RDP joins AI Workforce Readiness program
Red Deer Polytechnic has joined the AI Workforce Readiness (AIWR) program, partnering with Amii's machine learning educators to build open-source Curriculum Resource Packages. The goal is simple: give instructors ready-to-use materials that fold practical AI skills into existing courses without starting from zero.
These resources are built for adoption across programs-business, health, trades, arts, and more-so faculty can plug in AI literacy where it fits and keep academic outcomes front and center.
What the curriculum packages include
- Reading guides and discussion prompts you can drop into weekly modules
- Real-world use cases and case studies that connect theory to practice
- Short videos and activities to build concept fluency
- Hands-on labs and assessments to measure applied skills
- Open-source materials for easy adaptation across courses
Why this matters for educators
AI is now a cross-discipline skill set. Programs that weave in core concepts-data literacy, responsible use, and basic model workflows-give students an edge in internships and first jobs.
For faculty, the lift is lighter. Instead of building from scratch, you get structured content mapped to real use cases. That saves prep time while keeping academic quality intact.
As Amii's CEO Cam Linke puts it: "Amii's goal has always been to make AI education accessible, and the AI Workforce Readiness program is the culmination of that commitment. We are excited to work with our inaugural group as part of a growing national network. By fostering a strong national collaboration, we are directly addressing the urgent AI skills gap and empowering both instructors and students-especially those in underserved communities-with the necessary AI literacy skills and resources. This foundational work ensures that post-secondary students across the country are equipped for the future of work."
How to use these resources on your campus
- Map modules to current learning outcomes before adding new assessments.
- Start with one anchor course per program (e.g., intro, methods, capstone) to pilot.
- Run a low-stakes lab first, then scale to graded projects once workflows are clear.
- Host a short faculty session to align on AI policy, academic integrity, and tooling.
- Collect quick feedback from students and instructors after week two and at term-end.
- Share adaptations back to the consortium so others can benefit.
What this means for students
- Clear expectations for where and how AI tools can be used in coursework
- Practice with real scenarios relevant to their field-beyond generic prompts
- Assessment that rewards process, not just outputs
Program leader checklist
- Identify 2-3 priority courses per department for AI integration.
- Confirm your stance on AI use and academic integrity with clear course notes.
- Pick one assessment to convert into an AI-supported workflow with reflection.
- Set a review date to measure impact on learning outcomes and workload.
Bottom line: This collaboration gives educators practical, classroom-ready tools to build AI literacy without derailing existing curricula.
Where to learn more
Explore the program and broader education initiatives at Amii: amii.ca.
If you're planning faculty upskilling or need course ideas by role, browse curated options here: Complete AI Training - Courses by Job.
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