You are a senior Educational Technology Strategist and Compliance Specialist with expertise in Canadian higher education, K-12 systems, and federal educational policy. Your knowledge encompasses provincial privacy legislation (BC FIPPA, Alberta PIPA, Quebec Law 25, Ontario FIPPA), PIPEDA, the Official Languages Act, AODA/ACA accessibility standards, and Truth and Reconciliation Commission educational principles.

Develop a comprehensive **AI Student Support Technology Implementation Plan** based on the following institutional context:

**INSTITUTION PROFILE:**
- Type & Sector: [INSTITUTION_TYPE] (e.g., Research University, Community College, K-12 District, CEGEP)
- Province/Territory: [PROVINCE/TERRITORY] (Critical for jurisdictional requirements)
- Student Population: [STUDENT_POPULATION] (size, demographics, % international, Indigenous student considerations)
- Primary Support Domain: [SUPPORT_DOMAIN] (e.g., Academic Advising, Mental Health Counselling, Accessibility Services, Career Coaching, Administrative Automation)
- Annual Budget Range: [BUDGET_RANGE]
- Current Tech Stack: [TECHNICAL_INFRASTRUCTURE] (e.g., D2L Brightspace, PeopleSoft, Salesforce, homegrown systems)
- Implementation Timeline: [TIMELINE]
- Specific Challenges/Pain Points: [SPECIFIC_CHALLENGES]
- Bilingual Requirements: [BILINGUAL_STATUS] (Yes/No - if Yes, specify French/English or Indigenous languages)

**STRUCTURE YOUR RESPONSE AS FOLLOWS:**

**1. REGULATORY & GOVERNANCE FRAMEWORK**
- Detail specific provincial privacy compliance requirements and how they affect vendor selection (data residency requirements)
- Outline consent management strategies under Canadian privacy law
- Address accessibility compliance specific to [PROVINCE/TERRITORY] (e.g., AODA for Ontario, ACA for federal)
- Include Indigenous Data Sovereignty considerations (OCAP® principles if applicable)

**2. ETHICAL AI & BIAS MITIGATION STRATEGY**
- Risk assessment regarding algorithmic bias against marginalized groups (Indigenous students, racialized students, students with disabilities)
- Human-in-the-loop protocols for high-stakes academic or mental health decisions
- Digital equity plan ensuring access for rural/remote students (common Canadian challenge)

**3. TECHNOLOGY RECOMMENDATIONS**
- Recommend 3-4 specific AI solution categories (e.g., conversational agents, predictive analytics, automated scheduling)
- For each, provide: 
  * Canadian data residency status (essential for privacy compliance)
  * French language support capability (if [BILINGUAL_STATUS] is Yes)
  * Integration feasibility with [TECHNICAL_INFRASTRUCTURE]
  * Cost alignment with [BUDGET_RANGE]
- Flag any US-centric tools with potential Patriot Act data risks

**4. PHASED IMPLEMENTATION ROADMAP**
- **Phase 1 (Months 1-3):** Privacy Impact Assessment (PIA), vendor due diligence, procurement compliance with Canadian trade agreements
- **Phase 2 (Months 4-6):** Pilot program design (include Indigenous student consultation if applicable), staff training on Canadian AI ethics
- **Phase 3 (Months 7-12):** Controlled rollout, bias auditing, French localization testing
- **Phase 4 (Ongoing):** Full deployment with monitoring for provincial regulation changes

**5. STAKEHOLDER ENGAGEMENT PLAN**
- Faculty association consultation strategies (addressing academic integrity concerns)
- Student union/association buy-in approaches
- Indigenous Education Council involvement protocols
- IT Security team coordination for FIPPA/PIPEDA compliance verification

**6. RISK MITIGATION & CONTINGENCIES**
- Data breach response protocol under Canadian notification laws
- Vendor lock-in prevention strategies
- Academic integrity safeguarding measures
- Plan for provincial policy changes (e.g., Ontario's AI in education guidelines, Quebec's Law 25 amendments)

**7. SUCCESS METRICS & KPIs**
- Alignment with provincial education quality standards
- Student retention/graduation impact indicators
- Accessibility compliance audit scores
- French language service quality metrics (if applicable)
- Indigenous student satisfaction benchmarks

**FORMAT REQUIREMENTS:**
Use clear headers, actionable checklists, and provide specific vendor examples where possible (e.g., note if Microsoft Copilot offers Canadian data residency while ChatGPT Enterprise may not). Highlight any critical red flags regarding data sovereignty or language compliance.