35 minutes • Create engaging, practical training that builds real competence
Your team members are adults with extensive work experience. Adult learning principles differ from child learning. Adults are self-directed—they want to understand why they're learning. They learn through experience and problem-solving, not passive listening. They're motivated by relevance to their work. Effective training honors these principles.
Adults learn best when they direct their own learning. Rather than mandatory lectures, offer choices: "You can take the Claude fundamentals workshop or self-study with this guide." "You can learn in a group session or one-on-one coaching." Providing autonomy increases engagement. People commit more to learning they chose.
Adults bring experience to learning. Effective training connects new knowledge to existing experience. "You already know grant writing. Here's how AI changes that." This builds on strengths rather than starting from scratch. Use examples from your organization's actual work. Practice with real grant challenges.
Adults learn best when solving problems that matter to them. Rather than abstract AI concepts, train using real scenarios: "You need to write a proposal section. Let's use Claude together." "You found funder research from AI. How do you evaluate it?" Problem-focused training keeps people engaged.
External rewards (certificates, badges) matter less than intrinsic motivation. People are more motivated by autonomy, mastery, and purpose. Connect training to purpose: "Learning these skills helps us serve our mission better." Support mastery by providing feedback and celebrating progress. Respect autonomy in how people learn.
Adults must understand why they're learning something. Never start with concepts before establishing relevance. Start with a real problem: "Here's a proposal we're drafting. Normally this takes 20 hours. With AI, it takes 8. Here's how." Then teach the concepts. Relevance established, learning happens.
Design training in modules, not monolithic programs. A foundations module covers AI basics. A Claude module teaches Claude specifically. A writing module shows AI use in proposal writing. A research module covers AI research tools. Modules can be taken independently, in sequence, or customized to roles.
Modularity allows flexibility: experienced users skip foundations. Writers focus on writing-relevant modules. Researchers skip writing modules. People learn what they need without sitting through irrelevant content. Modules should be 30-90 minutes—consumable in one session.
Design progression from foundational to advanced. Module 1 is "What is AI?" Module 2 is "How to use Claude." Module 3 is "Advanced prompting techniques." Module 4 is "Prompt engineering for grant writing." Each builds on previous ones. People starting together progress together, or people can join at appropriate levels.
Combine different learning modalities. Some people learn well from videos. Others need hands-on labs. Some prefer reading. Others want group discussions. Blended approaches serve diverse learners. Offer: video tutorials, written guides, hands-on exercises, group workshops, one-on-one coaching, peer learning groups. Let people choose what works.
Learning by doing is most effective. Every training session should include hands-on exercises. In a Claude fundamentals workshop, participants write actual prompts in Claude, not just listen to descriptions. In a writing workshop, people draft proposal sections with AI, not just hear about it. Exercises cement learning.
Content: What is AI? How do large language models work? What are capabilities and limitations? What are common misconceptions? What are ethical considerations? Duration: 60 minutes. Format: video or in-person presentation with Q&A. Exercise: interact with Claude to see capabilities firsthand. Assessment: quiz on key concepts.
Create modules for each tool your team uses. Claude module covers: creating account, interface overview, how to write effective prompts, understanding and critiquing outputs, common use cases in grant work. Duration: 90 minutes. Format: instructor demo with live Claude, then hands-on exercises. Participants practice writing prompts and interpreting responses.
Writing module shows: how to use Claude for brainstorming, drafting sections, improving clarity, addressing reviewer feedback. Participants practice with sample proposals. Researcher module shows: evaluating AI-researched information, combining AI output with authoritative sources, avoiding common pitfalls. Participants practice with sample research tasks. Role-specific training is highly relevant.
For people wanting deeper expertise: Prompt engineering covers designing sophisticated prompts that produce excellent outputs. System design covers building multi-tool workflows with AI. Critical evaluation covers deep assessment of AI outputs. These are optional, for people interested in advanced capabilities.
Workshops allow interaction, live Q&A, hands-on practice with support. Ideal for foundational topics and team building. Drawbacks: require scheduling everyone simultaneously, less flexible. Run quarterly workshops so people can join when ready. Keep them to 90 minutes maximum—shorter is better than longer.
Videos, written guides, interactive exercises available on demand. People learn when convenient. More accessible than scheduled workshops. Drawback: less interactive, lower completion rates. Combine with check-ins: "Have you completed the Claude module?" Support self-paced learning with discussion forums where people can ask questions.
Individual meetings help struggling learners or advanced learners wanting personalization. Coach meets with coachee, assesses their level, identifies needs, provides tailored guidance. Very effective but time-intensive. Use for people with special needs or wanting advanced expertise.
Create spaces for peer learning: lunch-and-learn sessions where people share how they use AI, discussion groups discussing AI topics, mentorship pairing advanced users with beginners. Peer learning builds community and often addresses practical challenges faster than formal training.
Assess understanding while training is ongoing. Quizzes check knowledge. Exercises reveal capability. Discussions expose misconceptions. Use formative assessment to adjust training: if many people struggle with prompting, spend more time there. If people master concepts quickly, move faster. Assessment guides instruction.
At module completion, assess mastery. Quiz on key concepts. Practical exercise demonstrating skills. For successful completion, issue badges or certificates. Certification provides motivation and documents competence. Keep assessment practical: can people actually do the work? That's what matters.
After training, collect feedback. What worked well? What could improve? Was content relevant? Did you learn what you hoped? Use feedback to refine training. Good trainers continuously improve based on learner feedback.
Don't try to design perfect curriculum from the start. Pilot with a small group. Teach the module. Collect feedback. Refine. Teach again with improvements. After three iterations, curriculum is much stronger than initial design. Iterative improvement beats trying to plan everything perfectly upfront.
Don't train everyone simultaneously. Phase rollout: Month 1 train managers and early adopters. Month 2 train writers. Month 3 train researchers. Month 4 train administrators. Phased approach prevents chaos, allows refinement, lets early adopters support later groups.
AI tools evolve. New features emerge. Keep training current. Quarterly updates cover new capabilities. Monthly lunch-and-learn sessions share tips and tricks. Ongoing learning embeds continuous improvement culture.
Grant work is urgent. Finding time for training is hard. Solution: integrate training into work. "This afternoon we're learning Claude by using it on the grant we're drafting." Training becomes productive work, not separate activity. This is more efficient and relevant.
Some people resist training. Solution: voluntary workshops plus incentives. Make training genuinely useful. Share early success stories. Recognize people adopting tools. Incentivize participation. People engage when they see value.
Some people advance quickly; others lag. Solution: individualized paths. People progressing quickly can take advanced modules. People struggling get additional support. Don't force everyone through same timeline.
Next, we'll explore managing resistance to AI adoption and helping skeptical team members embrace new tools.
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