Building Vietnam's First Comprehensive AI Education ProgramsA Full Lifecycle Partnership from Design to Delivery
Project Overview
Van Lang University is undertaking one of the most consequential academic initiatives in its history: building Vietnam's first comprehensive, institution-wide AI education strategy — spanning all faculties, degree levels, and professional clusters — at a moment when no Vietnamese institution has yet attempted this at any comparable scale or depth.
This proposal outlines a multi-year, full lifecycle partnership — from assembling the right team of experts and developing the program blueprint, through curriculum design, content development, and hands-on implementation alongside Van Lang faculty and leadership. Minh Le (Principal Instructional Designer, Teachers College, Columbia University) will recruit, vet, and direct a blended team of academic and industry experts from discovery through delivery. Preliminary research and stakeholder outreach are already underway; a set of internationally recognized practitioners has been identified as potential team members (see Section 7).
The scope has two overarching goals:
Designing and delivering an AI literacy program that builds practical AI skills in students across all faculties and degree levels, grounded in a competency framework synthesized from emerging research and pioneering institutional practice.
Designing, developing, and launching a set of domain AI programs tailored to three professional clusters — Communications, Healthcare, and Business — spanning interdisciplinary minors at the undergraduate level, concentration programs at the postgraduate level, and short-form executive education.
Both goals are designed to directly support the strategic vision that VLU leadership has articulated for 2026–2030: the AI literacy program delivers the foundational capability layer that all 30 postgraduate programs in that roadmap depend on; the cluster programs advance the three postgraduate flagships prioritized for 2027 launch — the Master of Applied AI (MAI), Master of Communications (MComm), and the AI-enhanced MBA — and build on the interdisciplinary AI minors already developed through the Interdisciplinary Minors project.
No Vietnamese university has yet built AI education with this level of institutional integration — a shared competency framework, discipline-specific programs across multiple professional clusters, a governance layer, and a sustained faculty development pathway operating together. This project makes Van Lang the first to do so, establishing a national benchmark and a durable competitive advantage as AI fluency becomes a core expectation of Vietnamese employers and students.
Program Portfolio
This engagement spans programs across three audience levels and multiple professional clusters. The table below maps the full portfolio in one place — organized by cluster, with estimated launch timelines to reflect prioritization.
| Cluster | Undergraduate Minors | Postgraduate Programs | Executive Education |
|---|---|---|---|
| Communications & Media |
2026
|
2026
2027–29
2029–30
|
2027–29
|
| Business & Management |
2026
|
2026
2027–29
|
2027–29
|
| Finance & FinTech |
2026
|
2026
2027–29
|
2027–29
|
| Healthcare & Life Sciences |
2026
|
2026
2027–29
|
2027–29
|
| Technology & Governance |
2026
|
2026
2027–29
|
2027–29
|
| Foundation — All Programs |
2026
|
2026
|
AI literacy core integrated into all exec programs |
Objectives
- Build and coordinate a blended consulting team with academic and industry depth across all three clusters, approved by VLU leadership before engagement begins
- Develop an AI governance and ethics framework — covering responsible use policies, academic integrity, data privacy, and equitable access — to serve as the institutional foundation before any program launches
- Design, develop, implement, and refine an institution-wide AI literacy program — from competency framework and curriculum architecture through learning materials, delivery support, and ongoing improvement informed by student outcomes
- Design, develop, implement, and refine domain-specific AI programs for three priority clusters — Communications, Healthcare, and Business — spanning interdisciplinary minors, postgraduate concentrations, and executive education
- Co-develop instructional content alongside VLU faculty: learning modules, case studies, assessments, and teaching resources that bring each program to life in the classroom
- Establish a faculty development pathway so Van Lang faculty can teach, adapt, and sustain AI programs independently after the consulting partnership concludes
Background Research
Before scoping this project, we conducted a preliminary benchmarking study of institutions that have undertaken comparable AI education initiatives. The findings inform the overall approach and direction of this proposal — not as a definitive evidence base, but as design precedents the project team will adapt to Van Lang's academic context, regulatory environment, and cluster priorities. More thorough research will be conducted at each phase of the engagement as needed.
University of Florida — AI Across the Curriculum (QEP)
UF is the strongest direct model for VLU's AI literacy goal. Their Quality Enhancement Plan embeds AI literacy across all 16 colleges and 50,000+ students through a shared competency framework designed for cross-disciplinary adoption — building common AI fluency without requiring faculty to become AI specialists. Fully documented and peer-reviewed.
Reference: ai.ufl.edu/teaching-with-ai/ai-across-the-curriculum
AAC&U — Institute on AI, Pedagogy, and the Curriculum
AAC&U has helped 176+ institutions build AI curriculum action plans through a structured 8-month program. Their four-domain AI literacy framework — technical understanding, evaluative assessment, practical application, and ethical vigilance — is the most widely adopted framework in US higher education and a strong starting point for VLU's competency framework design.
University of Alabama — UA AI Experience (2026)
UA launched a mandatory three-hour AI literacy course assigned to all incoming students beginning Fall 2026 — one of the most recent examples of an institution-wide AI literacy mandate in the US. Demonstrates what a lightweight, scalable all-student AI orientation looks like in practice.
Reference: news.ua.edu — UA AI-Readiness Initiative
Columbia Journalism School — CJS2030 AI Initiative
Columbia Journalism School's CJS2030 initiative is the most comprehensive AI integration effort at a journalism school globally. It covers hands-on student and practitioner training, AI ethics embedded as a required course component, AI research tools (Brown Institute Magic Grants, Tow Center for Digital Journalism), and a dedicated MS in Data Journalism. The school frames AI as a tool for journalistic integrity — a positioning directly aligned with VLU's MComm cluster. The Brown Institute's faculty model (pairing technical experts with communications practitioners) is a direct template for cluster team design.
Reference: journalism.columbia.edu/CJS2030/AI
Northwestern Medill — Knight Lab AI in Media
Medill's Knight Lab has embedded dedicated AI-in-media coursework covering AI tools, storytelling innovation, and ethics into their journalism curriculum. As a top journalism school whose curriculum decisions set US standards, Medill's model is a strong design reference for VLU's AI-Powered Content and Data-Driven PR concentrations.
Inspire Higher Ed + AACSB — A Framework for AI in Business Education (2026)
Developed with GMAC and the Graduate Business Curriculum Roundtable from input across nearly 50 schools, this framework covers curriculum design, faculty development, and governance for AI in business programs. The most grounded and actionable resource available for VLU's MBA cluster.
Reference: aacsb.edu — A Framework for AI in Business Education
Queen Mary University of London — AI in Business Management (Multi-Level)
QMUL has embedded AI as a compulsory module at each undergraduate year level in their AACSB-accredited Business and Management school, creating a progressive three-year scaffolding model. Particularly relevant for designing progressive AI competency development within VLU's undergraduate Business track.
Reference: qmul.ac.uk/busman
Boston University — MS in Financial Management, AI Applications Concentration
One of the few programs at a major US research university to explicitly combine financial management with an AI applications concentration, covering machine learning for finance, algorithmic decision-making, and AI-driven analytics — directly relevant for VLU's Financial Data & Quant Analytics and FinTech & Digital Assets concentrations.
Reference: bu.edu/met — MS Financial Management AI Applications
MIT Sloan — AI and Finance Courses (2025–2026)
MIT Sloan added dedicated AI-in-finance courses in 2025–2026, including "AI and Money" and hands-on deep learning for financial applications. MIT's faculty model — pairing technical AI expertise with domain-specific finance knowledge — is a useful template for building interdisciplinary teaching teams in VLU's Finance cluster.
Reference: mitsloan.mit.edu — AI and Finance Courses
MIT Sloan Executive Education — Transforming Healthcare with AI
MIT Sloan's AI in healthcare programs combine technical AI expertise with healthcare strategy and executive leadership design. Their two-tier structure — one program for executives, one for technical practitioners — and their interdisciplinary faculty pairing model provide a direct template for VLU's Healthcare cluster across postgraduate and executive education levels.
Reference: executive.mit.edu — Transforming Healthcare with AI
Harvard Law School / Berkman Klein Center — AI and the Law
Harvard Law's executive program on AI governance, developed with the Berkman Klein Center, is the leading academic program on AI legal implications and regulatory frameworks in the US. Provides both a curriculum model and a faculty network for VLU's Digital Law & AI Governance concentration. The Berkman Klein Center's research publications are a key reference for both the institutional governance framework and the program design.
Reference: hls.harvard.edu — AI and the Law
NTU Singapore — Master of Computing in Applied AI (MCAAI)
NTU is ranked #1 in Asia and #5 globally for Data Science and AI (2025 QS rankings). Their MCAAI program bridges AI theory and real-world application with an emphasis on responsible AI deployment across industries — directly parallel to VLU's MAI program goals. As a regional peer institution, NTU is the most directly relevant Asian benchmark for MAI program design.
XJTLU — Education + AI Strategic Framework 2025–2028
Xi'an Jiaotong-Liverpool University — a private UK-China joint venture university — published a comprehensive six-pillar AI strategic framework in 2025 covering governance, curriculum, research, industry collaboration, operations, and infrastructure. As a private university navigating both Chinese regulatory requirements and international academic standards, XJTLU's phased curriculum rollout model is the closest structural parallel to VLU's context and ambitions.
Reference: XJTLU Education + AI Strategic Framework 2025–2028 (PDF)
Across all clusters, the benchmarking reveals a clear pattern: the institutions leading in AI education are those that have moved beyond adding isolated courses and into systemic integration — shared competency frameworks, interdisciplinary faculty teams, progressive multi-year scaffolding, and governance structures that support responsible use. This level of integration is exactly what Van Lang is now building — and no Vietnamese institution has yet done it. The models above confirm that this kind of initiative is achievable, and provide the design precedents the project team will draw on throughout the engagement. Van Lang is not catching up to a Vietnamese peer: it is setting the standard.
Scope of Work
This engagement is organized into six phases. Phases 1 and 2 run concurrently from the start. Phase 3 begins once the team is in place. Phase 4 runs in two waves — Communications and Business first, Healthcare second. Phases 5 and 6 are rolling, beginning per cluster as earlier phases complete. Click any phase to expand activities and key deliverables.
1
Weeks 1–6
Discovery, Strategic Alignment, and Governance Framework
Runs with Phase 2 from Week 2
- Conduct structured discovery sessions with VLU leadership and faculty to assess current AI readiness, faculty capacity, technology infrastructure, and institutional culture
- Map the full project scope to VLU's institutional roadmap and confirm cluster priorities, launch targets, and success metrics
- Define criteria and profile requirements for expert team recruitment
- Develop the institutional AI Governance and Ethics Framework: policies covering responsible AI use, academic integrity in AI-assisted work, data privacy, and equitable access — to serve as the foundational guardrails before any program launches
- Establish a lightweight evaluation approach: define the key indicators that will be tracked across all programs to inform ongoing refinement (student learning outcomes, faculty confidence, program satisfaction)
- AI Governance and Ethics Framework — a practical institutional policy document that gives VLU clear, enforceable guidance on how AI is used across teaching, learning, and administration before any new program launches. The framework is designed for three audiences and levels of implementation:
- For students: an Acceptable Use Policy for AI in academic work — defining what AI assistance is permitted, where disclosure is required, and what constitutes a violation. Includes guidance specific to different assessment types (exams, essays, group projects, capstone work).
- For faculty: a set of course-level AI integration guidelines — helping faculty decide how to handle AI in their courses, with practical examples for allowing, restricting, or building AI use into assessments intentionally. Includes guidance on detecting misuse and responding to edge cases.
- For the institution: a governance structure for ongoing oversight — defining who is responsible for reviewing and updating AI policies as the technology evolves, how student and faculty concerns are escalated, and how VLU's AI practices align with emerging Vietnamese and international regulatory standards (including MOET guidelines on academic integrity).
- Discovery and alignment report (cluster priorities, launch targets, success metrics confirmed)
- Expert team recruitment brief
2
Weeks 2–8
Expert Team Assembly
Concurrent with Phase 1
- Recruit, vet, and onboard a blended consulting team of academic and industry experts for each cluster
- Confirm team composition with VLU leadership before design work begins
- Establish collaboration protocols, roles, and deliverable ownership for each subsequent phase
- Expert Team Composition Report — recommended team with profiles, roles, and onboarding plan, approved by VLU leadership
3
Weeks 7–16
AI Literacy Program Design
Begins once core team is in place · Concurrent with Phase 4a
- Design the institution-wide AI literacy competency framework: define competency tiers (informed by research and benchmarking), learning outcomes per tier, and a general education integration model
- Design the actual AI literacy program: course structure, instructional activities, and progression pathways for students across all faculties and degree levels
- Produce a curated external resource map — identifying and organizing existing high-quality AI learning resources from vendors (Google AI Essentials, Microsoft AI Fundamentals) and MOOCs (Coursera AI for Everyone, edX/IBM offerings) by competency level, so VLU does not build foundational content from scratch
- Develop per-cluster faculty integration guides showing how AI literacy connects to each discipline's context
- Design a faculty readiness and adoption plan: implementation sequence, support structures, and internal AI champion model
- AI Literacy Competency Framework — a synthesized competency model drawn from leading institutional frameworks, emerging research, and practitioner models (including EDUCAUSE's four-domain AI literacy framework, UNESCO's AI competency guidance, and program models from University of Florida, Arizona State University, and other pioneering institutions).
| Level | Target Audience | Core Competencies |
|---|---|---|
| AI Awareness | All incoming students | Understanding how AI systems work and where they fail; identifying appropriate and inappropriate uses of AI; applying responsible use principles in academic and professional contexts; recognizing ethical implications (bias, privacy, attribution) |
| AI Application | Core disciplinary courses, mid-program | Using AI tools purposefully within a discipline; critically evaluating the quality and reliability of AI outputs; adapting AI workflows to discipline-specific professional standards; communicating transparently about AI-assisted work |
| AI Fluency | Advanced coursework and professional programs | Designing AI-augmented workflows for professional contexts; evaluating AI systems from a governance and ethics perspective; leading AI adoption initiatives; supporting others' AI literacy development |
- AI Literacy Program Design — the program structure that delivers the framework in practice, differentiated by audience level:
- Undergraduate students: A required AI Literacy orientation module integrated into the first-year student experience — delivered as a short, structured program (approximately 15–20 hours, equivalent to a 1-credit module) covering AI Awareness-level competencies.
- Postgraduate students: AI literacy is integrated directly into degree program coursework rather than delivered as a standalone requirement. Each postgraduate program includes a designated AI Application module in its core curriculum (typically one 3-credit course or its equivalent), with AI Fluency outcomes embedded into capstone or research methodology courses.
- Executive Education participants: AI literacy for executive cohorts is delivered as a condensed, application-focused workshop (typically half-day to one full day) embedded within each short program.
- Curated External Resource Map — a structured library of existing high-quality AI learning resources mapped to competency levels and discipline clusters
- Per-Cluster Faculty Integration Guides — practical guidance for each discipline cluster on how AI literacy connects to their field's professional standards, common tools, and ethical considerations
- Faculty Readiness and Adoption Plan — implementation sequence, internal AI champion model, tiered professional development pathway, and support structures for faculty at different levels of AI confidence
4a
Weeks 9–20
Cluster Design — Communications and Business
Concurrent with Phase 3
For each cluster, deliver full curriculum design across three program formats:
Postgraduate and Undergraduate concentrations (MOET-track programs):
- Program architecture and overall design rationale
- Course titles and descriptions
- Credit structure and program duration
- Learning outcomes per course
- Assessment framework and progression logic across levels
- MOET-ready program documentation
Executive Education (short-format B2B programs):
- Program concept and target audience profile
- Learning objectives and modular curriculum outline (2–5 day format)
- Delivery model recommendations (in-person, blended, cohort-based)
- Partner engagement model and pricing structure guidance
- Signature AI Program Architecture: Communications — full curriculum design at UG, PG, and Exec Ed levels
- Signature AI Program Architecture: Business — full curriculum design at UG, PG, and Exec Ed levels
4b
Weeks 16–30
Cluster Design — Healthcare
Follows Phase 4a · Concurrent with Phase 5 for earlier clusters
Same scope as Phase 4a, applied to the Healthcare cluster. Runs on a later timeline given the 2028–2029 launch horizon, and benefits from lessons learned in Phase 4a.
- Signature AI Program Architecture: Healthcare — full curriculum design at UG, PG, and Exec Ed levels. Illustrative structure:
| Level | Program Type | Duration & Core Themes |
|---|---|---|
| Undergraduate | AI in Healthcare elective or integration module | 1 semester — Digital health fundamentals, health data literacy, AI tools in clinical and administrative practice |
| Postgraduate | Master of Applied AI: Healthcare concentration | 2 years — Health data analytics, AI in diagnostics and care delivery, digital health strategy, regulatory and ethical frameworks |
| Executive Education | AI for Healthcare Organizations | 3–5 days — AI strategy for healthcare organizations, implementation planning, change leadership, vendor and technology assessment |
5
Rolling · 14–20 wks / cluster
Content Development and Faculty Co-Creation
Begins per cluster after Phase 4 approval · Continues beyond Week 30
- Work alongside VLU faculty as co-developers to produce course learning modules, structured lesson sequences, and in-class activities for each cluster AI program
- Develop Vietnamese AI case studies and industry examples localized to each cluster: Communications examples from the Vietnamese media and creator economy landscape; Healthcare examples from Vietnam's digital health sector; Business examples from the China+1 FDI environment and Vietnamese startup ecosystem
- Design and produce assessment frameworks, rubrics, and sample assessment items for each course
- Build a shared teaching resource library mapped to the AI literacy competency levels, organized for ongoing faculty use and extension
- Facilitate faculty co-creation workshops per cluster: faculty participate as content contributors, not just recipients, building ownership and internal sustainability
- Develop faculty facilitation guides for each course: pedagogical notes, discussion prompts, AI tool integration guidance, and suggestions for keeping content current as the AI landscape evolves
- Content Development Package: Communications — learning modules, Vietnamese case studies (media/creator economy), assessment items, faculty facilitation guides, and teaching resource library
- Content Development Package: Business — same scope, localized to the China+1 FDI and Vietnamese business context
- Content Development Package: Healthcare — same scope, localized to Vietnam's digital health sector
6
Rolling · through 2030
Implementation Support and Evaluation
Concurrent with each program launch · Ongoing through 2027–2030
- Develop a phased implementation roadmap per cluster as each workstream completes, aligned with the 2027–2030 launch milestones
- Provide advisory support during the first semester of program delivery for each cluster
- Collect structured feedback from faculty and students after each first delivery cycle and produce a refinement brief per cluster with recommended adjustments
- Produce a faculty capacity-building plan for internal sustainability once the consulting partnership scales back
- Deliver a final consolidated report covering all program designs, the AI literacy framework, content packages, governance framework, and implementation guidance
- Implementation Roadmap — per-cluster rollout plan aligned with 2027–2030 milestones, including launch sequencing and risk considerations
- Post-Delivery Refinement Briefs — one per cluster after first semester, with data-informed recommendations for adjustment
- Faculty Development and Sustainability Plan — training pathways, capacity-building roadmap, and internal champion model
- Final Consolidated Report — complete record of all program architectures, AI literacy framework, content packages, governance framework, and implementation guidance
Summary of Deliverables
The table below consolidates all deliverables across the six phases for quick reference. Expand any phase in Section 5 above for full descriptions, implementation details, and illustrative samples.
| Deliverable | Phase | Type |
|---|---|---|
| AI Governance and Ethics Framework | Phase 1 | Policy document — student, faculty, and institutional tiers |
| Discovery and Alignment Report | Phase 1 | Internal report — confirmed priorities, targets, and metrics |
| Expert Team Recruitment Brief | Phase 1 | Specification document — profile requirements and selection criteria |
| Expert Team Composition Report | Phase 2 | Team proposal — profiles, roles, onboarding plan; approved by VLU leadership |
| AI Literacy Competency Framework | Phase 3 | Framework document — three-tier competency model with learning outcomes |
| AI Literacy Program Design | Phase 3 | Program blueprint — structure and delivery model for UG, PG, and Exec Ed |
| Curated External Resource Map | Phase 3 | Resource library — vendor and MOOC materials mapped to competency levels and clusters |
| Per-Cluster Faculty Integration Guides | Phase 3 | Practitioner guides — one per cluster, connecting AI literacy to professional context |
| Faculty Readiness and Adoption Plan | Phase 3 | Implementation plan — development pathway, champion model, support structures |
| Signature AI Program Architecture: Communications | Phase 4a | Curriculum design package — UG, PG, and Exec Ed levels |
| Signature AI Program Architecture: Business | Phase 4a | Curriculum design package — UG, PG, and Exec Ed levels |
| Signature AI Program Architecture: Healthcare | Phase 4b | Curriculum design package — UG, PG, and Exec Ed levels |
| Content Development Package: Communications | Phase 5 | Content package — learning modules, case studies, assessments, facilitation guides |
| Content Development Package: Business | Phase 5 | Content package — same scope, localized to the Vietnamese business context |
| Content Development Package: Healthcare | Phase 5 | Content package — same scope, localized to Vietnam's digital health sector |
| Implementation Roadmap | Phase 6 | Rollout plan — per-cluster sequencing aligned with 2027–2030 milestones |
| Post-Delivery Refinement Briefs | Phase 6 | Evaluation briefs — one per cluster after first semester, with adjustment recommendations |
| Faculty Development and Sustainability Plan | Phase 6 | Capacity-building plan — training pathways and internal champion model |
| Final Consolidated Report | Phase 6 | Master record — all program architectures, frameworks, content packages, and implementation guidance |
Proposed Initial Expert Team
This engagement is led by Minh Le as Project Lead and organized across five areas of work. Each area has a defined responsible party before individual contributors are named, so the engagement structure is clear and stable as the team is assembled and confirmed with VLU leadership.
| Area of Work | Responsibilities | Responsible Party |
|---|---|---|
| Project Leadership and Coordination | Directs the consulting team; manages the VLU relationship; sequences phases and phase gates; holds final accountability for all deliverables | Minh Le (Project Lead) |
| AI Literacy and Governance | Designs the institution-wide competency framework and program structure; develops the AI Governance and Ethics Framework; advises on responsible use standards | AI Literacy Advisor + Minh Le |
| Communications Cluster | Leads all curriculum design and content development for Communications programs across UG, PG, and Exec Ed levels; co-facilitates faculty workshops for this cluster | Communications Cluster Lead |
| Business Cluster | Leads all curriculum design and content development for Business programs across UG, PG, and Exec Ed levels; co-facilitates faculty workshops for this cluster | Business Cluster Lead |
| Healthcare Cluster | Leads all curriculum design and content development for Healthcare programs across UG, PG, and Exec Ed levels; co-facilitates faculty workshops for this cluster | Healthcare Cluster Lead |
| Implementation and Institutional Strategy | Advises on rollout sequencing, faculty adoption models, and institutional change management; draws on cross-institutional implementation experience | Implementation Advisor |
| VLU Faculty Co-Development | Participates in co-design workshops; contributes local domain expertise and Vietnamese professional context; reviews program designs for institutional fit | VLU Faculty Representatives (per cluster) |
| Institutional Decision-Making | Approves team composition, phase deliverables, and program designs; provides institutional access and leadership decisions at each phase gate | VLU Leadership |
Based on the benchmarking research, we have identified the following individuals as strong candidates for each role. Outreach is currently in progress. All team members are subject to VLU leadership approval; formal onboarding will occur in Phase 2.
| Recommended Expert | Affiliation | Role | Rationale |
|---|---|---|---|
| Jane Southworth | University of Florida, QEP Lead | AI Literacy Advisor | Led the design of UF's institution-wide AI literacy initiative — the closest international equivalent to this project's Goal 1. Brings deep expertise in cross-disciplinary AI curriculum design and faculty engagement at scale. |
| Hannah Schneider | AAC&U, Director of Digital Education Programs | Implementation Advisor | Oversees AI curriculum programs serving 176+ institutions. Can advise on implementation structure and connect the team to additional practitioners from the AAC&U network. |
| Tawnya Means | Inspire Higher Ed, Co-founder and Principal | Business Cluster Lead | Lead author of the AACSB/GMAC AI in Business Education framework. Consults directly with business schools on AI program integration; the most practically grounded expert available for the Business cluster. |
| Paul McDonagh-Smith | MIT Sloan Executive Education, Visiting Senior Lecturer | Healthcare Cluster Lead | Co-teaches MIT Sloan's "Transforming Healthcare with AI" program. Brings executive education design experience and is accessible for external consulting engagements. |
| Communications Expert | TBD | Communications Cluster Lead | To be identified through the AAC&U network or direct outreach. Target profile: AI and media/communications program design at the graduate or executive level, with experience in creative and generative AI applications. |
Team and Engagement Model
Timeline
Phases are sequenced around VLU's program launch milestones, not a generic calendar. Phases 1 and 2 begin immediately and run concurrently; all subsequent phases are gated on the milestone they support.
Phase Sequencing
| Phase | Target Window | VLU Milestone Supported |
|---|---|---|
| Phase 1: Discovery, Alignment, and Governance | Q2–Q3 2026 (Weeks 1–6) | Governance framework and confirmed priorities in place before any program launches |
| Phase 2: Expert Team Assembly | Q2–Q3 2026 (Weeks 2–8, concurrent) | Expert team approved and onboarded before design work begins |
| Phase 3: AI Literacy Program Design | Q3 2026 (Weeks 7–16) | AI literacy framework and program design complete ahead of AY 2026–27 undergraduate rollout |
| Phase 4a: Communications + Business Cluster Design | Q4 2026 – Q1 2027 (Weeks 9–20) | Program architectures ready for 2027 concentration openings (Data-Driven PR, AI-Augmented Management) |
| Phase 4b: Healthcare Cluster Design | 2027 (Weeks 16–30) | Healthcare architecture complete ahead of 2028–2029 launch target |
| Phase 5: Content Development | Rolling — begins per cluster after Phase 4 approval | Communications + Business: Q1–Q2 2027; Healthcare: 2027–2028 |
| Phase 6: Implementation Support | Rolling — concurrent with each launch through 2030 | Advisory support through first semester of each launch; final report at full program launch |
Alignment with VLU Launch Milestones
| Year | VLU Milestone | This Project's Contribution |
|---|---|---|
| 2026 | Launch 4 flagship core programs (MBA, MFin, MComm, MAI) | Phase 1 governance framework in place; AI literacy design underway |
| AY 2026–27 | Layer 1 AI Capability Foundation across all programs; Phase 1 UG AI minors | Phase 3 complete: AI literacy framework, program design, and faculty integration guides delivered |
| 2027 | Open Data-Driven PR (MComm) + AI-Augmented Management (MBA) concentrations | Phase 4a complete; Communications and Business content development in progress |
| 2028 | Open Creative Tech & IP, AI Supply Chain concentrations | Phase 5: Communications and Business content packages complete and in use |
| 2029 | Open AI in Healthcare & Life Sciences concentration | Phase 4b + Phase 5: Healthcare design and content development complete |
| 2030 | 40+ programs, 5,000–8,000 students/year | All cluster architectures, content packages, and faculty development plans operational |
Investment
This is a multi-phase, multi-year engagement. Investment is structured by phase, with each phase fee confirmed before work begins. A full investment schedule will be proposed at the conclusion of Phase 1, once scope, team composition, and launch sequencing are confirmed.
Payment terms 50% upon commencement of each phase, 50% upon delivery of that phase's deliverables.
Terms and Conditions
- This proposal is valid for 30 days from the date of issue.
- Scope changes requested after a phase has commenced may be subject to additional fees; any changes will be agreed upon in writing before work proceeds.
- Van Lang University will provide timely access to relevant stakeholders, institutional documents, faculty contacts, and leadership decisions needed to complete the work.
- All frameworks, curricula, and deliverables produced under this engagement are for Van Lang University's institutional use. Minh Le retains the right to reference the engagement in a general professional capacity without disclosing proprietary institutional content.
- Either party may terminate the engagement with 14 days written notice; work completed to that point will be billed at the agreed rate.