Program overview
All 8 weeks complete · Program concluded May 28, 2026
Avg attendance rate
84.0%
Across all 8 weeks
Learners enrolled
14
2 withdrew after Week 2
Feedback responses
66
Across 8 modules
Attendance chart
87%
100%
86%
86%
79%
86%
71%
79%
Week 1
13/15
Week 2
14/14
Week 3
12/14
Week 4
12/14
Week 5
11/14
Week 6
12/14
Week 7
10/14
Week 8
11/14
Instructor engagement ratings by week
Week 1 · Merveille and Ryan · avg of both
Week 2 · Merveille and Ryan · avg of both
Week 3 · Merveille and Ryan · avg of both
Week 5 · Merveille · single submission
Week 7 · Ryan · single submission
Week 8 · Merveille and Ryan · avg of both
Student self-ratings by week
6 Wk 1 · 20 Wk 2 · 8 Wk 3 · 11 Wk 4 · 10 Wk 5 · 4 Wk 6 · 5 Wk 7 · 2 Wk 8 responses
What's resonating: student learning moments
The program is complete. Across 8 weeks, this cohort built a working vocabulary for responsible AI use, applied frameworks to real workplace decisions, and left more confident than they arrived. Several learners said the program changed how they think about AI entirely.
"I was unconfident and struggled to use my curiosity. Now I am much more confident and structured — and I have made a lot of headway in the space."
Program reflection · Cameron Johnson
"Once you understand how to communicate with AI properly, it becomes a very practical and useful tool that can really support your work and help you grow."
Program reflection · Neslihan Marlali
"AI can provide data and recommendations, but human judgment, communication, and contextual decision-making are still essential for making the right decisions."
Week 6 · Module 6
"Always keep a human in the loop — AI is there for assistance, not replacement."
Week 1 · Module 1
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 2 learners are managers and team leads in the retail industry, building skills in prompt construction, output evaluation, human-in-the-loop decision-making, and responsible AI use at the leadership level. 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 supports work but doesn't remove human responsibility. The wide range of backgrounds in the room created rich discussion. Students were highly engaged and many stayed after class to ask for resources.
★★★★☆
4.2 student avg
2
Using AI to Reduce Friction in Everyday Retail Work
Key takeaways: The AI Decision-Making Matrix helps managers evaluate when and how to use AI. Human review remains essential, especially for customer-facing decisions. The friction points discussion generated strong energy across the group. Several students followed up by email asking for additional resources.
★★★★☆
4.0 student avg
3
Evaluating AI Outputs in Customer-Facing Situations
Key takeaways: Students are building prompt construction skills and learning to critically evaluate AI outputs before applying them. The retail scenario activity generated the deepest discussion of the program so far — students pushed back on Gemini's suggestions and thought carefully about edge cases. Prompt engineering is emerging as a major area of interest.
★★★★★
4.4 student avg
4
Responsible and Ethical AI Use in Retail Environments
Key takeaways: Bias can enter AI workflows before a prompt is even written. The AI Responsibility Gate framework gave students a structured way to evaluate ethical risk. This was the most energized session of the program — 75–90% active participation and every group fully engaged in the breakout. Several students noted this topic could support a standalone microcredential for data-heavy industries.
★★★★★
4.5 student avg
5
AI-Supported Decision-Making for Retail Teams
Key takeaways: The DECIDE framework gave students a practical structure for evaluating when and how to involve AI in workplace decisions. Students applied the AI Decision Matrix to assess the level of oversight needed for specific tasks. Despite smaller attendance, the session was highly engaged — several students stayed after class to continue discussing AI systems connectivity and the risks of over-relying on AI without considering human input.
★★★★★
4.6 student avg
6
Adapting AI Skills Across Retail Roles
Key takeaways: Students explored how their roles are evolving alongside AI and which human skills — emotional intelligence, contextual judgment, communication — remain essential. The opening discussion questions ran long in the best possible way: students were debating, building on each other's ideas, and connecting AI concepts to their real workplaces without being prompted. One standout exchange moved from restaurant customer service to the idea of curating experiences, to emotional intelligence as a competitive skill. Both instructors noted it was one of the strongest discussion sessions of the program.
★★★★★
4.75 student avg
7
Microcredential Preparation Lab
Key takeaways: Students worked through a mock microcredential assessment, applying the frameworks and skills built across the program. The summary slides served as a useful refresher before moving into the activity. In breakout rooms, students were actively supporting each other — asking questions, sharing approaches, and working collaboratively through the assessment steps. Two students had questions about the microcredential process and were directed to reach out to Jessica at TMU. Ryan noted the session timing needs fine-tuning for future cohorts: three breakout rounds with slightly more time each, and a firm cut-off to protect space for debrief, would improve the flow.
★★★★★
5.0 · 1 response
8
Program Wrap-Up and Reflection
Key takeaways: The final session brought the program to a close with a full review of all modules and space for learners to reflect on their growth. Engagement was high and learner gratitude was described by Merveille as very strong. A standout moment: one learner shared how much she enjoyed Module 3 and prompt engineering — several others agreed it was the most useful part of the program. Learners expressed feeling more comfortable using AI in their work as a direct result of completing the program. Ryan noted three learners still needed the TMU registration form to finish their microcredential submission. Ryan also suggested swapping Modules 7 and 8 in future cohorts so learners complete the review session before the mock assessment, which could improve both preparation and final-class attendance.
★★★★☆
4.0 student avg
Attendance
Live session presence across the 8-week program
Total enrolled
14
2 withdrew after Week 2
Avg attendance rate
84.0%
Across all 8 weeks
Week 8 attendance
11/14
79% — program complete
Weekly attendance log
Week 1
13/15
87%
Week 2
14/14
100%
Week 3
12/14
86%
Week 4
12/14
86%
Week 5
11/14
79%
Week 6
12/14
86%
Week 7
10/14
71%
Week 8
11/14
79%
Notes
Two learners withdrew from the program after Week 2, bringing active enrolment to 14. Attendance from Week 3 onward is calculated against that revised total. The 8-week average is 84.0%. Week 8 saw a small recovery to 79% (11/14), up from the Week 7 low of 71%. The program ran its full eight sessions with consistent attendance, peaking at 100% in Week 2 and holding above 79% in the final session.
Engagement
Composite score from instructor and student ratings · Program complete
Week 1
4.4 /5
Composite
Week 2
3.9 /5
Composite
Week 3
4.6 /5
Composite
Week 4
4.6 /5
Merveille only
Week 5
4.7 /5
Merveille only
Week 6
4.8 /5
Both instructors
Week 7
4.2 /5
Ryan only · 5 responses
Week 8
4.4 /5
Both instructors · 2 responses
Overall avg
4.5/5
Eight 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 1 instructor observations: Merveille and Ryan
Students were highly engaged from the start. Group discussions opened up strong interest in the current AI landscape and generated real energy in the room.
The session struck a good balance between lecturing and discussion, keeping it both informative and engaging. Key points from the lesson plan were clearly emphasized and well summarized at the end.
The instructor met learners where they are. Despite a wide range of AI knowledge in the group, the material was accessible to all while still engaging more advanced learners.
There was more content than could realistically be covered in the allotted time. Identifying which parts of each lesson are essential would help instructors prioritize when time constraints arise.
Many students stayed after class to ask for resources and books on AI — a strong signal of genuine curiosity and motivation going into Week 2.
Week 2 instructor observations: Merveille and Ryan
Engagement and participation were lower this week. Ryan rated both at 3/5, while Merveille rated engagement 4/5 and participation 5/5 — averaging 3.5 and 4.0 respectively across facilitators.
Concept understanding remained strong. Students who engaged made great observations during the Gemini activity and showed they are working with the material.
The friction points discussion generated genuine interest. The AI Decision-Making Matrix was a clear standout — many students were hearing about it for the first time and spent real time on it.
During the Gemini breakout activity, many students copied and pasted the scenario prompts rather than writing their own. Prompting students to construct their own prompts would drive deeper learning.
Several students followed up after class by email asking for additional resources to start engaging with AI on their own — a positive signal of motivation outside the classroom.
Week 8 instructor observations: Merveille and Ryan
Both instructors rated engagement and participation at 5/5, with 50–90% of students actively contributing. Merveille noted that learner gratitude for the program was very high, and the energy in the final session reflected that.
A standout moment: one learner shared how much she enjoyed Module 3 and the focus on prompt engineering. Several others agreed it was the most useful part of the program. Learners also expressed feeling more comfortable using AI in their work as a direct result of taking the course.
Ryan described the final session as an overall very good review of all modules, with pacing and timing working out well. Session content and lesson plan were rated 5/5 and 4/5 respectively. Both instructors found the review format effective for closing the program.
Three learners still need the TMU registration form to complete their microcredential submission: Aisha Abdullahi, Cameron Johnson, and Gary Whittingham. The program team should follow up directly.
Ryan raised a structural suggestion for future cohorts: consider swapping Modules 7 and 8 so learners complete the full review session before the mock microcredential activity. This could improve preparation and create more incentive to attend the final class. Ryan also noted that the review content built into Module 6 may create some overlap with the final modules, which is worth reviewing for balance.
Week 7 instructor observations: Ryan
Ryan rated both engagement and participation at 4/5, with 50–75% of students actively contributing. Concept understanding was strong — no students appeared to struggle with the material, and the session content and lesson plan were rated 5/5.
The mock microcredential activity worked well. Students understood the format, completed the steps as expected, and demonstrated a solid grasp of the frameworks from across the program. The summary slides at the start were an effective refresher before moving into the activity.
A standout moment: in breakout rooms, students were actively supporting each other — asking questions, sharing approaches, and helping peers work through the assessment steps. The peer dynamic was strong even with a smaller group.
Session timing needs attention for future cohorts. The original plan called for three breakout rounds — Parts 1 and 2, then Parts 3 and 4, then Parts 5 and 6. The first round (20 minutes) felt rushed for some learners. Ryan adjusted mid-session to combine Parts 3 through 6 into one 25-minute round, which helped, but left limited time for a full group debrief afterward.
Ryan's suggested fix for future cohorts: keep the three-round structure but increase time per round, reduce the amount of instruction before the activity begins, and set a firm cut-off for breakout rooms to protect discussion time. A shorter, more focused debrief after each round would work better than one long debrief at the end.
Two students had questions about the microcredential process and were directed to reach out to Jessica at TMU. Note: only Ryan submitted instructor feedback for Week 7. Merveille's submission is pending.
Week 6 instructor observations: Merveille and Ryan
Both instructors rated engagement and participation at 5/5, with 75–90% of students actively contributing. This is the first week since Week 3 that both Merveille and Ryan submitted feedback, and both described the session in the same terms: highly engaged, strong discussion, students making real connections.
The opening discussion questions carried the session. Students were so engaged that the discussion ran significantly longer than planned, leaving less time for the scenario activity. Both instructors noted this was worth it — the conversation was thoughtful, student-led, and directly connected to the course concepts.
A standout moment: one student proposed that the future of restaurant customer service may involve curating an experience rather than just taking orders. Another connected this to restaurants as social spaces. The group landed on emotional intelligence as a key human skill that AI cannot replace — exactly the kind of synthesis the program is designed to build.
Concept understanding was strong. Neither instructor noted any areas where students appeared to struggle. The session content and lesson plan were both rated 5/5.
Time management was the one area flagged for improvement. Ryan suggested identifying in advance which parts of the lesson can be shortened if discussion runs long — so instructors have a clear decision point without cutting what's working.
Week 5 instructor observations: Merveille
Engagement and participation were both rated 5/5. Despite the smaller group, 75–90% of students actively contributed — consistent with the last two weeks.
The DECIDE framework was a standout. Merveille noted it was well thought out and students found it a genuinely useful tool for evaluating AI input when making decisions — not just a theoretical concept but something they could apply right away.
Concept understanding was strong. No students appeared to struggle with the key material, and the session content and lesson plan were rated 5/5 for supporting delivery.
Several students stayed after class to continue the conversation — specifically around how AI systems connect to each other and how much people are using AI to make decisions without factoring in human judgment. A strong signal the content is landing beyond the session itself.
Note: only Merveille submitted an instructor feedback form for Week 5. Ryan's submission is pending. The Week 5 composite score reflects Merveille's ratings only and may shift once Ryan's data is in.
Week 4 instructor observations: Merveille
Engagement was at its highest this session — rated 5/5 for both overall engagement and participation. Merveille noted this was an overall standout class with great participation from everyone.
The topic of responsible and ethical AI use was one students were clearly interested in, which drove the high energy. Approximately 75–90% of students actively participated — the strongest participation rate of the program to date.
Concept understanding was strong across the board. Students did not appear to struggle with any of the key concepts, and the session content and lesson plan were rated 5/5 for how well they supported teaching the material.
One student noted this topic could support a standalone microcredential for industries that handle large volumes of sensitive data — a strong signal that the content resonated beyond the retail context.
Merveille raised a useful question for the program team: beyond the AI Responsibility Gate, what frameworks does CILAR recommend for identifying and escalating bias found in a company's AI systems? And what data or examples should learners bring to management? This could be worth addressing in a future module or resource.
Note: only Merveille submitted an instructor feedback form for Week 4. Ryan's submission is pending. The Week 4 composite score reflects Merveille's ratings only and may shift once Ryan's data is in.
Week 3 instructor observations: Merveille and Ryan
Engagement rebounded strongly — both instructors rated engagement and participation at 5/5. The highest-rated session of the program so far.
The retail scenario activity was the standout of the session. Students spent real time thinking critically about different Gemini outputs and pushed back on suggestions from the AI rather than accepting them at face value.
Groups were equally active in both constructing prompts and editing outputs — a significant improvement over Week 2 where many students copied and pasted instead.
Prompt engineering is emerging as a topic of real interest. Students in the chat and in discussion were asking follow-up questions and wanting to go deeper.
Three scenarios were assigned during the breakout — most groups only completed one or two. Focusing everyone on a single scenario next time would allow for a richer debrief without feeling rushed.
Three students dropped from the session during breakout rooms. One reported Wi-Fi issues; the reason for the other two is unknown. Week 3 attendance was 12/14 (86%) with the revised enrolment.
Student voice
What students are learning and what they want next
Usefulness rating distribution: all weeks combined (66 responses)
0
1
1
2
7
3
32
4
26
5
4 is the most common rating across all eight weeks. No ratings of 1 recorded across 66 total responses. 89% of all responses were a 4 or 5.
What students want to learn more about
Durable and human skills alongside AI
Balancing AI recommendations with human judgment
DECIDE framework
Prompt engineering and writing
AI tools by task and situation
Bias identification and mitigation
TMU microcredential preparation
AI in customer-facing roles
Legislative frameworks around AI
Training and bias in AI models
AI templates for internal workflows
When not to use AI
Direct quotes from student feedback
Program reflections
"I was unconfident and struggled to use my curiosity. Now I am much more confident and structured — and I have made a lot of headway in the space."
Cameron Johnson · Course reflection
"Once you understand how to communicate with AI properly, it becomes a very practical and useful tool that can really support your work and help you grow."
Neslihan Marlali · Course reflection
"This course reinforced certain concepts like ethical and responsible AI thinking."
Colin Smith · Course reflection
"This course can help you build confidence in using AI at work."
Sittipong Liamsuwan · Course reflection
Week 7 · Module 7
"How to use AI analytics to support professional decisions and maximize outcomes."
Week 7 · Module 7
Week 6 · Module 6
"AI can provide data and recommendations, but human judgment, communication, and contextual decision-making are still essential for making the right decisions."
Week 6 · Module 6
"Using AI to help customers and using the DECIDE model to ensure empathy stays in the process."
Week 6 · Module 6
"AI doesn't know all the facts and only gives results based on the prompt received. The decision to use the results or ignore them always remains with us."
Week 6 · Module 6
"Critical thinking was the main takeaway. At this stage, things aren't as complex as I assumed, considering my level of AI fluency."
Week 6 · Module 6
Week 5 · Module 5
"DECIDE: when to use and when not to use AI."
Week 5 · Module 5
"Bioethics and how integral it is in the evolution of AI integrating into our daily lives."
Week 5 · Module 5
Week 4 · Module 4
"A strong AI prompt is not just about what you ask for — it is about what you constrain."
Week 4 · Module 4
Week 1 · Module 1
"Always keep a human in the loop — AI is there for assistance, not replacement."
Week 1 · Module 1
Self-paced completion
Learner progress through the Disco self-paced modules
Module 1
12/14
86% completed
Module 2
12/14
86% completed
Module 3
10/14
71% completed
Module 4
10/14
71% completed
Module 5
8/14
57% completed
Module 6
8/14
57% completed
Module 7
4/14
29% completed · 13 in progress
Module 8
3/14
21% completed · 14 in progress
Completion by module
Module 1
12/14
86%
Module 2
12/14
86%
Module 3
10/14
71%
Module 4
10/14
71%
Module 5
8/14
57%
Module 6
8/14
57%
Module 7
4/14
29%
Module 8
3/14
21%
About self-paced learning
After each live class, learners complete a corresponding self-paced module on the Disco platform. The program has concluded. Modules 1 and 2 reached 86% completion (12/14), with Modules 3 and 4 at 71%. Modules 5 and 6 are at 57%. Modules 7 and 8 are still in progress for most learners — 4 have completed Module 7 and 3 have completed Module 8, with the remainder still working through the self-paced content. Learners have access to complete outstanding modules as they finish their microcredential submission.
Baseline data
12 learners completed a baseline survey and assessment before the program began. 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
5.1 /6
Pre-program quiz avg (12 respondents)
Avg AI confidence
5.6/10
Self-rated before program start
Used AI tools before
92%
11 of 12 learners had prior AI experience
Likelihood to pursue AI career
7.8/10
Avg across cohort
Familiarity with AI concepts before the program (0 to 4 scale)
Data privacy is the lowest-rated area, and prompt engineering is close behind. Both are directly addressed in this program.
Pre-program knowledge quiz scores (out of 6)
| Learner | Score | Bar |
|---|---|---|
| Sittipong Liamsuwan | 6/6 | |
| Cameron Johnson | 6/6 | |
| Colin Smith | 6/6 | |
| Q. Shelly | 6/6 | |
| Veronica Jarabe | 5/6 | |
| Aisha Abdullahi | 5/6 | |
| Moniruzzaman Chowdhury | 5/6 | |
| Neslihan Marlali | 5/6 | |
| Marc Lalande | 5/6 | |
| Derya Durmazsen | 5/6 | |
| Samantha Ko | 5/6 | |
| Gary Whittingham | 2/6 |
View of AI in the workplace before the program
AI will likely make work easier
8/12
Helpful but unsure how to use it
4/12
67% of learners arrived with a positive outlook on AI at work. The remaining third see it as useful but need support getting started — exactly what this program is designed to provide.
How learners were already using AI before the program
What learners hoped to get from this program: in their own words
"When and when not to use AI in the workplace."
Marc Lalande
"Creating and scheduling AI routines for processes."
Colin Smith
"Data privacy and prompt engineering."
Aisha Abdullahi
"Useful AI applications in retail I can implement."
Cameron Johnson
"Ways I can use AI to improve sales and support in our business."
Gary Whittingham
What learners found confusing or challenging before starting
Accuracy and reliability were the most common concerns. Prompt engineering — knowing how to ask AI the right questions — came up repeatedly. Several learners also named data security and knowing which tool to use for which task as areas of uncertainty.
"I find it challenging to know how to use AI to create agents so it can do the minimal work behind the scenes."
Moniruzzaman Chowdhury
"Identifying hallucinations and accuracy of AI response."
Veronica Jarabe
"Which AI tool is better for a specific task. What AI models work best in which scenario."
Cameron Johnson
"A challenge for me is knowing how best to apply AI tools in day-to-day work while ensuring accuracy, consistency, and appropriate use of information."
Samantha Ko
Top interest areas for the program
AI for business productivity (12)
Data and analytics (10)
AI policy and ethics (8)
AI development (8)
Cybersecurity (6)
Cohort profile
Who is in the room and why that matters
Role distribution (12 baseline respondents)
Cohort 2 was designed specifically for managers and team leads in the retail industry. The baseline data reflects that focus: supervisors and corporate/office roles make up the majority of respondents. Several participants are also managers outside of retail who applied to deepen their AI skills in a leadership context.
Corporate / office role
5 · 42%
Supervisor / team lead
4 · 33%
Manager (non-retail)
1 · 8%
Retail associate / frontline staff
1 · 8%
Other / not specified
1 · 8%
Industry backgrounds in the room (student feedback, Weeks 1, 2, and 3)
Retail (corporate)Multiple respondents each week
Retail (in-store)Customer-facing roles
Banking / Financial servicesMultiple respondents
Government / Public sectorMultiple respondents
TechMultiple respondents
Non-profit1 respondent
Customer service (non-retail)Multiple respondents
Warehouse / Operations1 respondent
While Cohort 2 targets retail managers and team leads, the room includes professionals from adjacent sectors. The leadership framing of the content travels well across these backgrounds.
What learners are bringing to the room
Cohort 2 is a cohort of experienced managers and team leads. Most have already been using AI tools — ChatGPT, Microsoft Copilot, Google Gemini — in their day-to-day work. They are not starting from scratch. They are looking for the frameworks and judgment to use AI more deliberately: knowing when to use it, how to evaluate its outputs, and how to lead others in using it responsibly. This makes the program's decision-making and ethical 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. Cohort 2's leadership focus makes the supervisory pathway particularly relevant. Both require demonstrated applied AI competency, not just participation.
End of program
Knowledge check results, confidence growth, and learner reflections
End-of-program knowledge check scores (out of 16)
| Learner | Score | Bar |
|---|---|---|
| Shilanda Stewart | 15/16 | |
| Sittipong Liamsuwan | 15/16 | |
| Aisha Abdullahi | 14/16 | |
| Veronica Jarabe | 14/16 | |
| Samantha Ko | 11/16 |
Avg: 86.2% across 5 respondents. Compare to pre-program avg of 85.0% (12 respondents, out of 6) — the group who completed both assessments held strong from start to finish.
AI confidence and concept familiarity: before vs after (3 respondents)
Confidence self-rated 0–10 · Concept familiarity 0–4
Post-program concept familiarity (3 respondents). Pre-program avg was 2.5/4 across all concepts. AI and Generative AI show the strongest growth.
What learners said about their growth: course reflections
8 learners completed the course reflection survey. Their responses show a cohort that arrived with working AI experience and left with structured frameworks, stronger critical thinking, and more confidence applying AI deliberately in their work.
"I was unconfident and struggled to use my curiosity. Now I am much more confident and structured — and I have made a lot of headway in the space."
Cameron Johnson
"Understanding how to communicate with AI better has motivated me to explore different areas and improve my skills."
Neslihan Marlali
"I had thought AI would replace humans and take their jobs. For now, it seems like humans are still needed — context, critical thinking, reasoning, and judgment are still required."
Samantha Ko
"After this class, I will ensure sensitive data is reviewed before using AI and will evaluate all outputs carefully."
Sittipong Liamsuwan
"Responsible AI use means knowing when to use AI and when to skip it. We are the ones responsible for our jobs and must know to use it wisely."
Veronica Jarabe
"I use AI as an assistant to help me work more efficiently. But I must review all outputs and ensure sensitive data is protected before sharing anything with others."
Sittipong Liamsuwan
What learners plan to take forward
AI Decision-Making Matrix
DECIDE framework
Effective prompt writing
Critical thinking with AI outputs
Human in the loop
Responsible AI use in daily work
Protecting sensitive data
Evaluating AI outputs before sharing
AI for documentation and reporting
Agentic AI and workflow automation
What learners would tell someone starting the program
"Join with an open mind and learn about both the pros and cons of AI."
Moniruzzaman Chowdhury
"Don't worry if it feels unfamiliar at the beginning. Once you understand how to communicate with AI properly, it becomes a very practical and useful tool."
Neslihan Marlali
"Be prepared to be engaged in class activities and have an open mind."
Colin Smith
"It is useful and gives good insights into how AI should be used."
Qiana Shelly
"It is a great introductory course."
Gary Whittingham
Insights
Program complete · Final summary across all 8 weeks
The program finished on a high note. Both instructors rated Week 8 engagement and participation at 5/5. Learner gratitude was described as very high, and the session produced a standout moment: learners organically affirmed that prompt engineering — Module 3 — was the most useful part of the program. Multiple learners expressed feeling more comfortable using AI at work as a direct result of completing the course.
Learners who engaged with the assessment showed strong knowledge retention. The end-of-program knowledge check averaged 86.2% across 5 respondents (vs 85.0% pre-program baseline). Confidence in using AI at work grew from 5.6/10 to 7.7/10 across the 3 learners who completed both growth surveys.
Engagement quality was consistently strong despite lower attendance in the back half. The 8-week composite average is 4.5/5. The program dipped in Week 2 and again in Week 7 (where only Ryan submitted feedback), but Weeks 3 through 6 and Week 8 all came in at 4.4 or above. Students who attended were consistently engaged and rated sessions highly.
Attendance declined in the second half, which affected self-paced completion. The 8-week average is 84.0%, but Weeks 5 through 7 ranged from 71% to 79%. Modules 7 and 8 self-paced completion is still low — 29% and 21% respectively. Three learners still need the TMU registration form to complete their microcredential submission.
Two structural recommendations for future cohorts. Ryan suggested swapping Modules 7 and 8 so learners complete the program review before the mock assessment — this could improve preparation and final-class attendance. He also flagged that the review content spread across Modules 6, 7, and 8 may need rebalancing to avoid redundancy.
Prompt engineering was the standout topic across the cohort. It appeared in Week 7 and Week 8 reflections as the most practically useful skill, was named repeatedly in "want to learn more" responses throughout the program, and was highlighted in the end-of-program course reflections by multiple learners. The applied format of Module 3 is worth preserving and potentially expanding in future cohorts.