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AI Powered Futures — Cohort 3 Summary Report
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AI Powered Futures
CILAR  ·  Cohort 3  ·  Summary Report
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AI Powered Futures: Cohort 3 Summary Report
Prepared by Wiz Learning
Cohort 3 Complete April 28 – June 4, 2026 8 modules · 8 weeks Virtual 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 TMU microcredential pathway.
  • All 8 sessions delivered online
  • Each session ran 90 minutes with an optional 30-minute office hour
  • Cohort 3 was a mixed group spanning retail, tech, hospitality, consulting, and career transition roles
  • Lead facilitation by Shawna M., with co-facilitation by Ryan P. across all modules
Cohort profile
17 learners enrolled at the start of the program. Enrolment settled at 15 by Week 4 as two learners withdrew in the early weeks. Cohort 3 was the most industry-diverse group across the pilot, with tech professionals making up the largest single cluster, alongside retail associates, corporate office roles, job seekers in career transition, hospitality workers, and professionals from consulting, banking, government, and logistics.
  • All 11 baseline respondents had used AI tools before the program began, with an average pre-program knowledge score of 5.5 out of 6
  • 73% arrived with a positive outlook on AI at work; 18% saw it as useful but were unsure how to apply it; 9% felt somewhat nervous
  • Most common AI uses at program start: writing messages or emails and research (8/11 each), brainstorming (7/11), customer communication (6/11)
  • The cohort's most common challenge before starting was knowing when to trust an AI output and when to question it, a concern that maps directly to the program's human-in-the-loop framing
Attendance and engagement
Attendance was Cohort 3's most significant structural challenge across the program. The 8-week average of 65.6% was below the targets set in Cohorts 1 and 2. Only Weeks 1 and 3 exceeded 80%. Week 4 was the program low at 47%, and both Weeks 7 and 8 closed at 53%.
Avg attendance
65.6%
Across all 8 weeks
Peak attendance
88%
Week 1 (15/17)
Avg engagement composite
3.75/5
Across all 8 weeks
Usefulness rated 4 or 5
53%
Of 47 total responses
Weekly attendance
Strong (80%+) Standard Below avg
Wk1:15/17 88%, Wk2:11/16 69%, Wk3:13/16 81%, Wk4:7/15 47%, Wk5:10/15 67%, Wk6:10/15 67%, Wk7:8/15 53%, Wk8:8/15 53%
Weekly engagement composite (out of 5)
Composite score
Wk1:3.77, Wk2:4.45, Wk3:3.49, Wk4:3.30, Wk5:3.44, Wk6:4.24, Wk7:4.30, Wk8:2.98
The engagement pattern across Cohort 3 shows two distinct peaks at Week 2 (4.45) and Week 6 (4.24), with lower scores in the middle and final sessions. Week 2's strong showing was driven by full instructor engagement and rich real-world examples from learners. Week 6's recovery reflected excellent group work and scenario activity that the instructors described as the best of the program. Week 8 recorded the lowest composite at 2.98, partly due to very low student response counts in the final two weeks. Weeks 7 and 8 had only 2 and 3 learner responses respectively, which limits the reliability of those scores.
Assessment and knowledge growth
🎓
TMU microcredential pathway
Applied AI for Operational and Service Excellence
10
Enrolled in pathway
—
Results pending
Knowledge check performance was strong among the learners who completed the assessments. The mid-program knowledge check, completed by 7 learners, averaged 9.57 out of 10 (96%). The end-of-program knowledge check, completed by 5 learners, averaged 13.4 out of 16 (84%). These are different instruments assessing different content and are not directly comparable, but both reflect solid understanding of the key frameworks.
Knowledge check performance
Mid-program (7 learners) End-of-program (5 learners)
Mid: 96%, End: 84%
Self-paced module completion rate
Strong Moderate Low
M1:60%, M2:60%, M3:53%, M4:53%, M5:47%, M6:47%, M7:27%, M8:27%
Self-paced completion followed the pattern seen in previous cohorts, with Modules 1 and 2 at 60% and gradual decline through the program, closing at 27% for Modules 7 and 8. Completion was somewhat lower across the board than Cohorts 1 and 2, consistent with the cohort's lower live attendance overall.
Program highlights
Despite lower attendance overall, the learners who consistently showed up engaged meaningfully with the material. The program's central message about human judgment in AI-supported work was still being articulated clearly by learners in the final session, which is the clearest evidence that the framing landed.
  • Week 6 was the standout session of the program, producing the highest composite score at 4.24 and the strongest group work of any week. Instructors noted excellent discussion throughout and no suggestions for improvement
  • Module 3 produced a notable signal of real-world transfer: two learners opened the session by sharing they had already started using AI at work as a result of earlier modules
  • Module 4's standout moment came when one group concluded AI was not the right tool for their scenario at all — applying critical judgment rather than defaulting to use
  • Module 7 worked well in its individual-or-group format, with the realistic document-style scenario generating a meaningful discussion about data privacy when uploading documents to AI tools
  • The AI Decision-Making Matrix, prompt engineering framework, and DECIDE Check were the most frequently cited frameworks in learner feedback across the program
AI Decision-Making Matrix DECIDE Check AI Responsibility Gate Prompt engineering framework
"Human is always the final responsible person, not AI. I still have to use my knowledge to judge the answer given by AI."
Module 8 — Program wrap-up
"I was hesitant using AI and didn't know much how to use it effectively, but now I feel more comfortable and more ready to use it efficiently."
Erim Y. — Post-program reflection
"I was comfortable with AI prior to this program. I am still comfortable with AI but now I am more aware and intentional. Now, I use my human judgment to determine if it is necessary to use AI to help me complete a task."
Ephrata G. — Post-program reflection
"Making sure to revise and use my critical thinking skills, and judgment, lead with empathy whenever I work with AI."
Caitlin M. — Post-program reflection
Areas for improvement
  • Attendance averaged 65.6% across the program and dropped to 47% in Week 4 and 53% in both Weeks 7 and 8. No reasons for absences were documented in any week. A brief check-in process for absent learners, combined with stronger advance communication about the importance of the final sessions, would help sustain presence through completion in future cohorts.
  • Learner feedback response rates were very low in the second half of the program. Weeks 3, 7, and 8 had between 2 and 4 responses from groups of 8 to 13 attendees. Building the feedback form into the final 5 minutes of every session as a required close, rather than a post-class ask, would produce more complete data.
  • Self-paced completion closed at 27% for Modules 7 and 8, meaning many learners went into the microcredential assessment without completing the designed scaffolding for those weeks. Earlier, structured reminders tied explicitly to assessment requirements should be a priority for future cohorts.
  • The Module 8 wrap-up session received the lowest content support rating of the program, with both instructors rating the session content at 3/5. The structure of the final session warrants a refresh before the next cohort, with more time built in for reflection, questions, and a clearer path to the microcredential process.
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 3 concluded June 4, 2026. Microcredential results reflect status as of report preparation date.

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