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AI Powered Futures — Cohort 1 Summary Report
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AI Powered Futures
CILAR  ·  Cohort 1  ·  Summary Report
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AI Powered Futures: Cohort 1 Summary Report
Prepared by Wiz Learning
Cohort 1 Complete March 18 – May 6, 2026 8 modules · 8 weeks Blended delivery
Program overview
AI Powered Futures is an 8-week applied AI upskilling program developed by Wiz Learning for the Centre for Innovation and Leadership in Retail (CILAR), in partnership with Toronto Metropolitan University's G. Raymond Chang School of Continuing Education. The program builds practical AI literacy for retail and customer-facing professionals, with a focus on responsible use, applied decision-making, and human oversight of AI tools. Completion of the program supports eligibility for the CURV 700: Applied AI for Operational and Service Excellence microcredential from TMU.
  • Session 1 held in person at TMU; all subsequent sessions delivered online
  • Each session ran 90 minutes with an optional 30-minute office hour
  • Lead facilitation by Shawna M., with co-facilitation by Merveille M. (Modules 1 and 3) and Ryan P. (Modules 4 and 8)
Cohort profile
25 learners registered. One withdrew before completion, bringing final enrollment to 24. While designed for retail and customer-facing workers, Cohort 1 reflected a broader professional range.
  • Approximately 52% came from corporate and office roles
  • Remaining learners from in-store retail, warehousing, banking, financial services, nonprofits, hospitality, and government
  • All 24 learners had prior AI tool experience before the program began
  • 75% arrived with a positive outlook on AI at work
  • Most common AI uses at program start: drafting messages or emails (22/24), brainstorming and research (17/24 each), organizing information (15/24)
Attendance and engagement
Avg attendance
81%
~19 of 24 per session
Peak attendance
96%
Weeks 2 and 5
Avg engagement composite
3.98/5
Across all 8 weeks
Usefulness rated 4 or 5
84%
Of 69 total responses
Weekly attendance (out of 24)
Peak Standard Below avg
Wk1:20, Wk2:23, Wk3:21, Wk4:18, Wk5:23, Wk6:19, Wk7:15, Wk8:16
Weekly engagement composite (out of 5)
Composite score
Wk1:4.45, Wk2:3.58, Wk3:3.62, Wk4:3.36, Wk5:4.26, Wk6:4.06, Wk7:4.25, Wk8:4.24
Assessment and knowledge growth
🎓
TMU CURV 700 microcredential results
Applied AI for Operational and Service Excellence
16
Enrolled in pathway
10
Submitted
9
Confirmed passes
1
Result pending
Among the 6 learners who completed both pre-program and post-program surveys, the following gains were recorded:
AI confidence before and after (out of 10)
Before After
Before: 6.6, After: 8.5
Concept familiarity gains (out of 4-point scale)
Point gain
Prompt eng: +1.4, Data privacy: +1.5, Bias and fairness: +1.5
Self-paced completion (Disco)
Completion held strong through the first two modules then declined steadily across the back half of the program.
Module completion rate
Strong (75%+) Moderate Declining Low
M1:83%, M2:79%, M3:62%, M4:58%, M5:54%, M6:42%, M7:29%, M8:29%
Program highlights
Framework-based learning was the program's clearest strength. Learners referenced specific named frameworks repeatedly and unprompted in post-program reflections, with at least one learner applying the AI Decision-Making Matrix to a real workplace situation before the program concluded. The content translated effectively across professional backgrounds beyond retail, with learners from warehousing, banking, hospitality, and government all describing direct workplace relevance.
AI Decision-Making Matrix DECIDE Check AI Responsibility Gate Prompt engineering framework
"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 L. — Nonprofit and Hospitality
"I have developed the habit of always reviewing AI-generated output to ensure it is accurate, appropriate, and aligned with the situation."
Shauna E. — Banking and Financial Services
"At the beginning I would consider myself a beginner. Now I have intermediate knowledge."
Tyler C. — Retail
Areas for improvement
  • Self-paced completion declined steadily from 83% in Module 1 to 29% by Modules 7 and 8, suggesting learners deprioritized asynchronous work as live sessions progressed. Additional strategies for sustaining self-paced engagement in the second half of the program are worth exploring for future cohorts.
  • Attendance declined in the final two sessions, both carrying significant content including the microcredential preparation lab and program wrap-up. This pattern warrants monitoring in future cohorts given the importance of that material.
  • Week 4 recorded the lowest engagement composite at 3.36. The responsible and ethical AI content in that module is central to the program's framework, and the format or pacing of that session warrants review for future iterations.
  • A significant portion of Cohort 1 came from outside retail, and retail-specific scenarios did not always connect with learners in banking, government, warehousing, and other sectors. The facilitation team adapted many scenarios in real time to broaden their applicability, which improved relevance and engagement. For future cohorts with similarly mixed professional backgrounds, building in role-flexible scenario options from the outset would reduce facilitation load and strengthen learner connection to the material from the start.
About Wiz Learning
Wiz Learning is an AI-powered education technology company that helps schools, nonprofits, workforce agencies, and organizations design engaging, scalable, and personalized learning experiences. Supporting more than 9,000 learners across 30 countries, Wiz Learning partners with institutions to build future-ready programs rooted in equity, accessibility, and real-world outcomes. The company has been featured in Forbes, recognized as a Columbia University EdTech Fellow, spotlighted by Stanford University's Graduate School of Business, and backed by funders including Google, the Cartier Women's Initiative, and ECMC. AI Powered Futures was developed and delivered by Wiz Learning on behalf of CILAR in partnership with Toronto Metropolitan University's G. Raymond Chang School of Continuing Education.
This report was prepared by Wiz Learning. Data drawn from weekly learner feedback forms, instructor session reports, post-program reflection surveys, and program tracking records. Cohort 1 concluded May 6, 2026. Microcredential results reflect status as of report preparation date.

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