Program overview
Weeks 1 and 2 complete · Last updated April 1, 2026
Avg engagement score
3.9 /5
Instructor + student avg
Avg attendance rate
88%
Across Weeks 1 and 2
Feedback responses
22
Across 2 modules
Attendance chart
84%
Week 1
21/25
92%
Week 2
24/26
—
Week 3
upcoming
—
Week 4
upcoming
—
Week 5
upcoming
—
Week 6
upcoming
—
Week 7
upcoming
—
Week 8
upcoming
Instructor engagement ratings — Week 2
Student self-ratings
6 Week 1 responses · 16 Week 2 responses
What's resonating — student learning moments
The AI Decision-Making Matrix was the single most cited learning across both weeks — mentioned independently by multiple students in their own words. Across both modules, students are connecting course content to real workplace situations.
"AI can and will not replace humans and human judgment; humans will be working alongside it."
Week 1 · Module 1
"The prompts you use need to be very specific at times if possible to get the best responses."
Week 2 · Module 2
"I learnt how I can apply AI to my everyday work to ease friction."
Week 2 · Module 2
Building toward a TMU microcredential
Every module in AI Powered Futures is designed to build the applied AI competencies required for TMU's microcredential assessment. Learners are developing skills in prompt construction, output evaluation, human-in-the-loop decision-making, and responsible AI use — with two pathways available at completion: one for frontline retail roles, one for supervisory and leadership contexts.
Module log
Session topics, takeaways, and student ratings
1
AI Foundations for Retail Work
Key takeaways: AI supports work but doesn't remove human responsibility. Building AI awareness increases confidence and career readiness.
★★★★☆
4.4 student avg
2
Using AI to Reduce Friction in Everyday Retail Work
Key takeaways: The AI Decision-Making Matrix helps evaluate when and how to use AI. Human review and oversight remain essential.
★★★★☆
3.8 student avg
3
Evaluating AI Outputs in Customer-Facing Situations
Not yet run
4–7
Modules 4–7
Not yet run
Attendance
Live session presence across the 8-week program
Total enrolled
26
+1 added in Week 2
Avg attendance rate
88%
Across Wk 1 + Wk 2
Attendance trend
↑ +8%
84% in Week 1, 92% in Week 2
Weekly attendance log
Week 1
21/25
84%
Week 2
24/26
92%
Week 3
—
—
Week 4
—
—
Week 5
—
—
Week 6
—
—
Week 7
—
—
Week 8
—
—
Notes
Week 2 enrolment increased by 1 student (25 to 26), reflecting continued interest in the program. Attendance improved from 84% to 92% week over week — a strong early indicator of learner commitment.
Engagement
Composite score from instructor and student ratings
Composite score — Week 1
4.3 /5
Student ratings only
Composite score — Week 2
3.6 /5
Instructor + student avg
Overall program avg
3.9/5
Across both weeks
How the engagement score is calculated
Instructor rating
Overall engagement + comfort participating (1–5)
Weight: 40%
Student usefulness
How useful did you find today's class? (1–5)
Weight: 35%
Student understanding
Class helped me understand AI in real work (1–5)
Weight: 25%
Week 2 instructor observations — Shawna
The AI Decision-Making Matrix resonated strongly. Multiple students identified it as a standout moment.
Two groups gave very detailed activity share-backs, showing strong engagement in the small group format.
Concept understanding was rated positively. Students demonstrated a good grasp of the session's key ideas.
Session pacing was well-received. The flow of content supported learner engagement throughout.
Student voice
What students are learning and what they want next
Usefulness rating distribution — Week 2 (16 responses)
0
1
0
2
4
3
9
4
3
5
No student rated the session below 3. The majority rated it 4 out of 5.
What students want to learn more about
Prompt engineering
AI ethics
AI tools by use case
Agentic AI
AI Decision Matrix
AI hallucination
Data privacy
Agentic workflows
Ethical use of AI
Direct quotes from student feedback
"AI can and will not replace humans and human judgment; humans will be working alongside it."
Week 1 · Module 1
"The prompts you use need to be very specific at times if possible to get the best responses."
Week 2 · Module 2
"I learnt how I can apply AI to my everyday work to ease friction."
Week 2 · Module 2
"The need for human review and oversight in AI generated content to ensure accuracy and relevance."
Week 2 · Module 2
"I learned that AI has been used in retail and businesses in the early 2000, way before anyone used ChatGPT or Gemini."
Week 1 · Module 1
Self-paced completion
Learner progress through the Disco self-paced modules
Module 1 completions
16/26
62% of learners completed
Module 2 completions
9/26
35% of learners completed
Total enrolled
26
Current cohort size
Completion by module
Module 1
16/26
62%
Module 2
9/26
35%
Module 3
—
—
About self-paced learning
After each live class, learners complete a corresponding self-paced module on the Disco platform. These modules include video content, knowledge checks, and applied exercises that reinforce what was covered in the session. Module 2 completions are expected to grow over the coming days as learners work through the content at their own pace.
Cohort profile
Who is in the room and why that matters
Broader than retail: a signal of program relevance
While AI Powered Futures was designed for retail and customer-facing workers, Cohort 1 reflects a wider range of professionals seeking applied AI skills. This points to the program's relevance beyond a single sector, pointing to demand for practical, workplace-grounded AI education across industries.
Retail & customer serviceFrontline and customer-facing roles
Banking & financial servicesIncluding loan officers and financial advisors
Government & policyIncluding federal policy analysts advising on AI governance
ConsultingClient-facing professionals at global firms
Accounting & professional servicesExploring AI for workflow and client work
Data & technologyProfessionals transitioning into data-adjacent roles
What learners are bringing to the room
Many participants are already working alongside AI tools in their roles — including Microsoft Copilot, internal chatbots, and generative AI platforms. They are not beginners in a technical sense. They are experienced professionals looking to deepen their understanding, apply AI more deliberately, and build the judgment to use it responsibly. This makes the program's human-in-the-loop and responsible AI framing especially well-matched to the cohort.
TMU microcredential pathway
All learners are building toward a TMU microcredential at program completion. Two pathways are available: one for frontline roles, one for supervisory and leadership contexts. Both require demonstrated applied AI competency — not just participation.
Insights
What the data tells us through Week 2
Attendance is strong and growing. 84% in Week 1, 92% in Week 2 — trending up. Enrolment also grew by 1 student between sessions, reflecting continued interest in the program.
The AI Decision-Making Matrix is landing. It was the most cited learning moment across both weeks, mentioned independently by multiple students in their own words — a strong signal that this framework is genuinely useful to learners.
Students are connecting content to real work. Feedback consistently shows learners applying concepts to their own roles — from reducing friction to understanding when human oversight is required.
No student rated either session below 3. Across 22 feedback responses, the floor of usefulness ratings was 3 out of 5 — with the majority of Week 2 responses at 4.
Top interest areas align with upcoming content. Students are most curious about prompt engineering, AI ethics, and agentic AI — all of which are addressed directly in Modules 3 and beyond.
Cohort diversity signals broader program relevance. Participants from banking, government, consulting, and accounting point to demand for applied AI education well beyond the retail sector.