AI for Career Growth

Understand how AI is changing tasks in your area and separate evidence from hype.

• Tasks versus jobs: how AI changes work activities
• Finding credible reports and sources on your field
• Using Perplexity and Gemini to research industry trends with citations
• Spotting exaggerated claims about AI and employment
• Identifying tasks in your role that are changing
• Setting realistic personal goals

Hands-on lab: Research your field with two tools, verify five claims against original sources, and write a one-page landscape note.

Turn role requirements into a concrete, checkable learning plan.

• Collecting and comparing job descriptions
• Extracting skills, tools, and responsibilities
• Separating essential from optional skills
• Choosing learning resources and checking their quality
• Weekly milestones and evidence of progress
• Using an assistant as a study partner without outsourcing learning

Hands-on lab: Analyze three role descriptions, build a skills matrix, and draft a 90-day plan with milestones you can demonstrate.

Improve how you present your experience while keeping every claim true.

• Turning duties into specific, evidence-based achievements
• Tailoring content to a role description
• Checking AI edits for invented details
• Keeping your own voice and avoiding generic phrasing
• Protecting personal data in drafts you share with tools
• Applicant-system realities and employer views on AI use

Hands-on lab: Revise your résumé and profile with an assistant, then create a line-by-line accuracy check confirming each claim.

Use assistants as practice partners for interviews, presentations, and difficult workplace conversations.

• Role-play prompts for interviews and negotiations
• Structuring answers to behavioral questions
• Drafting professional emails and networking messages
• Receiving and applying feedback from an assistant
• Limits of AI feedback and when to ask a person
• Bias and cultural fit in AI-generated advice

Hands-on lab: Run three mock interview rounds with an assistant, record feedback, and revise your answers each round.

Create visible proof of skill through a small, well-documented AI-assisted project.

• Choosing a portfolio project that reflects real tasks
• Documenting process, prompts, and verification steps
• Presenting results in clear one-page formats with Canva
• Crediting sources and disclosing AI assistance
• Removing confidential employer or client information
• Sharing work through professional channels

Hands-on lab: Start a portfolio piece from a real or realistic task, and write a process note with prompts, checks, and corrections.

Build habits for ethical, sustainable use of AI across your career.

• Explaining how you use AI at work, clearly and honestly
• Employer policies and data-handling expectations
• Verification habits for professional output
• Staying current with a manageable learning routine
• Building a peer network and feedback loop
• Reviewing and updating your plan quarterly

Hands-on lab: Write a short responsible-AI statement for your own practice and present your portfolio piece and updated plan to the cohort.