Impact Hub and Code for Compassion?
Amplify for Animals is designed as a foundational training program. For participants who want to go deeper afterward, we will point them toward complementary initiatives such as the AI Impact Hub and Code for Compassion hackathons. We see these resources as natural next steps for future learning and collaboration. Our role is to prepare participants with core skills and then connect them to these broader opportunities where they can continue growing and contributing.
How will we help our audiences understand how this program fits into the long-term vision of AI-enabled animal advocacy?
Amplify for Animals is intended as a first step: a foundational training that equips advocates with the core skills to begin using AI responsibly and effectively. We will share a clear roadmap that shows how this program connects to the broader AI ecosystem in animal advocacy and how it supports future initiatives. This framing will help funders, partners, and participants see the program’s role in building momentum toward more advanced projects and sustained, long-term impact.

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