12 Designing an AI-Integrated Unit

One AI-enhanced activity is an experiment. A whole unit designed around AI integration is a pedagogy.

12.1 Start Small, Then Scale

Before redesigning an entire unit, most educators benefit from a phased approach:

Phase 1: Personal experimentation. Before your next class, spend an hour generating teaching resources with AI. Create a case study, run a simulation yourself, review an existing assignment through the lens of “could AI enhance this?” You need to be comfortable with the tool before introducing it to students.

Phase 2: Low-stakes student introduction. Introduce AI as an optional practice tool for an upcoming assignment, or demonstrate it live in a lecture. No grades attached. Let students see what it does and form their own impressions. Students who try it will spread the word to peers.

Phase 3: Pilot one assessment. Choose a single assignment worth 15-25% of the grade. Test the prompts thoroughly yourself first. Provide clear instructions, do a live demo, and build in flexibility for technical issues. Grade thinking and process, not just outputs.

Phase 4: Gather feedback. Survey students. Reflect on what worked. Make 2-3 specific changes for next time.

Phase 5: Expand next semester. Add a second AI component, or make the existing one more sophisticated. By now you know what works in your context.

Most lecturers should complete at least one pilot before attempting whole-unit redesign. Once you have, you are ready for what follows.

12.2 Beyond Individual Assignments: Whole-Unit Design

The real power of AI in education emerges when you design an entire unit — a complete semester’s learning — with AI integration from the start.

This is not about “adding AI” to an existing unit. It is about redesigning with AI as a pedagogical partner, creating learning experiences that were not previously possible.

This chapter walks through complete unit design using backwards design principles, showing you how to scaffold student learning from “first encounter with AI” to “competent professional use.”

12.3 The Backwards Design Approach

12.3.1 Step 1: Define Learning Outcomes (AI-Neutral)

Start here, always. What should students be able to do by the end of the unit?

Example Unit: Workplace Conflict and Resolution (Third-year undergraduate, HR)

Learning Outcomes: 1. Analyse workplace conflicts using conflict resolution theory and organisational justice frameworks 2. Conduct fair, impartial investigations of workplace complaints 3. Demonstrate effective communication in difficult conversations (de-escalation, active listening, empathy) 4. Design and facilitate conflict resolution interventions appropriate to context 5. Apply relevant employment law and procedural fairness principles 6. Reflect critically on own practice and identify areas for development

Note: These outcomes don’t mention AI. They describe professional competencies. AI is the means, not the end.

Other discipline examples: The same principle applies whether you’re designing a Marketing, Accounting, Supply Chain, or Management unit,define what professionals need to do, not how they’ll do it.

12.3.2 Step 2: Design Assessments (How Will Students Demonstrate Mastery?)

Using the process-based assessment principles from the Assessment chapter, design assessments that make professional competence visible.

Assessment 1: Investigation Interview Simulation (25%) - What: Students conduct simulated investigation interview with AI persona, submit transcript + process audit - Assesses: Learning outcomes 2, 3, 5, 6 - Due: Week 6 (mid-semester) - Why this timing: Gives students foundational practice before more complex work

Assessment 2: Conflict Resolution Portfolio (40%) - What: Students design intervention for multi-stakeholder conflict, conduct simulated mediation/facilitation, write reflective analysis - Assesses: Learning outcomes 1, 3, 4, 6 - Due: Week 11 - Why this timing: Builds on skills from Assessment 1, integrates theory from mid-semester content

Assessment 3: Research Essay (Critical Analysis) (35%) - What: Critical analysis of conflict resolution approaches in specific organisational contexts (e.g., remote work, culturally diverse teams, union environments) - Assesses: Learning outcomes 1, 5 - Due: Week 13 (exam period) - Why this timing: Synthesizes learning from entire semester - AI integration: Students use AI for literature synthesis and draft feedback (the Self-Assessment chapter model)

12.3.3 Step 3: Map Learning Activities (How Will Students Prepare for Assessments?)

Now design the week-by-week learning journey that scaffolds students from novice to competent.

Key principle: Gradually increase complexity of AI interaction while building skill.

12.4 Complete 12-Week Unit Design Example

12.4.1 Week 1: Introduction to Conflict and Introduction to AI

Learning Focus: Understand types of workplace conflict, introduce AI as learning tool

Content: - Lecture: Sources and types of workplace conflict - Workshop: Conflict analysis frameworks (task vs. relationship conflict, etc.)

AI Activity (Low stakes, introductory):

In-class demonstration:
- Show students a simple conflict scenario
- Use AI to generate 3 different stakeholder perspectives on the same incident
- Discuss: "How can seeing multiple perspectives help us understand conflict?"

Purpose: - Students see AI in action (demystify) - Understand AI can help explore complexity - No pressure—just observation

Student Task: - Install ChatGPT or Claude - Complete the “Getting Started” tutorial (the Getting Started chapter exercise) - Submit screenshot showing they successfully generated a simple HR scenario

12.4.2 Week 2: Conflict Theory and AI Exploration

Learning Focus: Apply conflict theory; practice writing prompts

Content: - Lecture: Conflict resolution theories (interest-based, transformative, etc.) - Workshop: Analysing conflict through theoretical lenses

AI Activity (First hands-on practice):

Assignment: Theory Application Practice (ungraded)
- Students receive a workplace conflict scenario
- Use AI to analyse the conflict through 2 different theoretical frameworks
- Write 300 words comparing the insights each theory provides
- Submit both the AI conversation and their reflection

Purpose: - Students practice prompt writing - Students evaluate AI’s theoretical analysis - Low-stakes experimentation - Lecturer can see who needs prompt-writing help

12.4.3 Week 3: Communication Skills for Conflict

Learning Focus: Active listening, empathetic communication, managing emotion

Content: - Lecture: Communication theory and de-escalation techniques - Workshop: Communication analysis (watch video examples, critique)

AI Activity (First simulation):

Practice Simulation (ungraded, but required):
- Students conduct 5-minute conversation with AI playing "frustrated employee"
- Focus: Practice de-escalation language
- Students can retry as many times as they want
- Submit their best attempt + 200-word reflection: "What did I learn about my communication?"

Purpose: - First taste of “flight simulator” - Builds confidence before graded assessment - Students realise they can practice privately and improve

12.4.4 Week 4: Legal Framework and Procedural Fairness

Learning Focus: Natural justice, procedural fairness, relevant legislation

Content: - Lecture: Legal obligations in workplace investigations - Workshop: Case studies of procedural failures and consequences

AI Activity (Legal application practice):

Formative Exercise:
- AI generates 5 short investigation scenarios
- For each, students identify: What legal principle is at risk? What should HR do?
- Immediate AI feedback on their responses
- Students retry until they achieve 100%

Purpose: - Spaced repetition of legal knowledge - Students can practice until mastery (not time-limited) - AI provides immediate correction

12.4.5 Week 5: Investigation Skills

Learning Focus: Conducting fair, thorough workplace investigations

Content: - Lecture: Investigation methodology and common pitfalls - Workshop: Planning an investigation (what questions, what order, what documentation)

AI Activity (Assessment preparation):

Scaffolded practice for Assessment 1:
- Students receive the persona they'll encounter in Assessment 1 (preview)
- Conduct practice interview
- Generate AI critique
- Revise approach
- Conduct second practice interview

Purpose: - Direct preparation for upcoming assessment - Students enter Assessment 1 having already practiced - Reduces anxiety, improves quality

12.4.6 Week 6: Assessment 1 Due - Investigation Interview Simulation

No new content this week—focus on assessment

Students submit: 1. Transcript of investigation interview with AI persona 2. Process audit document analysing their own performance 3. 500-word reflection on learning

Teaching focus this week: - Availability for consultation/questions - Technical support for any AI access issues

12.4.7 Week 7: Feedback Week + Mediation Theory

Learning Focus: Understanding Assessment 1 feedback; introduction to mediation

Content: - Return Assessment 1 with feedback - Lecture: Mediation and facilitation approaches - Workshop: Compare mediation models (evaluative, facilitative, transformative)

AI Activity (Exploring alternatives):

Scenario Exploration:
- Students receive a conflict scenario suitable for mediation
- Use AI "Pros and Cons" technique (the Seven Techniques chapter) to evaluate which mediation approach is best
- In-class discussion: Did different students reach different conclusions? Why?

Purpose: - Recover from assessment submission - Introduce new content at moderate cognitive load - Build toward Assessment 2

12.4.8 Week 8: Facilitation Skills

Learning Focus: Facilitation techniques for multi-party conflict

Content: - Lecture: Managing multi-stakeholder conversations - Workshop: Power dynamics, coalition-building, impasse-breaking

AI Activity (Complex simulation introduction):

Multi-party simulation practice:
- Students manage conversation between 2 AI personas in conflict
- Practice balancing airtime, managing interruptions, keeping focus
- Ungraded but highly recommended for Assessment 2 preparation

Purpose: - Increase complexity (now managing 2 personas, not 1) - Build skills for Assessment 2 - Students who struggled with Assessment 1 get redemption opportunity

12.4.9 Week 9: Cultural and Ethical Considerations

Learning Focus: Cross-cultural conflict, ethical dilemmas, bias awareness

Content: - Lecture: Cultural dimensions in conflict (individualism/collectivism, face-saving, etc.) - Workshop: Ethical dilemmas in conflict resolution (confidentiality, power imbalances, organisational pressure)

AI Activity (Critical evaluation):

AI Ethics Exercise:
- AI generates a conflict resolution plan
- Students critique it for:
  - Cultural insensitivity
  - Ethical gaps
  - Bias toward organisational interests over fairness
- Write memo explaining what AI got wrong and why

Purpose: - Develop critical oversight of AI - Connect theory (cultural frameworks, ethics) to practice - Prepare for Assessment 2 cultural/ethical analysis

12.4.10 Week 10: Designing Interventions

Learning Focus: Strategic planning for conflict resolution

Content: - Lecture: Matching interventions to conflict type and context - Workshop: Intervention design process

AI Activity (Assessment 2 preparation):

Portfolio Development:
- Students begin working on Assessment 2
- Use AI to generate multiple intervention options for their chosen scenario
- Bring draft analysis to workshop for peer feedback

Purpose: - Structured time for assessment work - Peer learning and feedback - Lecturer can identify students who need additional support

12.4.11 Week 11: Assessment 2 Due - Conflict Resolution Portfolio

Students submit: 1. Conflict analysis and intervention design (written component) 2. Transcript(s) of simulated intervention (conversation with AI personas) 3. Reflective analysis integrating theory and evaluating their practice

12.4.12 Week 12: Contemporary Issues and Research Essay Support

Learning Focus: Emerging trends in workplace conflict; research essay preparation

Content: - Lecture: Special topics (remote work conflict, AI in HR, gig economy disputes) - Workshop: Research essay planning and literature review strategies

AI Activity (Research support):

Essay development support:
- Students use AI to identify key literature on their chosen topic
- Use AI self-assessment tool to check essay plan
- Optional: Book consultation with lecturer to discuss draft

Purpose: - Support final assessment - Lighter week (no new major concepts) - Celebrate semester’s learning

12.4.13 Week 13: Assessment 3 Due - Research Essay

Students submit critical analysis essay.

12.5 The Scaffolding Progression Model

Notice how AI integration increases in complexity:

Week	AI Complexity	Student Agency	Stakes
1-2	Observation— simple prompts	Low (following instructions)	None (ungraded)
3-4	Single persona— structured scenarios	Medium (some choice in approach)	Low (formative)
5-6	Graded simulation— self-assessment	High (must plan and execute)	Medium (25% of grade)
7-9	Multi-persona— ethical critique	High (designing interventions)	Preparation for high-stakes
10-11	Complex portfolio with multiple components	Very high (strategic choices)	High (40% of grade)
12-13	AI as research assistant	Very high (independent work)	High (35% of grade)

This progression develops: 1. Technical comfort (Weeks 1-2) 2. Basic AI literacy (Weeks 3-4) 3. Applied competence (Weeks 5-8) 4. Critical oversight (Weeks 9-11) 5. Independent professional use (Weeks 12-13)

12.6 Balancing AI and Non-AI Activities

Important: Not everything should involve AI.

12.6.1 This unit includes traditional elements:

Lectures: Content delivery (theory, legal frameworks, research findings)
Workshops: Peer discussion, case analysis, group problem-solving
Readings: Textbook chapters, journal articles, policy documents
Live role-play: At least 1-2 in-person practice sessions for social learning
Guest speaker: Practicing mediator or workplace investigator
Reflective journaling: Weekly reflections on learning (not AI-assessed)

12.6.2 The 60/40 rule:

Aim for approximately: - 60% traditional teaching and learning activities - 40% AI-enhanced activities

This ensures students develop both technological proficiency and traditional professional skills (working with humans, not just chatbots).

12.7 Supporting Student AI Literacy Development

Across the semester, explicitly teach AI literacy:

12.7.1 Week 1: What AI Is (and Isn’t)

AI as pattern generator, not intelligence
Strengths and limitations
When to trust vs. verify

12.7.2 Week 4: Advanced Prompting

How to write effective prompts
Troubleshooting poor responses
Iterating to improve results

12.7.3 Week 7: Critical Evaluation

How to spot AI errors
When AI oversimplifies
recognising bias in AI outputs

12.7.4 Week 9: Professional Ethics

Accountability when using AI tools
When to use AI vs. when human judgment is essential
Transparent vs. hidden AI use

By semester’s end, students haven’t just used AI,they’ve developed AI literacy as a professional competency.

12.8 Unit Outline Template (for Your Own Design)

Use this template to design your AI-integrated unit:

12.8.1 UNIT INFORMATION

Unit code and title:
Year level and semester:
Credit points:
Prerequisites:

12.8.2 LEARNING OUTCOMES (AI-neutral)

12.8.3 ASSESSMENT SUMMARY

Assessment	Weight	Due Week	AI Integration	Outcomes Assessed

12.8.4 WEEKLY SCHEDULE

Week [X]: [Topic] - Learning focus: - Content delivery: - AI activity: - Purpose: - Preparation for next week:

[Repeat for 12-13 weeks]

12.8.5 AI LITERACY PROGRESSION

Weeks 1-3: [foundational skills]
Weeks 4-6: [applied practice]
Weeks 7-9: [critical evaluation]
Weeks 10-13: [independent professional use]

12.8.6 BALANCE CHECK

Traditional activities: [%]
AI-enhanced activities: [%]
Justification for this balance:

12.8.7 STUDENT SUPPORT

Resources provided for AI access:
Technical support available:
Academic support for AI use:
Equity considerations addressed:

12.9 Common Design Mistakes to Avoid

12.9.1 Mistake 1: “AI for AI’s Sake”

Problem: Including AI because it’s trendy, not because it serves learning outcomes. Solution: Every AI activity must clearly connect to a learning outcome. If you can’t justify it pedagogically, remove it.

12.9.2 Mistake 2: All or Nothing

Problem: Either avoiding AI entirely or making everything AI-based. Solution: Balance. Use AI where it adds value (simulation, feedback, practice) and traditional methods where they’re superior (peer learning, live practice, social skills).

12.9.3 Mistake 3: Assuming Technical Competence

Problem: Expecting students to figure out AI tools independently. Solution: Explicitly teach prompt writing, troubleshooting, critical evaluation. Scaffold technical skills just like you scaffold content knowledge.

12.9.4 Mistake 4: No Progression

Problem: Same level of AI complexity all semester. Solution: Design deliberate progression from simple to complex, guided to independent, low-stakes to high-stakes.

12.9.5 Mistake 5: Ignoring Equity

Problem: Assuming all students have equal access to AI tools, devices, internet. Solution: Provide alternatives (lab access, in-class time for AI activities), use university-subscribed tools where possible, ensure core learning is accessible without premium AI access.

12.10 Aligning Unit Design with Programme Goals

Your unit doesn’t exist in isolation—it’s part of a degree programme.

12.10.1 Consider:

Vertical integration: - What AI skills do students bring from earlier units? - What AI competencies will later units assume? - How does your unit scaffold toward programme-level AI literacy?

Horizontal integration: - What other units are students taking concurrently? - Could you coordinate AI activities across multiple units? - Are there opportunities for cross-unit projects?

Programme-level graduate capabilities: - How does your AI integration support overarching graduate capabilities? - Communication? Critical thinking? Professional practice? Technological proficiency?

12.11 Communicating the Design to Students

Students need to understand the pedagogical design—it helps them engage meaningfully.

12.11.1 First lecture (explain the approach):

“This unit uses AI tools as part of your learning. Here’s why:

In your [professional field] careers, you’ll use AI for analysis, strategy development, decision support, and other professional tasks. Our job is to prepare you to use those tools competently and ethically.

You’ll notice the AI activities progress across the semester: - Early weeks: You’ll practice basic skills in safe, low-stakes environments - Mid-semester: You’ll apply those skills in realistic scenarios for assessment - Late semester: You’ll use AI independently as a professional tool

By the end, you’ll have practiced complex professional scenarios dozens of times,something that would be impossible without AI. You’ll also know when to trust AI, when to question it, and when human judgment must override technology.

This isn’t about making your degree easier. It’s about preparing you for professional practice in an AI-augmented world.”

12.11.2 In your unit outline (be explicit):

Include a section titled “AI Integration in This Unit” that explains: - Why AI is used - How it supports learning outcomes - What skills students will develop - Expectations for academic integrity - Support available

12.12 Evaluating Your AI-Integrated Unit

After the semester, evaluate systematically:

12.12.1 Student learning evidence:

Did assessment results improve compared to previous semesters?
Did students demonstrate competencies that previous cohorts struggled with?
What does student work reveal about their AI literacy development?

12.12.2 Student feedback:

Survey: How useful was AI for your learning? (1-5 scale)
What AI activities were most valuable?
What AI activities felt like “busy work”?
Do you feel more prepared for professional practice?

12.12.3 Your experience:

Did AI integration save or cost you time overall?
What worked better than expected? Worse?
What would you change next semester?
What would you keep?

12.12.4 Iterate and refine based on evidence.

12.13 Cross-Discipline Unit Design Examples

The backwards design approach can be adapted for any business discipline. Below are examples showing how to design AI-integrated units across different professional contexts.

Example: Accounting — Advanced Audit and Assurance

Learning Outcomes (AI-Neutral): 1. Apply professional auditing standards and ethical principles 2. Design risk-based audit procedures and testing strategies 3. Evaluate internal controls and assess control effectiveness 4. Communicate audit findings to diverse stakeholders 5. Demonstrate professional skepticism and critical analysis 6. Apply data analytics in audit planning and execution

Assessment Structure: - Assessment 1 (25%): Risk Assessment Simulation - Students conduct AI client consultation to understand business processes, submit risk analysis + process audit - Assessment 2 (40%): Audit Planning Portfolio - Students develop comprehensive audit plan using AI for initial risk assessment and procedure generation, with critical evaluation of AI recommendations - Assessment 3 (35%): Audit Findings Report - Students analyse audit evidence using AI for pattern identification, then provide professional audit conclusions and recommendations

Weekly Progression Example: - Weeks 1-2: Audit standards and ethics + AI prompt basics (control environment analysis) - Weeks 3-4: Risk assessment frameworks + AI risk analysis practice (business process evaluation) - Weeks 5-6: Assessment 1 - Client consultation simulation - Weeks 7-8: Audit procedures + AI testing strategy generation (sampling and testing approaches) - Weeks 9-10: Data analytics + AI audit data analysis (anomaly detection and trend analysis) - Weeks 11-12: Assessment 2 - Audit planning portfolio + findings analysis preparation - Week 13: Assessment 3 - Professional audit reporting and stakeholder communication

Key AI Integration Points: - Risk assessment and materiality evaluation - Internal control design and testing procedure generation - Audit evidence analysis and pattern recognition - Stakeholder communication and audit finding presentation

12.14 Adapting Unit Design Principles Across Disciplines

12.14.1 Common Design Elements

Progression Framework: Regardless of discipline, follow the same scaffolding progression:

Weeks 1-2: Foundational content + AI basics
Weeks 3-4: Core concepts + AI application practice
Weeks 5-6: First assessment (simulation-based)
Weeks 7-9: Advanced concepts + critical AI evaluation
Weeks 10-12: Major assessment (portfolio-based)
Week 13: Synthesis assessment (research/analysis-based)

Assessment Balance: Maintain similar weighting across disciplines:

25%: Process-focused simulation (consultation/interview)
40%: Portfolio assessment (design/strategy development)
35%: Critical analysis (research/strategic evaluation)

AI Literacy Development: Include the same AI literacy progression in all disciplines:

Technical comfort (prompt writing, tool navigation)
Applied competence (discipline-specific applications)
Critical oversight (evaluation of AI outputs)
Independent professional use (strategic AI integration)

12.14.2 Discipline-Specific Considerations

Creative Fields (Marketing, Design): - Emphasise subjective evaluation and iterative refinement - Include portfolio development and presentation skills - Balance analytical and creative AI applications

Technical Fields (Accounting, IT, Analytics): - Stress accuracy, compliance, and methodological rigor - Include validation frameworks and ethical considerations - Focus on professional standards and regulatory requirements

Service Fields (Tourism, Hospitality, Management): - Emphasise stakeholder dynamics and relationship management - Include cultural competence and emotional intelligence - Focus on practical implementation and human factors

Adaptation Strategy: Start with the HR unit design as a template, then modify:

Content: Replace HR-specific topics with discipline-specific concepts
Scenarios: Adapt AI personas and contexts to discipline-appropriate situations
Assessments: Modify evaluation criteria to reflect professional standards
Progression: Maintain scaffolding structure while adjusting complexity levels

12.15 Your Action Step

Design (or redesign) one unit using this backwards design approach:

Choose a unit you teach (or will teach)
Define learning outcomes (without mentioning AI)
Design assessments that make competence visible
Map 12-week learning journey with deliberate AI scaffolding
Check balance (60% traditional, 40% AI-enhanced)
Plan equity supports (access, alternatives, scaffolding)

Don’t aim for perfection—aim for “better than what I’m currently doing.”

You can refine each semester based on what you learn.

# Designing an AI-Integrated Unit > One AI-enhanced activity is an experiment. A whole unit designed around AI integration is a pedagogy. ## Start Small, Then Scale Before redesigning an entire unit, most educators benefit from a phased approach: **Phase 1: Personal experimentation.** Before your next class, spend an hour generating teaching resources with AI. Create a case study, run a simulation yourself, review an existing assignment through the lens of "could AI enhance this?" You need to be comfortable with the tool before introducing it to students. **Phase 2: Low-stakes student introduction.** Introduce AI as an optional practice tool for an upcoming assignment, or demonstrate it live in a lecture. No grades attached. Let students see what it does and form their own impressions. Students who try it will spread the word to peers. **Phase 3: Pilot one assessment.** Choose a single assignment worth 15-25% of the grade. Test the prompts thoroughly yourself first. Provide clear instructions, do a live demo, and build in flexibility for technical issues. Grade thinking and process, not just outputs. **Phase 4: Gather feedback.** Survey students. Reflect on what worked. Make 2-3 specific changes for next time. **Phase 5: Expand next semester.** Add a second AI component, or make the existing one more sophisticated. By now you know what works in your context. Most lecturers should complete at least one pilot before attempting whole-unit redesign. Once you have, you are ready for what follows. ## Beyond Individual Assignments: Whole-Unit Design The real power of AI in education emerges when you design an entire unit — a complete semester's learning — with AI integration from the start. This is not about "adding AI" to an existing unit. It is about redesigning with AI as a pedagogical partner, creating learning experiences that were not previously possible. This chapter walks through complete unit design using backwards design principles, showing you how to scaffold student learning from "first encounter with AI" to "competent professional use." --- ## The Backwards Design Approach ### Step 1: Define Learning Outcomes (AI-Neutral) Start here, always. What should students be able to do by the end of the unit? **Example Unit:** Workplace Conflict and Resolution (Third-year undergraduate, HR) **Learning Outcomes:** 1. Analyse workplace conflicts using conflict resolution theory and organisational justice frameworks 2. Conduct fair, impartial investigations of workplace complaints 3. Demonstrate effective communication in difficult conversations (de-escalation, active listening, empathy) 4. Design and facilitate conflict resolution interventions appropriate to context 5. Apply relevant employment law and procedural fairness principles 6. Reflect critically on own practice and identify areas for development **Note:** These outcomes don't mention AI. They describe professional competencies. AI is the *means*, not the *end*. **Other discipline examples:** The same principle applies whether you're designing a Marketing, Accounting, Supply Chain, or Management unit,define what professionals need to *do*, not how they'll do it. --- ### Step 2: Design Assessments (How Will Students Demonstrate Mastery?) Using the process-based assessment principles from the Assessment chapter, design assessments that make professional competence visible. **Assessment 1: Investigation Interview Simulation (25%)** - **What:** Students conduct simulated investigation interview with AI persona, submit transcript + process audit - **Assesses:** Learning outcomes 2, 3, 5, 6 - **Due:** Week 6 (mid-semester) - **Why this timing:** Gives students foundational practice before more complex work **Assessment 2: Conflict Resolution Portfolio (40%)** - **What:** Students design intervention for multi-stakeholder conflict, conduct simulated mediation/facilitation, write reflective analysis - **Assesses:** Learning outcomes 1, 3, 4, 6 - **Due:** Week 11 - **Why this timing:** Builds on skills from Assessment 1, integrates theory from mid-semester content **Assessment 3: Research Essay (Critical Analysis) (35%)** - **What:** Critical analysis of conflict resolution approaches in specific organisational contexts (e.g., remote work, culturally diverse teams, union environments) - **Assesses:** Learning outcomes 1, 5 - **Due:** Week 13 (exam period) - **Why this timing:** Synthesizes learning from entire semester - **AI integration:** Students use AI for literature synthesis and draft feedback (the Self-Assessment chapter model) --- ### Step 3: Map Learning Activities (How Will Students Prepare for Assessments?) Now design the week-by-week learning journey that scaffolds students from novice to competent. **Key principle:** Gradually increase complexity of AI interaction while building skill. --- ## Complete 12-Week Unit Design Example ### Week 1: Introduction to Conflict and Introduction to AI **Learning Focus:** Understand types of workplace conflict, introduce AI as learning tool **Content:** - Lecture: Sources and types of workplace conflict - Workshop: Conflict analysis frameworks (task vs. relationship conflict, etc.) **AI Activity (Low stakes, introductory):** ``` In-class demonstration: - Show students a simple conflict scenario - Use AI to generate 3 different stakeholder perspectives on the same incident - Discuss: "How can seeing multiple perspectives help us understand conflict?" ``` **Purpose:** - Students see AI in action (demystify) - Understand AI can help explore complexity - No pressure---just observation **Student Task:** - Install ChatGPT or Claude - Complete the "Getting Started" tutorial (the Getting Started chapter exercise) - Submit screenshot showing they successfully generated a simple HR scenario --- ### Week 2: Conflict Theory and AI Exploration **Learning Focus:** Apply conflict theory; practice writing prompts **Content:** - Lecture: Conflict resolution theories (interest-based, transformative, etc.) - Workshop: Analysing conflict through theoretical lenses **AI Activity (First hands-on practice):** ``` Assignment: Theory Application Practice (ungraded) - Students receive a workplace conflict scenario - Use AI to analyse the conflict through 2 different theoretical frameworks - Write 300 words comparing the insights each theory provides - Submit both the AI conversation and their reflection ``` **Purpose:** - Students practice prompt writing - Students evaluate AI's theoretical analysis - Low-stakes experimentation - Lecturer can see who needs prompt-writing help --- ### Week 3: Communication Skills for Conflict **Learning Focus:** Active listening, empathetic communication, managing emotion **Content:** - Lecture: Communication theory and de-escalation techniques - Workshop: Communication analysis (watch video examples, critique) **AI Activity (First simulation):** ``` Practice Simulation (ungraded, but required): - Students conduct 5-minute conversation with AI playing "frustrated employee" - Focus: Practice de-escalation language - Students can retry as many times as they want - Submit their best attempt + 200-word reflection: "What did I learn about my communication?" ``` **Purpose:** - First taste of "flight simulator" - Builds confidence before graded assessment - Students realise they can practice privately and improve --- ### Week 4: Legal Framework and Procedural Fairness **Learning Focus:** Natural justice, procedural fairness, relevant legislation **Content:** - Lecture: Legal obligations in workplace investigations - Workshop: Case studies of procedural failures and consequences **AI Activity (Legal application practice):** ``` Formative Exercise: - AI generates 5 short investigation scenarios - For each, students identify: What legal principle is at risk? What should HR do? - Immediate AI feedback on their responses - Students retry until they achieve 100% ``` **Purpose:** - Spaced repetition of legal knowledge - Students can practice until mastery (not time-limited) - AI provides immediate correction --- ### Week 5: Investigation Skills **Learning Focus:** Conducting fair, thorough workplace investigations **Content:** - Lecture: Investigation methodology and common pitfalls - Workshop: Planning an investigation (what questions, what order, what documentation) **AI Activity (Assessment preparation):** ``` Scaffolded practice for Assessment 1: - Students receive the persona they'll encounter in Assessment 1 (preview) - Conduct practice interview - Generate AI critique - Revise approach - Conduct second practice interview ``` **Purpose:** - Direct preparation for upcoming assessment - Students enter Assessment 1 having already practiced - Reduces anxiety, improves quality --- ### Week 6: **Assessment 1 Due - Investigation Interview Simulation** **No new content this week---focus on assessment** Students submit: 1. Transcript of investigation interview with AI persona 2. Process audit document analysing their own performance 3. 500-word reflection on learning **Teaching focus this week:** - Availability for consultation/questions - Technical support for any AI access issues --- ### Week 7: Feedback Week + Mediation Theory **Learning Focus:** Understanding Assessment 1 feedback; introduction to mediation **Content:** - Return Assessment 1 with feedback - Lecture: Mediation and facilitation approaches - Workshop: Compare mediation models (evaluative, facilitative, transformative) **AI Activity (Exploring alternatives):** ``` Scenario Exploration: - Students receive a conflict scenario suitable for mediation - Use AI "Pros and Cons" technique (the Seven Techniques chapter) to evaluate which mediation approach is best - In-class discussion: Did different students reach different conclusions? Why? ``` **Purpose:** - Recover from assessment submission - Introduce new content at moderate cognitive load - Build toward Assessment 2 --- ### Week 8: Facilitation Skills **Learning Focus:** Facilitation techniques for multi-party conflict **Content:** - Lecture: Managing multi-stakeholder conversations - Workshop: Power dynamics, coalition-building, impasse-breaking **AI Activity (Complex simulation introduction):** ``` Multi-party simulation practice: - Students manage conversation between 2 AI personas in conflict - Practice balancing airtime, managing interruptions, keeping focus - Ungraded but highly recommended for Assessment 2 preparation ``` **Purpose:** - Increase complexity (now managing 2 personas, not 1) - Build skills for Assessment 2 - Students who struggled with Assessment 1 get redemption opportunity --- ### Week 9: Cultural and Ethical Considerations **Learning Focus:** Cross-cultural conflict, ethical dilemmas, bias awareness **Content:** - Lecture: Cultural dimensions in conflict (individualism/collectivism, face-saving, etc.) - Workshop: Ethical dilemmas in conflict resolution (confidentiality, power imbalances, organisational pressure) **AI Activity (Critical evaluation):** ``` AI Ethics Exercise: - AI generates a conflict resolution plan - Students critique it for: - Cultural insensitivity - Ethical gaps - Bias toward organisational interests over fairness - Write memo explaining what AI got wrong and why ``` **Purpose:** - Develop critical oversight of AI - Connect theory (cultural frameworks, ethics) to practice - Prepare for Assessment 2 cultural/ethical analysis --- ### Week 10: Designing Interventions **Learning Focus:** Strategic planning for conflict resolution **Content:** - Lecture: Matching interventions to conflict type and context - Workshop: Intervention design process **AI Activity (Assessment 2 preparation):** ``` Portfolio Development: - Students begin working on Assessment 2 - Use AI to generate multiple intervention options for their chosen scenario - Bring draft analysis to workshop for peer feedback ``` **Purpose:** - Structured time for assessment work - Peer learning and feedback - Lecturer can identify students who need additional support --- ### Week 11: **Assessment 2 Due - Conflict Resolution Portfolio** Students submit: 1. Conflict analysis and intervention design (written component) 2. Transcript(s) of simulated intervention (conversation with AI personas) 3. Reflective analysis integrating theory and evaluating their practice --- ### Week 12: Contemporary Issues and Research Essay Support **Learning Focus:** Emerging trends in workplace conflict; research essay preparation **Content:** - Lecture: Special topics (remote work conflict, AI in HR, gig economy disputes) - Workshop: Research essay planning and literature review strategies **AI Activity (Research support):** ``` Essay development support: - Students use AI to identify key literature on their chosen topic - Use AI self-assessment tool to check essay plan - Optional: Book consultation with lecturer to discuss draft ``` **Purpose:** - Support final assessment - Lighter week (no new major concepts) - Celebrate semester's learning --- ### Week 13: **Assessment 3 Due - Research Essay** Students submit critical analysis essay. --- ## The Scaffolding Progression Model Notice how AI integration increases in complexity: | Week | AI Complexity | Student Agency | Stakes | |---|---|---|---| | 1-2 | Observation--- simple prompts | Low (following instructions) | None (ungraded) | | 3-4 | Single persona--- structured scenarios | Medium (some choice in approach) | Low (formative) | | 5-6 | Graded simulation--- self-assessment | High (must plan and execute) | Medium (25% of grade) | | 7-9 | Multi-persona--- ethical critique | High (designing interventions) | Preparation for high-stakes | | 10-11 | Complex portfolio with multiple components | Very high (strategic choices) | High (40% of grade) | | 12-13 | AI as research assistant | Very high (independent work) | High (35% of grade) | **This progression develops:** 1. **Technical comfort** (Weeks 1-2) 2. **Basic AI literacy** (Weeks 3-4) 3. **Applied competence** (Weeks 5-8) 4. **Critical oversight** (Weeks 9-11) 5. **Independent professional use** (Weeks 12-13) --- ## Balancing AI and Non-AI Activities **Important:** Not everything should involve AI. ### This unit includes traditional elements: - **Lectures:** Content delivery (theory, legal frameworks, research findings) - **Workshops:** Peer discussion, case analysis, group problem-solving - **Readings:** Textbook chapters, journal articles, policy documents - **Live role-play:** At least 1-2 in-person practice sessions for social learning - **Guest speaker:** Practicing mediator or workplace investigator - **Reflective journaling:** Weekly reflections on learning (not AI-assessed) ### The 60/40 rule: Aim for approximately: - **60% traditional teaching and learning activities** - **40% AI-enhanced activities** This ensures students develop both technological proficiency and traditional professional skills (working with humans, not just chatbots). --- ## Supporting Student AI Literacy Development Across the semester, explicitly teach AI literacy: ### Week 1: What AI Is (and Isn't) - AI as pattern generator, not intelligence - Strengths and limitations - When to trust vs. verify ### Week 4: Advanced Prompting - How to write effective prompts - Troubleshooting poor responses - Iterating to improve results ### Week 7: Critical Evaluation - How to spot AI errors - When AI oversimplifies - recognising bias in AI outputs ### Week 9: Professional Ethics - Accountability when using AI tools - When to use AI vs. when human judgment is essential - Transparent vs. hidden AI use By semester's end, students haven't just used AI,they've developed **AI literacy as a professional competency**. --- ## Unit Outline Template (for Your Own Design) Use this template to design your AI-integrated unit: ### **UNIT INFORMATION** - Unit code and title: - Year level and semester: - Credit points: - Prerequisites: ### **LEARNING OUTCOMES** (AI-neutral) 1. 2. 3. 4. 5. ### **ASSESSMENT SUMMARY** | Assessment | Weight | Due Week | AI Integration | Outcomes Assessed | |---|---|---|---|---| | | | | | | | | | | | | | | | | | | ### **WEEKLY SCHEDULE** **Week [X]: [Topic]** - Learning focus: - Content delivery: - AI activity: - Purpose: - Preparation for next week: [Repeat for 12-13 weeks] ### **AI LITERACY PROGRESSION** - Weeks 1-3: [foundational skills] - Weeks 4-6: [applied practice] - Weeks 7-9: [critical evaluation] - Weeks 10-13: [independent professional use] ### **BALANCE CHECK** - Traditional activities: [%] - AI-enhanced activities: [%] - Justification for this balance: ### **STUDENT SUPPORT** - Resources provided for AI access: - Technical support available: - Academic support for AI use: - Equity considerations addressed: --- ## Common Design Mistakes to Avoid ### Mistake 1: "AI for AI's Sake" **Problem:** Including AI because it's trendy, not because it serves learning outcomes. **Solution:** Every AI activity must clearly connect to a learning outcome. If you can't justify it pedagogically, remove it. ### Mistake 2: All or Nothing **Problem:** Either avoiding AI entirely or making everything AI-based. **Solution:** Balance. Use AI where it adds value (simulation, feedback, practice) and traditional methods where they're superior (peer learning, live practice, social skills). ### Mistake 3: Assuming Technical Competence **Problem:** Expecting students to figure out AI tools independently. **Solution:** Explicitly teach prompt writing, troubleshooting, critical evaluation. Scaffold technical skills just like you scaffold content knowledge. ### Mistake 4: No Progression **Problem:** Same level of AI complexity all semester. **Solution:** Design deliberate progression from simple to complex, guided to independent, low-stakes to high-stakes. ### Mistake 5: Ignoring Equity **Problem:** Assuming all students have equal access to AI tools, devices, internet. **Solution:** Provide alternatives (lab access, in-class time for AI activities), use university-subscribed tools where possible, ensure core learning is accessible without premium AI access. --- ## Aligning Unit Design with Programme Goals Your unit doesn't exist in isolation---it's part of a degree programme. ### Consider: **Vertical integration:** - What AI skills do students bring from earlier units? - What AI competencies will later units assume? - How does your unit scaffold toward programme-level AI literacy? **Horizontal integration:** - What other units are students taking concurrently? - Could you coordinate AI activities across multiple units? - Are there opportunities for cross-unit projects? **Programme-level graduate capabilities:** - How does your AI integration support overarching graduate capabilities? - Communication? Critical thinking? Professional practice? Technological proficiency? --- ## Communicating the Design to Students Students need to understand the pedagogical design---it helps them engage meaningfully. ### First lecture (explain the approach): > "This unit uses AI tools as part of your learning. Here's why: > > In your [professional field] careers, you'll use AI for analysis, strategy development, decision support, and other professional tasks. Our job is to prepare you to use those tools competently and ethically. > > You'll notice the AI activities progress across the semester: > - Early weeks: You'll practice basic skills in safe, low-stakes environments > - Mid-semester: You'll apply those skills in realistic scenarios for assessment > - Late semester: You'll use AI independently as a professional tool > > By the end, you'll have practiced complex professional scenarios dozens of times,something that would be impossible without AI. You'll also know when to trust AI, when to question it, and when human judgment must override technology. > > This isn't about making your degree easier. It's about preparing you for professional practice in an AI-augmented world." ### In your unit outline (be explicit): Include a section titled **"AI Integration in This Unit"** that explains: - Why AI is used - How it supports learning outcomes - What skills students will develop - Expectations for academic integrity - Support available --- ## Evaluating Your AI-Integrated Unit After the semester, evaluate systematically: ### Student learning evidence: - Did assessment results improve compared to previous semesters? - Did students demonstrate competencies that previous cohorts struggled with? - What does student work reveal about their AI literacy development? ### Student feedback: - Survey: How useful was AI for your learning? (1-5 scale) - What AI activities were most valuable? - What AI activities felt like "busy work"? - Do you feel more prepared for professional practice? ### Your experience: - Did AI integration save or cost you time overall? - What worked better than expected? Worse? - What would you change next semester? - What would you keep? ### Iterate and refine based on evidence. --- ## Cross-Discipline Unit Design Examples The backwards design approach can be adapted for any business discipline. Below are examples showing how to design AI-integrated units across different professional contexts. ::: {.callout-tip title="Example: Accounting — Advanced Audit and Assurance"} **Learning Outcomes (AI-Neutral):** 1. Apply professional auditing standards and ethical principles 2. Design risk-based audit procedures and testing strategies 3. Evaluate internal controls and assess control effectiveness 4. Communicate audit findings to diverse stakeholders 5. Demonstrate professional skepticism and critical analysis 6. Apply data analytics in audit planning and execution **Assessment Structure:** - **Assessment 1 (25%):** Risk Assessment Simulation - Students conduct AI client consultation to understand business processes, submit risk analysis + process audit - **Assessment 2 (40%):** Audit Planning Portfolio - Students develop comprehensive audit plan using AI for initial risk assessment and procedure generation, with critical evaluation of AI recommendations - **Assessment 3 (35%):** Audit Findings Report - Students analyse audit evidence using AI for pattern identification, then provide professional audit conclusions and recommendations **Weekly Progression Example:** - **Weeks 1-2:** Audit standards and ethics + AI prompt basics (control environment analysis) - **Weeks 3-4:** Risk assessment frameworks + AI risk analysis practice (business process evaluation) - **Weeks 5-6:** Assessment 1 - Client consultation simulation - **Weeks 7-8:** Audit procedures + AI testing strategy generation (sampling and testing approaches) - **Weeks 9-10:** Data analytics + AI audit data analysis (anomaly detection and trend analysis) - **Weeks 11-12:** Assessment 2 - Audit planning portfolio + findings analysis preparation - **Week 13:** Assessment 3 - Professional audit reporting and stakeholder communication **Key AI Integration Points:** - Risk assessment and materiality evaluation - Internal control design and testing procedure generation - Audit evidence analysis and pattern recognition - Stakeholder communication and audit finding presentation ::: ## Adapting Unit Design Principles Across Disciplines ### Common Design Elements **Progression Framework:** Regardless of discipline, follow the same scaffolding progression: - **Weeks 1-2:** Foundational content + AI basics - **Weeks 3-4:** Core concepts + AI application practice - **Weeks 5-6:** First assessment (simulation-based) - **Weeks 7-9:** Advanced concepts + critical AI evaluation - **Weeks 10-12:** Major assessment (portfolio-based) - **Week 13:** Synthesis assessment (research/analysis-based) **Assessment Balance:** Maintain similar weighting across disciplines: - **25%:** Process-focused simulation (consultation/interview) - **40%:** Portfolio assessment (design/strategy development) - **35%:** Critical analysis (research/strategic evaluation) **AI Literacy Development:** Include the same AI literacy progression in all disciplines: - **Technical comfort** (prompt writing, tool navigation) - **Applied competence** (discipline-specific applications) - **Critical oversight** (evaluation of AI outputs) - **Independent professional use** (strategic AI integration) ### Discipline-Specific Considerations **Creative Fields (Marketing, Design):** - Emphasise subjective evaluation and iterative refinement - Include portfolio development and presentation skills - Balance analytical and creative AI applications **Technical Fields (Accounting, IT, Analytics):** - Stress accuracy, compliance, and methodological rigor - Include validation frameworks and ethical considerations - Focus on professional standards and regulatory requirements **Service Fields (Tourism, Hospitality, Management):** - Emphasise stakeholder dynamics and relationship management - Include cultural competence and emotional intelligence - Focus on practical implementation and human factors **Adaptation Strategy:** Start with the HR unit design as a template, then modify: 1. **Content:** Replace HR-specific topics with discipline-specific concepts 2. **Scenarios:** Adapt AI personas and contexts to discipline-appropriate situations 3. **Assessments:** Modify evaluation criteria to reflect professional standards 4. **Progression:** Maintain scaffolding structure while adjusting complexity levels --- ## Your Action Step Design (or redesign) one unit using this backwards design approach: 1. **Choose a unit** you teach (or will teach) 2. **Define learning outcomes** (without mentioning AI) 3. **Design assessments** that make competence visible 4. **Map 12-week learning journey** with deliberate AI scaffolding 5. **Check balance** (60% traditional, 40% AI-enhanced) 6. **Plan equity supports** (access, alternatives, scaffolding) Don't aim for perfection---aim for "better than what I'm currently doing." You can refine each semester based on what you learn. ---