Program overview
All 8 weeks complete · Last updated May 13, 2026
Avg attendance rate
81%
Across all 8 weeks
Learners enrolled
24
Final enrolment (1 dropout)
Feedback responses
68
Across all 8 modules
Post-program quiz avg
5.5/6
Up from 4.8/6 at baseline
Attendance across all 8 weeks
83%
96%
88%
75%
96%
79%
62%
67%
Wk 1
20/24
Wk 2
23/24
Wk 3
21/24
Wk 4
18/24
Wk 5
23/24
Wk 6
19/24
Wk 7
15/24
Wk 8
16/24
Instructor engagement ratings: Week 8 (Ryan + Shawna)
Shawna: slightly slow. Ryan: slightly fast. Both rated engagement 4/5 and understanding strong.
Student self-ratings: final week
The program closed on a strong note from learners. 5 of 7 Week 8 respondents rated usefulness at 5/5. No rating below 4 recorded in the final session.
7 responses · Week 8
How learners described their growth: in their own words
Six learners completed the post-program reflection survey. Across their responses, a consistent pattern emerged: learners arrived with some familiarity but left with structure, intention, and a vocabulary for using AI responsibly at work.
"I was often just plugging things in without fully understanding how to use AI effectively, thoughtfully, or responsibly. Now I feel more intentional and confident."
Melody Li · Post-program reflection
"This course has really provided a practical lens with how to use AI in the workplace. The importance of using AI responsibly has been highlighted throughout."
Tamara Shelly · Post-program reflection
"One student mentioned they had already used the matrix to reflect on a situation at work that required use of AI."
Ryan Pike · Week 8 instructor observation
Program complete — TMU microcredential pathway open
AI Powered Futures Cohort 1 has concluded. Learners who met the assessment threshold are eligible for the CURV 700: Applied AI for Operational and Service Excellence microcredential from Toronto Metropolitan University. Two pathways are available: one for frontline retail roles, one for supervisory and leadership contexts. Both require demonstrated applied AI competency, not just participation.
Module log
All 8 sessions — 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 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
Key takeaways: Writing detailed, specific prompts produces better AI outputs. Evaluating AI responses for accuracy, tone, and relevance is a core workplace skill.
★★★★☆
4.0 student avg
4
Responsible and Ethical AI Use
Key takeaways: The AI Responsibility Gate provides a structured checkpoint before applying AI. Responsible use requires protecting customer trust, fairness, and human accountability.
★★★☆☆
3.3 student avg
5
AI-Supported Workflows and Decision-Making
Key takeaways: The DECIDE Check gives learners a structured approach to AI-supported decisions. Reducing over-reliance on AI while using it to cut repetitive work requires ongoing judgment and human oversight.
★★★★☆
4.1 student avg
6
Adapting AI Skills Across Roles
Key takeaways: The four-part prompt engineering framework gives learners a repeatable structure for stronger prompts. Knowing when AI is not appropriate is just as important as knowing when it is.
★★★★★
5.0 student avg
7
Microcredential Preparation Lab
Key takeaways: The 5-step process — understand the situation, write the AI prompt, evaluate the output, write an improved response, explain the decision-making — gives learners a clear structure for the assessment. Perishable tool skills matter less than the durable judgment behind using AI well.
★★★★★
4.7 student avg
8
Program Wrap-Up
Key takeaways: The course recap was the standout moment — students commented positively on seeing the full arc of their learning and asked for a PDF of the complete presentation to keep. Keneisha's walkthrough of the microcredential and graduation process generated significant questions and enthusiasm. One student shared they had already applied the AI Decision-Making Matrix in a real situation at work.
★★★★★
4.7 student avg
Attendance
Live session presence across the full 8-week program
Total enrolled
24
Final enrolment (1 dropout)
Avg attendance rate
81%
Across all 8 weeks
Peak attendance
92%
Weeks 2 and 5 (23/24 each)
Weekly attendance log
Week 1
20/24
83%
Week 2
23/24
96%
Week 3
21/24
88%
Week 4
18/24
75%
Week 5
23/24
96%
Week 6
19/24
79%
Week 7
15/24
62%
Week 8
16/24
67%
Notes
Attendance peaked in Weeks 2 and 5 at 92% and held above 75% through Week 6. Weeks 7 and 8 both saw notable drops — 62% and 67% respectively — pulling the program average down to 81%. The dip in the final two weeks is worth examining for future cohorts, particularly as both sessions carried significant assessment and closure content. A targeted outreach before the final two sessions may help maintain presence in Cohort 2.
Engagement
Composite score from instructor and student ratings — all 8 weeks
Composite: Week 1
4.5 /5
Instructor + student avg
Composite: Week 2
3.6 /5
Instructor + student avg
Composite: Week 3
3.4 /5
Instructor + student avg
Composite: Week 4
3.0 /5
Instructor + student avg
Composite: Week 5
4.3 /5
Instructor + student avg
Composite: Week 6
4.4 /5
Instructor + student avg
Composite: Week 7
4.4 /5
Instructor + student avg
Composite: Week 8
4.3 /5
Instructor + student avg
Program avg
4.0/5
Across all 8 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%
Weeks 7 and 8 had two instructors. Ratings were averaged before applying the instructor weight.
Week 8 instructor observations: Ryan & Shawna
The full-program recap landed well. Shawna noted students responded enthusiastically to seeing the complete arc of their learning in one session and asked if they could receive a PDF of the entire presentation to refer back to. This suggests learners found genuine value in the framework-heavy curriculum and want to carry it forward.
Keneisha's microcredential walkthrough was a standout. Ryan noted having Keneisha present at the start of class to cover the microcredential and graduation process generated a lot of student questions. Bringing in a dedicated voice for the credential pathway added credibility and clarity.
Real-world application confirmed. Ryan shared that one student mentioned they had already used the AI Decision-Making Matrix to work through a situation at work. This is the clearest evidence of transfer recorded across the program.
Pacing varied by session. Shawna found it slightly slow to fill 1.5 hours; Ryan moved slightly fast through the material. Both suggested that future wrap-up sessions might benefit from an additional short activity or more deliberate reflection pauses between modules.
Breakout rooms were less effective in the final session. Shawna noted the breakout activity felt challenging and may not have been the right format for a wrap-up class. Worth reconsidering the activity structure for Module 8 in future iterations.
Student voice
What students learned, what they're taking with them, and what they'd tell someone starting the program
Usefulness rating distribution: all 8 weeks (68 responses)
0
1
1
2
10
3
32
4
25
5
84% of all ratings were 4 or 5. No ratings of 1 were recorded across any week of the program. The proportion of 5-star ratings grew steadily across the final three weeks.
Post-program reflections: what learners are taking with them
"One skill I plan to continue using is the AI output evaluation lens. Now I know to approach AI outputs much more critically — I'm more confident knowing when to trust, refine, or challenge what AI provides."
Melody Li · Post-program reflection
"I have developed the habit of always reviewing AI-generated output to ensure it is accurate, appropriate, and aligned with the situation."
Shauna Edwards · Post-program reflection
"Reviewing the responsibility matrix and being more cautious about my decision making before finalizing results."
Tamara Shelly · Post-program reflection
"One skill I plan to use is the prompt engineering framework."
Peter Guan · Post-program reflection
What stood out most: top-cited modules and concepts
"The DECIDE tool helps me decide whether I need to use AI."
Osazee Enogumengie · Post-program reflection
"I liked the AI Responsibility Gate because it demonstrated when it was appropriate to use AI compared to when it was not."
Tyler Chong · Post-program reflection
"Module 3 — this is the foundation of asking the right questions to AI."
Peter Guan · Post-program reflection
"Responsible AI use and retail workflow decision making. As we grow we need to be able to refine our workflows but at the same time maintain standards."
Tamara Shelly · Post-program reflection
What learners would tell someone starting the program
"Approach AI as a tool to support your thinking and not replace it."
Shauna Edwards
"You may have some knowledge about AI but this course expands your knowledge with ways to use AI practically and how to use it responsibly."
Tamara Shelly
"Keep up with the self-paced modules, so that when you get to the microcredential, you have a good understanding of everything."
Tyler Chong
"Get ready for some real fun and learning!"
Melody Li
"Participate in the group and in-class activities."
Osazee Enogumengie
Direct quotes from weekly feedback — selected across all 8 modules
"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
"Using AI tools can reduce repetitive tasks however over reliance on the AI tools may cause inaccuracies, which I need to be on top of by using the decision making matrix."
Week 5 · Module 5
"I learned that AI shouldn't be used in every situation, and that the four-question checkpoint is essential for deciding when it's appropriate to use it."
Week 6 · Module 6
"The 5-step process: understanding the situation, writing our AI prompt, evaluating the AI output, writing our improved response, and explaining our decision-making."
Week 7 · Module 7
"Ryan sharing the 'The Rundown AI' newsletter was very helpful."
Week 8 · Module 8
"The importance of responsible decision making."
Week 8 · Module 8
What students want to learn more about
Prompt engineering
AI tools by use case
AI ethics
Generative AI tools
Agentic AI
AI platforms by category
Creating AI agents
AI for social media and design
AI in corporate settings
Continuous improvement using AI
AI and human decision-making
Data privacy
AI governance at scale
Self-paced completion
Final learner progress across all Disco self-paced modules
Module 1 completions
20/24
83% of learners completed
Module 2 completions
19/24
79% of learners completed
Module 3 completions
15/24
62% of learners completed
Module 4 completions
13/24
54% of learners completed
Module 5 completions
12/24
50% of learners completed
Module 6 completions
10/24
42% of learners completed
Module 7 completions
7/24
29% of learners completed
Module 8 completions
7/24
29% of learners completed
Final completion by module
Module 1
20/24
83%
Module 2
19/24
79%
Module 3
15/24
60%
Module 4
13/24
52%
Module 5
12/24
48%
Module 6
10/24
40%
Module 7
7/24
29%
Module 8
7/24
29%
Final notes
Self-paced completion held strong through the first two modules — 80% and 76% respectively — then declined steadily across Modules 3 through 8. Modules 7 and 8 both closed at 29%. The drop-off pattern is consistent with what is commonly seen in blended learning programs where learners prioritize live sessions and deprioritize self-paced work in the final stretch. For Cohort 2, earlier and more frequent completion nudges, particularly between Modules 3 and 5, may help sustain momentum through the back half of the program.
Program growth
Pre-program baseline compared to post-program results — 6 respondents completed both surveys
AI confidence growth
+1.9 pts
6.6 → 8.5 out of 10
Quiz score growth
+0.7 pts
4.8 → 5.5 out of 6
Output reliability comfort
8.5/10
Post-program self-rating
Concept familiarity: before and after (0 to 4 scale)
Ratings reflect how familiar learners felt with each concept. All five areas increased. The largest gains were in the areas learners identified as most challenging at the start: prompt engineering, data privacy, and bias.
Artificial Intelligence
+0.2
Generative AI
+1.1
Prompt engineering
+1.4
Data privacy in AI
+1.5
Bias and fairness in AI
+1.5
AI confidence: before and after (out of 10)
6.6
Before
8.5
After
+1.9 point gain across 6 post-program respondents. Baseline drawn from 24 learners at program start.
Knowledge quiz scores: before and after (out of 6)
4.8
Before
5.5
After
+0.7 point gain. Post-program scores: 6, 6, 5, 5, 5, 6 across 6 respondents.
How learners described their growth: selected responses
"When I started this program, I already felt fairly comfortable using AI at work. However, I was often just plugging things in without fully understanding how to use AI effectively, thoughtfully, or responsibly. Now I feel more intentional and confident."
Melody Li · Nonprofit + Hospitality
"I started with little confidence, but now I am more confident."
Osazee Enogumengie · Warehousing
"I was very skeptical about AI and questioned how much I could trust its outputs. Now I know to approach AI outputs much more critically."
Shauna Edwards · Banking / Financial Services
"My comfort level with AI has been transformative — with each module my confidence in using the tool only increased."
Peter Guan · Customer Service
"At the beginning I would consider myself a beginner. Now I have intermediate knowledge."
Tyler Chong · Retail
A note on sample size
Growth data is drawn from 6 learners who completed both the pre-program baseline survey and the post-program growth survey. These figures reflect directional trends rather than statistically representative outcomes. Both the confidence and familiarity gains are consistent with what learners described qualitatively in their reflection responses.
Baseline data
24 learners completed a baseline survey and assessment before the program began. This data captures where learners started and serves as the benchmark for the growth data in the Program Growth tab.
Avg knowledge score
4.8 /6
Pre-program quiz avg (24 respondents)
Avg AI confidence
6.6/10
Self-rated before program start
Used AI tools before
100%
All 24 learners had prior AI experience
Likelihood to pursue AI career
8.2/10
Avg across cohort
Familiarity with AI concepts before the program (0 to 4 scale)
Prompt engineering, data privacy, and bias were the lowest-rated areas. All three saw the largest gains by program end.
Pre-program knowledge quiz scores (out of 6)
| Learner | Score | Bar |
|---|---|---|
| Tobi Lawanson | 6/6 | |
| Melody Li | 6/6 | |
| Peter Guan | 6/6 | |
| Kazandra Von | 6/6 | |
| Shauna Edwards | 6/6 | |
| Domina Chi | 6/6 | |
| Sohail Ahmed | 6/6 | |
| Ana Beatrice | 5/6 | |
| Isabella Iannone | 5/6 | |
| Diana Lubega | 5/6 | |
| Ahmed Orko Nur | 5/6 | |
| Ashbir Dhiman | 5/6 | |
| Tyler Chong | 5/6 | |
| Tiffany Chow | 5/6 | |
| Osazee Gerald | 5/6 | |
| Lavender M. Ontiriah | 5/6 | |
| Bih Mary Cheo | 4/6 | |
| Aliyah Rooplal | 4/6 | |
| Cassandra Hanchard | 4/6 | |
| Tamara Shelly | 4/6 | |
| Priya Puran | 4/6 | |
| Lavender Ontiriah | 4/6 | |
| Alex Ahabwe | 3/6 | |
| Linda Che | 2/6 |
View of AI in the workplace before the program
AI will likely make work easier
18/24
Helpful but unsure how to use it
5/24
AI seems complicated
1/24
75% of learners arrived with a positive outlook on AI at work.
How learners were already using AI before the program
Cohort profile
Who was in the room and why that matters
Role distribution (23 enrolled learners)
While AI Powered Futures was designed for retail and customer-facing workers, Cohort 1 reflected a wider range of professionals. One learner did not complete the program. Among the 23 who remained enrolled, 52% came from corporate and office roles rather than frontline retail — a finding worth considering for Cohort 2 intake and content design.
Corporate / office role
12 · 52%
Retail associate / frontline staff
5 · 22%
Supervisor / team lead
2 · 9%
E-commerce / digital marketing
1 · 4%
Other (nonprofit / government / homemaker)
3 · 13%
What learners brought to the room
Many participants were already working alongside AI tools in their roles, including Microsoft Copilot, internal chatbots, and generative AI platforms. They were not beginners in a technical sense. They were experienced professionals looking to deepen their understanding, apply AI more deliberately, and build the judgment to use it responsibly. This made the program's human-in-the-loop and responsible AI framing well-matched to the cohort — and explains why the frameworks landed as strongly as they did.
TMU microcredential pathway
All learners built toward the CURV 700: Applied AI for Operational and Service Excellence microcredential from Toronto Metropolitan University. Two pathways were available: one for frontline roles, one for supervisory and leadership contexts. Both required demonstrated applied AI competency, not just participation.
Insights
What the full program tells us — Cohort 1 complete
The frameworks worked. The AI Decision-Making Matrix, AI Responsibility Gate, DECIDE Check, and prompt engineering framework were each cited independently by learners as standout takeaways. More importantly, at least one learner had already applied the matrix to a real situation at work before the program ended. Framework-based learning transferred.
Confidence grew meaningfully. Among the 6 learners who completed both the pre- and post-program surveys, AI confidence increased from 6.6 to 8.5 out of 10 — a gain of 1.9 points. Familiarity with prompt engineering, data privacy, and bias and fairness each rose by more than 1.4 points on a 4-point scale. These were the three areas learners identified as most challenging at the start.
Facilitation adjustments compounded over time. The breakout room naming strategy introduced in Week 5, the earlier scenario placement from Week 6, and the chat encouragement and student grouping from Week 7 all tracked with improved engagement scores in the back half of the program. These are worth documenting as facilitation principles for Cohort 2.
Student-rated usefulness stayed consistently high. Across all 8 weeks, 84% of usefulness ratings were 4 or 5. No learner rated any session a 1. The program closed with 25 out of 68 total responses at 5 out of 5 — the highest 5-star count of the program came in the final three weeks.
Attendance declined in the final third of the program. After peaking at 92% in Weeks 2 and 5, attendance dropped to 76% in Week 6, 60% in Week 7, and 64% in Week 8. The drop-off is notable because Weeks 7 and 8 carried the prep lab and wrap-up content. For Cohort 2, targeted communication before the final two sessions may help sustain presence through completion.
Self-paced completion fell off steadily after Module 2. Modules 1 and 2 closed at 80% and 76%. By Module 6, completion was at 40%, and Modules 7 and 8 both finished at 29%. The pattern is consistent across the program and suggests learners deprioritize self-paced work as the course progresses. More frequent completion reminders — particularly between Modules 3 and 5 — may help in future cohorts.
Week 4 remains the lowest engagement point. The composite score of 3.0 in Week 4 was the only week that dipped below 3.5 for the program. Student ratings of 3.3 on both usefulness and understanding, combined with the lowest attendance of the first half, suggest the responsible AI content or the format of that session may need revisiting for Cohort 2.
The cohort was broader than the program's original scope. 52% of learners came from corporate and office roles rather than frontline retail. Despite this, the applied decision-making content landed across industry backgrounds — banking, warehousing, nonprofits, hospitality, and tech all appeared in weekly feedback. The program's human-in-the-loop framing proved transferable beyond retail.
Learners want to go further. Post-program interest areas point toward agentic AI, AI tools by use case, prompt engineering depth, and AI platforms by category. Several learners asked about a second iteration of the course with more tool exploration. These signals are useful for curriculum planning and for positioning a Cohort 2 or advanced pathway.