Program overview
Weeks 1 through 6 complete · Last updated May 27, 2026
Avg attendance rate
69.7%
Across Weeks 1 through 6
Learners enrolled
15
Current enrolment
Feedback responses
38
Across 6 modules
Attendance chart
88%
69%
81%
47%
67%
67%
—
—
Week 1
15/17
Week 2
11/16
Week 3
13/16
Week 4
7/15
Week 5
10/15
Week 6
10/15
Week 7
upcoming
Week 8
upcoming
Instructor engagement ratings by week
Week 1 · Shawna and Ryan · avg of both
Week 2 · Ryan · single submission
Week 3 · Shawna and Ryan · avg of both
Week 4 · Shawna and Ryan · avg of both
Week 5 · Shawna and Ryan · avg of both
Week 6 · Shawna and Ryan · avg of both
Student self-ratings by week
9 Wk 1 · 10 Wk 2 · 3 Wk 3 · 5 Wk 4 · 6 Wk 5 · 5 Wk 6 responses · Wk 5 includes one 0/5 rating from a learner whose stated interests are outside the scope of this program
What's resonating: student learning moments
Six weeks in, Week 6 was the strongest session of the program. Both instructors described it as an excellent class with no suggestions for improvement. Students applied skills from every previous module to the retail scenarios, brought in their own workplace examples, and engaged fully in group work. The recap structure gave the session a natural momentum as the program heads into its final two weeks.
"How important are soft skills in decision-making scenarios using AI."
Week 6 · Module 6
"Adaptability and transferability in using AI skills."
Week 6 · Module 6
"A workflow is a sequence of steps used to complete a task or process in the workplace. AI will help with certain steps in a workflow, but not the entire task."
Week 5 · Module 5
"Ethical risks involved with AI implementation in retail if not carefully designed."
Week 4 · Module 4
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. Cohort 3 learners are building skills in prompt construction, output evaluation, human-in-the-loop decision-making, and responsible AI use. Two pathways are 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 is already embedded in everyday life in ways many learners hadn't recognized. The group was active in chat throughout the session, which set a good tone for the cohort. Pacing was appropriate, though time ran short before the second breakout activity could be completed. One student stayed after class to continue the conversation with the instructor.
★★★☆☆
3.2 student avg
2
Using AI to Reduce Friction in Everyday Retail Work
Key takeaways: The AI Decision-Making Matrix was a clear standout. Students brought strong real-world examples of friction in retail that mapped well to the lesson plan. Breakout discussions on risk and human judgment were lively and well-applied. A small number of students confused risk levels, categorizing minor issues as high risk rather than harm or legal consequences. Ryan noted this could be addressed with a slide that more clearly illustrates the risk scale.
★★★★☆
4.0 student avg
3
Evaluating AI Outputs in Customer-Facing Situations
Key takeaways: Practical prompt examples set a clear tone for the activity and gave students something concrete to build on. Groups posted their prompts in the chat for group analysis, applied the evaluation lens to assess tone, accuracy, and appropriateness, and were able to explain their reasoning. Two students opened the session by sharing they had already started using AI at work. Ryan noted a small number of students appear to drop off early when breakout rooms open, which is worth monitoring.
★★★★☆
3.7 student avg
4
Responsible and Ethical AI Use in Retail Environments
Key takeaways: The content landed well with the students who attended. Real-world examples from the instructors helped ignite discussion around ethics in practice. One group dug into their scenario and concluded that AI was not the right tool at all for the situation, which Shawna noted as a standout moment. Students showed strong understanding of the key concepts with no major confusion flagged. Ryan noted the opening discussion questions could better frame the ethical stakes upfront, and suggested adding a short section on the environmental impacts of AI in a future iteration.
★★★★☆
3.5 student avg
5
AI-Supported Decision-Making for Retail Teams
Key takeaways: The DECIDE framework was well received. Based on what groups shared back, students applied it effectively to the breakout scenario. Two students shared real examples of how they are already using AI to speed up repetitive tasks and support decision-making at work. Ryan noted participation was lower than usual and suggested an icebreaker to boost interaction from the start. Shawna rated the session positively overall and noted the scenario was hands-on and effective.
★★★☆☆
2.3 student avg
6
Adapting AI Skills Across Retail Roles
Key takeaways: The session recapped all prior learning and gave students a chance to apply every framework from the program to new retail scenarios. Both instructors rated the lesson highly with no suggestions for improvement. Ryan noted the four questions attached to each scenario effectively guided groups through their full skill set, making it excellent prep for the Week 7 assessment. Students brought real workplace examples into the discussion and group work was described as excellent by both facilitators. Shawna noted that reinforcing the connection to the upcoming assessment helped reduce early drop-offs during the scenario activity.
★★★★☆
4.0 student avg
7–8
Modules 7–8
Not yet run
Attendance
Live session presence across the 8-week program
Total enrolled
15
Current enrolment
Avg attendance rate
69.7%
Across Weeks 1 through 6
Week 6 attendance
10/15
67% · consistent with Week 5
Weekly attendance log
Week 1
15/17
88%
Week 2
11/16
69%
Week 3
13/16
81%
Week 4
7/15
47%
Week 5
10/15
67%
Week 6
10/15
67%
Week 7
—
—
Week 8
—
—
Notes
Enrolment has decreased from 17 to 15 across the program as learners dropped. Week 1 was 15/17 (88%). From Week 2 the denominator moved to 16, then to 15 from Week 4 onward. Week 4 remains the lowest session at 47% (7/15). Weeks 5 and 6 have both held at 67% (10/15), suggesting attendance has stabilised after the Week 4 dip. The 6-week average is 69.7%. Reasons for absences have not been documented across any week.
Engagement
Composite score from instructor and student ratings
Composite: Week 1
3.7 /5
Shawna and Ryan · avg of both
Composite: Week 2
4.5 /5
Ryan only · Shawna pending
Composite: Week 3
3.7 /5
Shawna and Ryan · 3 responses
Composite: Week 4
3.1 /5
Shawna and Ryan · 5 responses
Composite: Week 5
3.1 /5
Shawna and Ryan · includes one 0/5
Composite: Week 6
4.4 /5
Shawna and Ryan · 5 responses
Overall program avg
3.7/5
Across six 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 6 instructor observations: Shawna and Ryan
Shawna rated engagement and participation both at 5/5. Ryan rated engagement at 4/5 and participation at 5/5. The averaged instructor score of 4.75/5 is the highest of the program. Both instructors described the session as excellent with no suggestions for improvement.
The retail scenario activity with four structured questions worked very well. Ryan noted it effectively guided groups through their full skill set from previous modules, making it strong preparation for the Week 7 assessment. Both instructors rated the activities at 5/5.
Students brought their own workplace examples into the discussion around using AI across different roles. Ryan described the responses to the slideshow questions as excellent and noted the group work was the best of the program. Lots of discussion throughout.
Shawna noted that reinforcing how the scenario activity would help students prepare for the assessment reduced early drop-offs during the breakout portion, a problem that had affected earlier sessions. This framing is worth keeping in future weeks.
Both instructors rated concept understanding as strong across the board. No concepts were flagged as confusing and pacing was described as about right by both facilitators.
Week 5 instructor observations: Shawna and Ryan
Shawna rated engagement and participation both at 4/5. She noted the scenario was hands-on and effective, and described participation as good overall. Ryan rated both at 2/5, noting participation was relatively low this evening. The averaged instructor score is 3/5 for both engagement and participation comfort.
The DECIDE framework landed well. Based on what groups shared back during the debrief, students applied the framework effectively to the scenario. Both instructors rated concept understanding as good, and neither flagged any specific concepts that students struggled with.
Two students shared real examples of how they are already using AI in their own work to improve decision-making and speed up repetitive tasks. Ryan noted this as a standout moment from the session, reflecting meaningful transfer from earlier modules.
Ryan suggested adding an icebreaker activity at the start of future sessions to boost interaction from the opening. With lower attendance and quieter participation, the energy at the start of class affected the overall dynamic.
One learner submitted a 0/5 rating on both usefulness and AI understanding, writing that they did not feel they learned anything new and expressed interest in more advanced AI topics, specifically agentic AI and how to build and deploy agents. This learner works in a student or non-employment context. The stated interests fall outside the scope of this program, which is designed for applied AI use in professional and retail-adjacent settings, not AI development.
Week 4 instructor observations: Shawna and Ryan
Despite the low attendance, both instructors noted the session went well. Shawna rated engagement at 4/5 and participation at 4/5. Ryan rated both at 2/5, averaging to 3/5 across the two facilitators. Shawna noted that real-world examples from the instructors' own lives seemed to ignite the discussion around responsible AI use.
The retail scenarios worked well as a format. Students had no trouble applying the AI Responsibility Gate to the scenarios, and concept understanding was rated as strong or good by both instructors. No concepts were flagged as confusing.
A standout moment: one group working on Scenario 2 concluded that AI was not the right tool for the situation at all. Shawna noted this as a sign the content is landing, not just being processed but actually being applied with critical judgment.
Ryan noted the opening discussion questions in the slides were not the strongest lead-in to the ethics topic. They got people talking but did not clearly frame the key ethical stakes of the module. A tighter opening that connects directly to the concepts explored later would improve the flow.
Students who showed up during the retail scenario activity dropped off as the session progressed. Shawna noted the pacing was about right, but Ryan rated it slightly fast. With only 7 attendees, the energy of the room was different from earlier weeks.
Ryan suggested adding a short section on the environmental impacts of AI to a future module iteration. Students have raised this topic in past sessions and it could usefully broaden the discussion around real-world costs and responsibilities connected to AI use.
Week 3 instructor observations: Shawna and Ryan
Shawna rated both engagement and comfort participating at 5/5. She noted the banter between herself and Ryan seemed to encourage students to participate more, and described the session as perfect with great participation when groups shared out their scenarios.
Ryan rated both engagement and comfort participating at 3/5. He noted the reviewed prompt examples set a clear and practical tone for the activity, and that group work during the prompt writing exercise was strong. All groups created solid prompts and were able to explain the reasoning behind their choices.
Students applied the AI Output Evaluation Lens well, using it to assess tone, accuracy, and appropriateness across scenarios. Posting prompts in the chat for group analysis worked particularly well as a format.
Two students opened the session by sharing that they had already been using AI in their own work: a strong signal of real-world connection and transfer from earlier modules.
Ryan noted that if more time were available, having participants write their own prompts from scratch rather than building on reviewed examples would deepen the learning. Worth building into a future activity design.
Ryan flagged a pattern of a few students joining briefly and then dropping off, particularly when breakout rooms open. This has come up across multiple weeks and may warrant a direct check-in with those learners.
Week 1 instructor observations: Shawna and Ryan
Students were active in the chat throughout the entire session, answering questions and contributing comments. Shawna rated engagement at 5/5 and comfort participating at 4/5. Ryan rated engagement at 4/5 and comfort participating at 3/5, averaging to 4.5 and 3.5 across the two facilitators.
No concepts appeared to cause confusion. Both instructors noted students showed strong understanding of the key ideas in the session. The material was accessible across the range of backgrounds in the room.
Pacing was appropriate according to both instructors. The session ran smoothly overall, with no major delivery issues flagged.
Time ran short before both breakout activities could be completed. Ryan suggested keeping the first activity shorter and reducing the three discussion questions down to one, to allow more room for the second activity in future sessions.
One student stayed after class to ask the instructor questions, a signal of genuine interest in the material heading into Week 2.
Week 2 instructor observations: Ryan
Ryan rated both engagement and comfort participating at 5/5. The class flowed well with strong participation throughout. Students were enthusiastic in both the main session and breakout rooms.
The breakout activity using the AI Decision-Making Matrix landed well. Students engaged seriously with the risk and human judgment scenarios and brought real examples of friction from their own workplaces. All examples aligned closely with the friction categories in the lesson plan: communication, planning and coordination, and information clarity.
Ryan noted it was clear that students had absorbed the content from Shawna's presentation. The depth of discussion in breakout rooms reflected strong concept uptake going into the activity.
A few students showed some confusion around risk levels, flagging things like miscommunications or minor sales losses as high risk rather than reserving that category for harm or legal consequences. Ryan suggested adding a slide that more clearly illustrates the risk scale, with examples at both ends.
Students felt they could have used more discussion time in breakout rooms. Worth considering a slight adjustment to the timing split between presentation and activity going forward.
Note: only Ryan submitted an instructor feedback form for Week 2. Shawna's submission is pending. The Week 2 composite score reflects Ryan's ratings only and may shift once Shawna's data is in.
Student voice
What students are learning and what they want next
Usefulness rating distribution: all weeks combined (38 responses)
1
0
3
2
13
3
15
4
6
5
4 is now the most common rating across all six weeks. No ratings of 1 have been recorded. One 0/5 rating was recorded in Week 5 from a learner whose stated interests are outside the scope of this program. Week 6 showed the strongest student ratings of the program, with 4 of 5 responses coming in at 4 or 5.
What students want to learn more about
Prompt engineering
AI Decision-Making Matrix
AI in operations and workflows
Practical AI tools and skills
Risk escalation and human judgment
AI ethics and responsibility
AI for business productivity
Optimizing schedules with AI
AI in supply chain and inventory
How to evaluate AI biases
Agentic AI
AI use cases in e-commerce
AI-assisted store policy review
Evaluating risk with AI
Direct quotes from student feedback
Week 6 · Module 6
"How important are soft skills in decision-making scenarios using AI."
Week 6 · Module 6
"Adaptability and transferability in using AI skills."
Week 6 · Module 6
"Using AI and analyzing the outcomes suits the current scenario."
Week 6 · Module 6
Week 5 · Module 5
"A workflow is a sequence of steps used to complete a task or process in the workplace. AI will help with certain steps in a workflow, but not the entire task."
Week 5 · Module 5
"How to use AI for productivity within retail."
Week 5 · Module 5
Week 4 · Module 4
"Ethical risks involved with AI implementation in retail if not carefully designed."
Week 4 · Module 4
"Different scenarios on when AI is appropriate to use and how it should be used."
Week 4 · Module 4
"The significance of accountability in AI use."
Week 4 · Module 4
Week 3 · Module 3
"Prompt engineering: how to write a better prompt."
Week 3 · Module 3
"How to better evaluate an AI response and how to improve prompting."
Week 3 · Module 3
Week 2 · Module 2
"AI will really help me build the SOPs I've been putting off for weeks."
Week 2 · Module 2
"You still need to keep a human in the loop under any circumstances."
Week 2 · Module 2
"In high pressure, fluctuating scenarios, human judgment and oversight is so important. AI can help with logistics, clarifying communication, and analyzing data, but you need to remember that human touch and empathy are crucial in terms of customer experience."
Week 2 · Module 2
"The AI Decision-Making Matrix can help categorize situations based on the level of risk and human judgment."
Week 2 · Module 2
"I learned the differences among automation, generative AI, and agentic AI."
Week 2 · Module 2
Week 1 · Module 1
"I learned how AI is being embedded in today's retail industry, such as its ability to forecast inventory, spot patterns, and contribute more quickly to a company's growth."
Week 1 · Module 1
"AI has been integrated into our lives without us knowing. For example, unlocking your phone with your face."
Week 1 · Module 1
"I'm excited to learn more about the practical impact AI is already having, like with the Joe Fresh example, where they determined customers chose not to shop online due to long wait times getting carts delivered to their cars."
Week 1 · Module 1
Self-paced completion
Learner progress through the Disco self-paced modules
Module 1 completions
7/15
47% · highest of any module
Module 2 completions
6/15
40% of learners completed
Module 3 completions
5/15
33% of learners completed
Module 4 completions
5/15
33% of learners completed
Module 5 completions
4/15
27% of learners completed
Module 6 completions
3/15
20% complete · 15 in progress
Completion by module
Module 1
7/15
47%
Module 2
6/15
40%
Module 3
5/15
33%
Module 4
5/15
33%
Module 5
4/15
27%
Module 6
3/15
20%
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 1 has reached 7 completions (47%), the highest of any module. Modules 2 through 4 range from 33% to 40%. Module 5 is at 27% and Module 6, which just opened, is at 20% with all 15 learners currently in progress. With two weeks remaining, learners who have not completed Modules 1 through 5 are at risk for the microcredential assessment. A direct, individual outreach to non-completers is the recommended next step.
Baseline data
11 learners completed a baseline survey and assessment before the program began. (Two learners submitted twice; their scores were averaged.) This data captures where learners started: their familiarity with AI concepts, confidence using AI tools, and existing knowledge. It serves as a benchmark to track how understanding and confidence grow across the 8 weeks.
Avg knowledge score
5.5 /6
Pre-program quiz avg (11 respondents)
Avg AI confidence
6.7/10
Self-rated before program start
Used AI tools before
100%
All 11 learners had prior AI experience
Likelihood to pursue AI career
8.1/10
Avg across cohort
Familiarity with AI concepts before the program (0 to 4 scale)
Learners arrived with strong general AI familiarity. Data privacy is the lowest-rated concept, with prompt engineering and bias close behind. All three are directly addressed in this program.
Pre-program knowledge quiz scores (out of 6)
| Learner | Score | Bar |
|---|---|---|
| Baksheesh Kaur | 6/6 | |
| DJenné Campbell | 6/6 | |
| Caitlin Mcleod | 6/6 | |
| Jaymin Luces-Mendes | 6/6 | |
| Taejah Daniel | 6/6 | |
| Erim Yalcin | 5.5/6 | |
| Ephrata Gidey | 5/6 | |
| Chantal Gayle | 5/6 | |
| Yiu Wa Ng | 5/6 | |
| Mikia Carter | 5/6 | |
| Damilola Ibrahim | 5/6 |
Erim Yalcin's score is averaged across two submissions. No learner scored below 5/6.
View of AI in the workplace before the program
AI will likely make work easier
8/11
Helpful but unsure how to use it
2/11
AI makes me somewhat nervous
1/11
73% of learners arrived with a positive outlook on AI at work. 18% see it as useful but need support getting started, and 9% feel somewhat nervous. This spread makes the program's practical, applied framing a good fit for the cohort.
How learners were already using AI before the program
What learners hoped to get from this program: in their own words
"Learning how to navigate the world of AI and how beneficial it can be in the retail and retail-adjacent fields."
Ephrata Gidey
"Real life examples that can help me integrate AI in my work experience, helpful tools, and tips on communicating its use clearly, finding opportunities for my career and building more connections."
Baksheesh Kaur
"All the ways that my clients can benefit by maximizing their own time using AI."
DJenné Campbell
"How to prepare for adopting AI use in the workplace."
Caitlin Mcleod
"How to use AI to make my retail and vending business more efficient, especially with inventory tracking, customer follow-up, sales forecasting, and marketing automation."
Taejah Daniel
What learners found confusing or challenging before starting
The most common concern was around trust: knowing when to rely on an AI output and when to question it. The "black box" problem came up more than once, with learners wanting to understand how AI actually arrives at its answers. Data privacy, ethics, and knowing which tool to use in which situation were also recurring themes.
"I'm still working out how to use AI reliably without overtrusting it, especially when it comes to accuracy, privacy, and knowing when to verify its output."
Erim Yalcin
"Lack of true explainability, often called the black box problem. It can be hard to understand exactly how AI models arrive at certain decisions or outputs, which makes it tricky to fully trust them in high-stakes situations."
Erim Yalcin
"How to choose a suitable one from different AI tools."
Yiu Wa Ng
"Understanding AI policy and ethics, especially around privacy, data protection, and responsible use."
Taejah Daniel
Top interest areas for the program
AI policy / ethics (10)
Data and analytics (10)
AI development (9)
AI for business productivity (9)
Cybersecurity (5)
Cohort profile
Who is in the room and why that matters
Role distribution (11 baseline respondents)
Cohort 3 is a mixed group that spans frontline retail staff, corporate office roles, job seekers, and professionals from adjacent fields. Retail associates and corporate/office roles are the two largest clusters. Several respondents are currently between jobs or in non-traditional roles, which reflects the program's reach into workforce transitions as well as active employment.
Retail associate / frontline staff
3 · 27%
Corporate / office role
3 · 27%
Consultant
1 · 9%
Job seeker / not currently employed
3 · 27%
Cybersecurity / specialist role
1 · 9%
Industry backgrounds in the room (learner feedback, Weeks 1, 2, and 3)
TechLargest group across both weeks
Student / Not currently workingMultiple respondents each week
HospitalityRestaurants and hotels
Retail (in-store)Customer-facing roles
Banking / Financial services1 respondent
Consulting1 respondent
Government / Public sector1 respondent
Logistics1 respondent
Printing industry1 respondent
Cohort 3 is one of the most industry-diverse groups so far. Tech professionals make up the largest single cluster across the two weeks of feedback, alongside a meaningful share of students and career transitioners. The applied, transferable framing of the content is well matched to this range of backgrounds.
What learners are bringing to the room
Cohort 3 arrived with a high baseline. All 11 baseline respondents had used AI tools before the program started, and the average pre-program knowledge quiz score was 5.5 out of 6. Most had been using tools like ChatGPT, Copilot, and Gemini regularly in their personal and professional lives. What they are looking for is not an introduction to AI but a framework for using it more deliberately, more responsibly, and more effectively in their actual work. The program's decision-making and practical skills focus is well aligned with where this cohort already is.
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. Given Cohort 3's strong pre-program knowledge base, the applied frameworks introduced in Weeks 1 and 2 are giving learners the structured language to match their existing hands-on experience.
Insights
What the data tells us through Week 6
Week 6 was the strongest session of the program. Instructor scores were the highest recorded: 4.5/5 for engagement and 5/5 for participation comfort. Student ratings came in at 4.0/5 usefulness and 4.2/5 AI understanding. The composite of 4.4/5 is the program's peak. Both instructors described it as an excellent class with nothing to improve.
The recap structure worked well and built momentum toward the assessment. Connecting the scenario activity directly to the upcoming Week 7 assessment reduced early drop-offs, which had been a recurring issue. Students brought their own workplace examples into the discussion, and Ryan noted the group work was the best of the program. This format is worth carrying forward into Week 7.
Self-paced completion is improving across earlier modules. Module 1 is at 47% and Module 2 at 40%, both meaningfully higher than earlier in the program. All 15 learners are in progress on Module 6. The trajectory is positive, though the absolute numbers remain low for the later modules where completion matters most for the assessment.
Attendance has stabilised but remains below expectations. Weeks 5 and 6 both came in at 67% (10/15). The 6-week average is 69.7%. With two sessions left, the five learners who have been consistently absent have now missed a significant portion of the program. Their eligibility for the microcredential assessment may be affected and is worth confirming with the program team.
Modules 3 through 5 completion rates are a concern heading into assessment week. Each sits at 27% to 33%. These modules cover prompt engineering, ethics, and decision-making, which are the core applied competencies the assessment tests. Learners who have not completed these modules are going into the assessment without the full self-paced reinforcement. Individual outreach before Week 7 is recommended.
Week 4 and 5 composites updated with more complete data. Previous dashboard versions had only 2 and 3 learner responses for Weeks 4 and 5 respectively. The current file includes 5 and 6 responses. Week 4 composite revised to 3.1/5 (was 3.3), Week 5 revised to 3.1/5 (was 2.6 with only the partial dataset that included the 0/5 outlier). The 6-week program average is 3.7/5.