“Beat the Machines”: Using AI to Develop Case Assignments and Simulate Work Experience
Objectives
This assignment was developed for IRE348: Recruitment and Selection (Winter 2026). The objective is to give students an opportunity to apply recruitment and selection theory and best practices to a real organization of their choosing, while using generative AI as a tool at two key points in the process rather than as a substitute for student work.
First, AI is used to generate a custom organizational analysis and set of preliminary job analyses for a company the team selects, giving each team a unique case to work with (rather than a single shared case for the whole class). Second, AI is used to generate a draft job advertisement, which students must then critically evaluate and “beat” by producing their own, stronger advertisement and recruitment strategy. Throughout, students are required to triangulate AI output against external sources such as the National Occupational Classification (NOC), O*NET, and real job postings, reinforcing that AI-generated content must be verified rather than taken at face value.
Process
The assignment was designed as a scaffolded, iterative approach to building a full recruitment and selection strategy around an AI-generated organizational case. The steps included:
Step 1: Generate a Custom Organizational Case
- Teams of three select a real organization operating in Canada or the United States
- Using a standardized, instructor-provided prompt, teams generate an AI-based organizational analysis covering company overview, workforce characteristics, culture, HR challenges, and three preliminary senior-level job analyses
- Teams select one senior-level role from the AI output to build their strategy around
Step 2: Verify and Build the Job Description
- Students draft a summary of organizational characteristics relevant to hiring, using course frameworks and verifying AI-generated claims
- Students create a full job description and specification, triangulating AI output against the NOC, O*NET, and comparable job postings (e.g., LinkedIn, Indeed)
Step 3: "Beat the Machines"
- Students use AI to draft a sample job advertisement based on their job description
- Students then create their own improved job advertisement designed to outperform the AI version
- Students write a critical comparison identifying where the AI ad fails (e.g., legal issues, lack of appeal, inaccuracies) and where their version succeeds
Step 4: Develop a Recruitment Strategy
- Students outline target candidates, sourcing channels, bonafide occupational requirements (BFORs), equity considerations, and geographic constraints for building an applicant pool
Step 5: Develop a Screening and Selection Strategy
- Students design a screening and selection process, including criteria, longlisting/shortlisting, selection methods, and rationale grounded in course material
Step 6: Document AI and Resource Use
- Submit all AI prompts and full outputs as an appendix
- Students cite any external sources used
Future-Focused Skill Development
This assignment directly aligns with the University of Calgary’s STRIVE model for designing assessments that effectively incorporate generative AI. In particular, it aligns with the STRIVE model’s approach to transparency, responsibility, and validity. The assignment promotes transparency by requiring students to use a standardized, instructor-approved prompt and to submit all AI prompts and outputs as an appendix, helping students develop clarity around how and when AI is being applied. It supports responsibility by asking students to verify AI-generated organizational and job analysis content against external sources, requiring them to be accountable for the accuracy of the material they ultimately rely on rather than accepting AI output at face value.
Finally, the “beat the machines” component strongly reflects the validity dimension: by critiquing the AI-generated job advertisement and producing an improved version, students build meta-cognitive skills through self-reflection on where AI succeeds and fails, and develop agency in recognizing the continued value of human judgment.
Student Feedback
Professor Seward shares: “Employing AI in this approach has provided a refreshing development to the tried-and-tested model of case assignments. From Lululemon to NASA, students were restricted only by their imaginations for the employers they envisioned working with. Students are more engaged when they have agency in choice, and this application of generative AI adds a level of personalization to the task and subsequently greater buy-in from students. In a few cases, students reached out with suggestions to further develop the setup prompt, leading to a collaborative exploration of AI as a tool.
Importantly, because students are able to freely choose their industry and company, the project serves as a problem-based, simulated experiential learning opportunity that can be used on a resume to signal students’ market-ready skills in the industries and sectors they plan to enter. Opportunities to communicate career readiness such as this one can be pivotal to early labour market entry for students that are still developing their work-related experience.”