Of course. This is a critical question for the future of higher education. Here is a comprehensive framework for how universities can redesign their degree programs to prepare students for an AI-transformed workplace.

The fundamental shift is away from knowledge dissemination (which AI can do better and faster) and towards human capability development. The goal is no longer to create walking encyclopedias, but to cultivate agile, critical, and creative thinkers who can partner with AI to solve complex problems.

Here is a multi-layered approach to this redesign.

I. The Core Philosophical Shift: From "Knowing" to "Being and Doing"

Before changing a single course, the university's mindset must evolve.

From (Old Model)	To (AI-Ready Model)
Siloed Disciplines	Interdisciplinary Synthesis (Real-world problems don't fit in one department)
Knowledge Transfer	Skills Application & Problem-Solving (Emphasis on process, not just facts)
"One-and-Done" Degree	Lifelong Learning Ecosystem (The degree is the start, not the end)
Humans vs. Machines	Humans-in-the-Loop (Focus on collaboration with AI as a tool)
Standardized Assessment	Authentic, Portfolio-Based Assessment (Show what you can do, not what you can recall)

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II. The Redesign Framework: Five Pillars of Change

1. Curriculum and Content Redesign

The "what" of learning must be fundamentally rethought.

• AI Literacy as a Core Competency: Just like writing and quantitative reasoning, every student, regardless of major (from History to Physics), must graduate with foundational AI literacy. This is not about coding; it's about:

  • Capabilities & Limitations: What can AI really do? Where does it fail?
  • Critical Evaluation: How to spot AI-generated misinformation and "hallucinations."
  • Prompt Engineering & Interaction: How to ask good questions to get useful outputs from generative AI.
  • Ethical Implications: Understanding bias, privacy, and the societal impact of AI systems.

• The "Human+Tech" Stack: Every degree should be a "T-shaped" program, combining deep domain expertise with broad, transferable skills.

  • The Vertical Bar (Deep Major): Retain deep, specialized knowledge in a chosen field (e.g., Biology, Finance, Art History). This expertise is crucial for guiding AI and validating its outputs.
  • The Horizontal Bar (Broad Skills): Integrate a mandatory cross-disciplinary layer that includes data literacy, systems thinking, design principles, ethics, and communication. A literature student might take a course on "Narrative Analysis with Natural Language Processing." A business student might take "Ethical Frameworks for Algorithmic Decision-Making."

• Dynamic and Modular Curriculum: The 4-year, rigid curriculum is too slow.

  • Shift to Modules: Design degrees around smaller, stackable modules and certificates that can be updated annually.
  • Problem-Based Cores: Replace generic "Intro to X" courses with interdisciplinary, problem-based seminars like "Solving Global Water Scarcity" or "The Future of Urban Mobility," which draw on science, policy, economics, and ethics.

2. Pedagogy and Assessment Redesign

The "how" of learning and measurement is where the most significant change occurs.

• AI as a Tool, Not a Taboo: Actively integrate AI into the classroom. The policy should not be "don't use ChatGPT," but "use ChatGPT, and here's how to do it critically and ethically."

  • Assignment Redesign: Instead of "Write an essay on Hamlet," the prompt becomes: "Use an AI to generate three different interpretations of Hamlet's motivations. Then, write an essay that critiques these interpretations, identifies their weaknesses, and synthesizes a more nuanced argument of your own, citing the AI's output and your own research."
  • Focus on Process: Assessment should value the student's process: their queries to the AI, their fact-checking, their a-ha moments, and their ethical considerations. This is demonstrated through logs, reflections, and presentations.

• Project-Based Learning (PBL) as the Default: Shift the center of gravity from lectures to hands-on, collaborative projects that mirror the workplace. Class time becomes a workshop or a studio space for collaboration, mentored by faculty.

• Authentic Assessment: Move away from exams that test rote memorization.

  • Portfolio-Based Finals: Students graduate with a professional portfolio (e.g., code repositories, marketing campaigns, policy briefs, design prototypes) that demonstrates their skills.
  • Oral Defenses: Require students to defend their work and thinking process, forcing them to articulate their choices and demonstrate true understanding.

3. Structural and Experiential Redesign

The "where" and "when" of learning must break out of the ivory tower.

• Mandatory Experiential Learning: Internships, co-ops, clinicals, and research projects should no longer be optional add-ons. They must be woven into the fabric of the degree, with academic credit given for structured reflection on the experience.

• Porous Walls with Industry:

  • "Professors of Practice": Hire industry leaders to co-teach courses.
  • Corporate "Challenge" Projects: Partner with companies to have students work on real, current business problems.
  • Lifelong Learning Portals: Create university platforms for alumni to access new skills modules, workshops, and AI updates, positioning the university as a career-long partner.

4. Human-Centric Skills Development

These are the "AI-proof" skills that must be explicitly taught and practiced across all disciplines.

• Complex Problem-Solving: Tackling ambiguous, multi-faceted problems with no clear answer.
• Critical & Creative Thinking: The ability to analyze, question, synthesize, and generate novel ideas.
• Emotional & Social Intelligence: Leadership, empathy, collaboration, negotiation, and cross-cultural communication.
• Adaptability & Learning Agility: The skill of learning new things quickly and being comfortable with uncertainty.
• Ethical and Systems Thinking: The ability to see the big picture, understand interconnectedness, and make decisions grounded in human values.

5. Faculty and Infrastructure Investment

None of this is possible without supporting the educators.

• Continuous Faculty Development: Universities must invest heavily in training faculty on how to use AI tools, design new types of assignments, and transition to a coaching/mentoring role.
• Technological Infrastructure: Provide campus-wide access to premium AI tools, data sets, and simulation platforms, treating them as essential library resources.

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Example: The Redesigned "Bachelor of Arts in History"

Old Model	AI-Ready Redesign
Core Courses: Survey of World History, Research Methods, Historiography.	Core Courses: The Human Story: A Problem-Based Intro, Data Analysis & Digital Archives, Ethics of Historical Narratives.
Typical Assignment: A 15-page research paper on the French Revolution.	Typical Assignment: "Use AI to analyze 10,000 digitized pamphlets from the French Revolution. Identify the three most prevalent themes. Now, write a paper arguing whether the AI's data-driven analysis confirms or challenges the traditional historiography you've read. Create a public-facing website to present your findings."
Final Exam: Essay questions recalling dates and key figures.	Final Project: A digital museum exhibit or a podcast series on a historical topic, complete with a portfolio documenting the research process and a defense of methodological choices.
Skills: Writing, research.	Skills: Writing, research, data analysis, prompt engineering, digital communication, project management, ethical reasoning.

Conclusion

Redesigning universities for an AI-transformed world is not about adding a few "AI courses." It is a fundamental re-imagining of the purpose of higher education. The university of the future will succeed if it stops being a gatekeeper of information and becomes an incubator for human potential—producing graduates who are not just knowledgeable, but are adaptable, creative, and wise enough to steer a world filled with powerful technology.

Of course. This is a critical question for the future of higher education. Universities must undergo a fundamental redesign, moving from a model of knowledge transfer to one of skill development, adaptability, and human-centric learning.

Here is a comprehensive framework for how universities can redesign their degree programmes for an AI-transformed workplace, broken down into core principles, specific strategies, and implementation challenges.

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I. Core Guiding Principles for the Redesign

Before diving into specific changes, universities must adopt a new philosophy.

1. From "Knowledge-Based" to "Skill-Based": The value of memorizing information that an AI can retrieve in seconds has plummeted. The new focus must be on durable, human-centric skills that complement AI.
2. Human-AI Collaboration as the Default: Students shouldn't be trained to compete with AI, but to collaborate with it. AI should be treated as a powerful tool—a "cobot" or "exoskeleton for the mind"—that augments human capabilities.
3. Embrace Lifelong Learning: The concept of a four-year degree as a "finished" product is obsolete. Universities must become springboards for continuous, lifelong learning, offering flexible and ongoing education.
4. Interdisciplinarity is Non-Negotiable: Real-world problems that AI will help solve (e.g., climate change, public health, supply chain logistics) do not exist in neat departmental silos.

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II. Specific Redesign Strategies

A. Curriculum and Content Redesign

1. Integrate an "AI Literacy" Core for All Students:

   • Just as every student takes basic writing and math, every student should complete a foundational course in "AI Literacy." This is not a coding class. It should cover:
     • Conceptual Understanding: What are large language models (LLMs), neural networks, and machine learning? How do they "think"?
     • Practical Application: Prompt engineering, using AI for brainstorming, data analysis, and content creation.
     • Ethical and Critical Lens: Understanding bias in AI, data privacy, misinformation, and the societal impact of AI technologies.

2. Develop "T-Shaped" Professionals:

   • The Vertical Bar (Deep Expertise): Students still need deep knowledge in their major (e.g., biology, finance, history). This core knowledge provides the context for applying AI effectively.
   • The Horizontal Bar (Broad, Cross-Disciplinary Skills): This is where the major redesign occurs. Integrate a mandatory, interdisciplinary core that focuses on:
     • Computational Thinking: Breaking down complex problems into logical steps that a system (human or AI) can execute.
     • Systems Thinking: Understanding how different parts of a complex system interact—essential for implementing AI solutions.
     • Data Literacy: The ability to read, interpret, analyze, and argue with data.

3. Embed "Human-Centric" Skills Across All Disciplines:

   • These are the skills that are currently difficult, if not impossible, for AI to replicate. They must be explicitly taught and assessed in every major.
   • Critical Thinking & Complex Problem-Solving: Move beyond textbook problems to messy, real-world case studies with incomplete information.
   • Creativity & Innovation: Use AI for divergent thinking (generating ideas) and then teach students the convergent thinking process of refining, combining, and selecting the best ones.
   • Emotional Intelligence & Empathy: Essential for leadership, teamwork, and any client-facing role. Use simulations, role-playing, and humanities-based analysis of human behaviour.
   • Communication & Persuasion: With AI handling first drafts, the premium is on high-level Gistediting, storytelling, and presenting arguments persuasively to diverse audiences.

4. Create a Dynamic and Modular Curriculum:

   • Stackable credentials: Allow students to earn micro-credentials or certificates in high-demand areas (e.g., "AI for Marketing Analytics," "Ethical AI Governance") that can be stacked towards a full degree or added post-graduation.
   • Rapid-release modules: Offer short, intensive courses on emerging technologies (e.g., a one-credit, three-week course on the latest generation of AI models) to keep the curriculum current.

B. Pedagogy and Assessment Transformation

1. Shift from Lecture to "Challenge-Based Learning":

   • The traditional lecture-and-exam model is broken in an AI world. Replace it with projects, case studies, and simulations.
   • Example (Marketing): Instead of an exam on the 4 P's of marketing, have students use an AI to generate ten potential marketing campaigns for a new product. Their task is to critique the AI's output, select the most viable option, refine the strategy, and present a full-fledged launch plan, defending their choices against critique.
   • Example (History): Instead of writing an essay on the causes of World War I, have students use an AI to simulate diplomatic conversations between nations. Their task is to analyze the simulation, identify key turning points, and write a policy memo arguing what a different decision might have changed.

2. Integrate AI as a Mandatory Tool (Don't Ban It):

   • Develop clear academic integrity policies that distinguish between cheating and effective collaboration with AI.
   • Require students to cite AI tools just as they would any other source.
   • Teach students to be critical consumers of AI output, a process that includes fact-checking, identifying bias, and refining generic text into a distinct voice.

3. Reinvent Assessment:

   • If an AI can pass the test, the test is obsolete. Assessments must target higher-order skills.
   • Oral Defenses and Vivas: Require students to verbally defend their project work and answer tough, unscripted questions.
   • Portfolio-Based Assessment: Evaluate students based on a portfolio of projects they've completed throughout their degree, showcasing their growth and abilities.
   • Live, Collaborative Problem-Solving: Assess teams of students as they work together, using AI tools, to solve a novel problem in real-time.

C. Structural and Experiential Changes

1. Deep Industry and Community Integration:

   • Move beyond optional internships. Make credit-bearing, co-curricular "residencies" a mandatory part of the degree. Students should work on real problems in real organizations, using the same AI tools as professionals.
   • Bring in "Professors of Practice"—industry experts who can co-teach courses and provide up-to-the-minute insights.

2. Invest Heavily in Faculty Development:

   • Faculty are the bottleneck. Universities must provide sustained training, resources, and incentives for professors to redesign their courses and learn to use AI in their own research and teaching.

3. Establish Interdisciplinary "Sandbox" Environments:

   • Create AI labs or innovation hubs where students from arts, humanities, engineering, and business can collaborate on projects, experiment with new AI tools, and build prototypes without the pressure of a specific course grade.

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Conclusion

The goal is not to create "AI-proof" graduates, because no such thing exists. The goal is to cultivate "AI-ready" graduates: adaptable, critical thinkers who can harness powerful technologies to solve complex human problems.

Universities that cling to the 20th-century model of siloed knowledge dissemination will become irrelevant. Those that embrace this moment as an opportunity to fundamentally re-center education on human creativity, collaboration, and critical thought will not only survive but will become more essential than ever.

Of course. This is a critical question for the future of higher education. Redesigning university degree programmes for an AI-transformed workplace requires a fundamental shift, not just incremental changes. It's about moving from a model of knowledge transmission to one of skills cultivation and adaptive learning.

Here is a comprehensive framework for how universities can redesign their programmes, broken down into four key pillars.

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Pillar 1: Reimagining the Curriculum and Content

The core of what students learn must be re-evaluated. The goal is no longer just deep disciplinary knowledge, but what can be described as a "T-shaped professional 2.0": deep expertise in a major (the vertical bar) combined with a broad, tech-infused understanding of other domains (the horizontal bar).

1. A New Foundational Core: AI Literacy for All Every single graduate, regardless of their major (History, Nursing, Business, Art), must have a baseline level of AI literacy. This foundational requirement would replace or augment traditional general education courses. It should include:

• Conceptual Understanding: What is AI (machine learning, LLMs, neural networks)? How does it learn? What are its capabilities and, importantly, its limitations?
• Practical Application: Hands-on experience using common AI tools for research, ideation, content creation, and data analysis (e.g., using ChatGPT for brainstorming, Midjourney for concept art, or data analysis tools for social science research).
• Ethical and Societal Implications: Critical examination of bias in algorithms, data privacy, job displacement, and the philosophical questions AI raises about creativity and consciousness.

2. Integrating AI into the Major Instead of siloing AI in the computer science department, it must be woven into the fabric of every discipline.

• Humanities: A Literature student should learn to use natural language processing (NLP) to analyze texts at scale or study how AI is changing narrative structures. A History student could use AI to analyze vast archives of digitized records.
• Sciences: A Biology student should work with AI models that predict protein folding (like AlphaFold) or analyze genomic data. An Environmental Science student could use AI to model climate change impacts.
• Arts: A Music student could learn to compose with AI-assisted tools. A Fine Arts student could explore generative AI as a new creative medium while also critiquing its aesthetic and ethical boundaries.
• Professional Programmes: A Business student must master AI-driven marketing personalization and supply chain optimization. A Law student needs to understand AI's role in legal research and e-discovery, as well as the laws governing AI itself.

3. Shift Focus from "Known" Knowledge to "Unknown" Problems Curriculums are often based on a canon of established knowledge. The new curriculum must prioritize teaching students how to solve problems where the answer isn't in a textbook.

• Interdisciplinary "Challenge Labs": Create credit-bearing courses centered on grand challenges (e.g., "AI for Sustainable Agriculture" or "AI and the Future of Democracy"). These would bring together students from engineering, policy, ethics, and design to work on complex, real-world problems.

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Pillar 2: Evolving Pedagogy and Assessment

How we teach is just as important as what we teach. AI's ability to generate content makes traditional assessment methods (like the five-paragraph essay) increasingly obsolete.

1. The Professor as "Guide on the Side," Not "Sage on the Stage" The professor's role shifts from being the primary source of information to being a coach, a facilitator, and a critical thinking partner. Their value lies in asking the right questions, challenging AI-generated outputs, and mentoring students through complex projects.

2. AI as a Learning Co-Pilot, Not a Cheating Tool Instead of banning AI tools, universities must formally integrate them into the learning process and teach students how to use them effectively and ethically.

• Process-Oriented Assessment: Grade students not on the final output, but on their process. For an essay, this could involve submitting their prompts, the AI's initial drafts, and a critical reflection on how they edited, fact-checked, and added their own original insights.
• New Assessment Formats: Emphasize oral exams, project demonstrations, portfolio defences, and collaborative team-based evaluations where students must defend their work and thinking in real-time.

3. Hyper-Personalized Learning Paths Use AI to create adaptive learning environments. An AI tutor could identify that a student is struggling with a specific statistical concept and provide them with targeted exercises and resources, freeing up professor time for higher-level instruction.

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Pillar 3: Prioritizing Essential Human Competencies

As AI handles more routine analytical and cognitive tasks, skills that are uniquely human become far more valuable. Universities must explicitly teach and cultivate these.

1. Critical and Creative Thinking:

• Teach students to be expert "AI-skeptics." They must learn to deconstruct and question AI outputs, identify potential biases, and verify information.
• Foster creativity not as coming up with something from nothing, but as the ability to connect disparate ideas, ask novel questions, and use AI as a tool for divergent thinking.

2. Collaboration and Communication:

• AI is a powerful tool, but it doesn't collaborate. Programmes must be rich with team-based projects that simulate a modern workplace, requiring students to communicate complex ideas, navigate interpersonal dynamics, and provide constructive feedback.

3. Adaptability and Learnability (AQ - Adaptability Quotient):

• The most important skill will be the ability to learn, unlearn, and relearn. The curriculum should bake in this principle. Perhaps a degree requirement is to learn a new high-demand software or framework every year.

4. Ethical Reasoning and Emotional Intelligence (EQ):

• As AI makes decisions with real-world consequences, the need for human oversight grounded in ethical principles is paramount. Every field needs professionals who can ask "Just because we can do this with AI, should we?"
• Skills like empathy, persuasion, and client management will be differentiators in fields from sales to medicine.

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Pillar 4: Structural and Institutional Reforms

These curricular and pedagogical changes are impossible without deep changes to the university structure itself.

1. Agile Curriculum Development: The traditional 5-7 year cycle for curriculum review is obsolete. Universities need nimble, faculty-led committees (with industry advisors) that can approve new courses, specializations, and micro-credentials in months, not years.

2. Micro-credentials and Stackable Degrees: The 4-year monolithic degree will be complemented by a more flexible, lifelong learning model. Universities should offer:

• Stackable Certificates: A student might earn a B.A. in Psychology and "stack" on a credential in "AI-driven People Analytics."
• Continuing Education for Alumni: Offer alumni subscriptions to stay current, with access to short courses on the latest AI developments in their field.

3. Radical Faculty Development: Universities must invest heavily in training their own faculty. This includes not just technical workshops but sustained support for professors to redesign their courses and research agendas around AI. This should be a key criterion for tenure and promotion.

4. Deep Industry and Research Partnerships: This must go beyond token advisory boards.

• Co-created Curricula: Design courses and entire degree programmes with industry partners to ensure they meet real-world needs.
• "Professionals-in-Residence": Bring in AI practitioners from industry to co-teach courses.
• Access to Real-World Data & Tools: Partnerships should provide students with access to the same large datasets and sophisticated AI platforms they will use in the workforce.

Example: The Redesigned History Degree

• Traditional: Focuses on historiography, period studies, and archival research.
• AI-Transformed:
  • Core: Retains courses on critical thinking, argumentation, and historical narrative.
  • New Requirements: Adds the university's AI Literacy core.
  • New Courses: "Digital Archives and AI," "Computational Analysis of Historical Texts," "Visualizing Historical Data."
  • Capstone Project: Instead of just a thesis, a student might create an interactive digital exhibit using AI to analyze and display patterns from thousands of historical newspapers, alongside a traditional analytical paper explaining their methodology and findings.

Conclusion

The goal is not to turn every student into a data scientist. It is to produce AI-ready graduates: doctors who can partner with AI diagnostic tools, lawyers who can argue cases involving algorithmic decisions, artists who can use AI as a new paintbrush, and executives who can lead their companies through this technological transformation with wisdom and ethical foresight. The university that successfully navigates this transition will shift from being a four-year destination to a lifelong learning partner.

Of course. This is a critical question facing higher education. Redesigning degree programs for an AI-transformed workplace isn't about simply adding a "How to use ChatGPT" module; it requires a fundamental rethinking of curriculum, pedagogy, and the very definition of a "graduate."

Here is a comprehensive framework for how universities can redesign their degree programs.

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The Guiding Philosophy: From "Fountain of Knowledge" to "Gymnasium for the Mind"

The university's role must shift. It can no longer be the primary source of information; AI can access and summarize information instantly. Instead, it must become a place where students train their minds to work with AI, to question it, to direct it, and to apply its outputs creatively and ethically. The goal is to produce augmented, not obsolete, professionals.

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I. Core Curriculum and Content Redesign

The "what" of learning needs to change.

1. Mandatory AI Literacy "Spine" for All Degrees

Just as writing and quantitative reasoning are core requirements, AI literacy should be a university-wide mandate. This isn't just for STEM students. This "spine" would include:

• Foundational Concepts: How do Large Language Models (LLMs), machine learning, and neural networks work at a conceptual level? What are their strengths and limitations (e.g., bias, hallucinations)?
• Practical Application: Hands-on workshops on using AI tools as a "co-pilot" for research, writing, coding, data analysis, and creative brainstorming. This includes mastering prompt engineering.
• AI Ethics and Governance: A mandatory course for all first-year students on the ethical implications of AI, including bias, job displacement, privacy, and intellectual property.

2. Deep Integration, Not Isolation

AI should be woven into the fabric of every discipline, not siloed in the computer science department.

• History: Students use AI to analyze vast digital archives, identify patterns in historical texts, and generate initial hypotheses for research papers.
• Medicine/Health Sciences: Students use AI diagnostic tools (in a simulated environment) and learn to critically evaluate the AI's suggestions against their own clinical knowledge.
• Law: Students use AI for legal research and contract analysis, focusing their time on strategy, negotiation, and ethical argumentation.
• Arts & Design: Students use generative AI to create mood boards, initial drafts, and conceptual variations, focusing their human effort on curation, refinement, and storytelling.

3. Prioritize "Human-Only" Skills

The curriculum must be ruthlessly curated to focus on skills that AI cannot easily replicate. These should be explicitly taught and assessed modules, not just soft-skill buzzwords.

• Complex Problem-Solving: Move from well-defined problems to messy, real-world case studies that require critical thinking across domains.
• Creativity & Innovation: Courses focused on divergent thinking, connecting disparate ideas, and developing novel solutions.
• Emotional Intelligence & Collaboration: Heavily weight team-based projects that require negotiation, empathy, and effective communication.
• Systems Thinking: The ability to understand how complex, interconnected parts of a system (e.g., a business, an ecosystem, a society) influence one another.

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II. Pedagogy and Teaching Method Overhaul

The "how" of learning is just as important as the what.

1. The Professor as "Chief Critic" and "Coach"

The professor’s role shifts from "sage on the stage" to "guide on the side." They should assume students will use AI for their first draft or initial research. The professor's job is to:

• Push for Depth: Challenge the superficial outputs of AI. "The AI gave you this summary. What are the three most critical flaws in its reasoning?"
• Coach the Process: Guide students on how to use AI effectively, how to refine their prompts, and how to blend AI output with their own unique insights.
• Facilitate High-Level Discussion: Use class time for debates, Socratic seminars, and complex problem-solving sessions that go beyond what AI can do.

2. Implement the "Flipped Classroom 2.0"

• Old Model: Watch a lecture at home, do a problem set in class.
• New Model: Use AI to learn the foundational concepts and generate a first draft of an assignment at home. Class time is used to critique, refine, and collaborate on that work under expert guidance.

3. Problem-Based and Project-Based Learning as the Default

Structure entire modules around solving a complex problem or completing a significant project. This forces students to synthesize knowledge, manage resources (including AI), and produce a tangible outcome, mirroring the modern workplace.

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III. Assessment and Evaluation Redesign

How we measure success must evolve, or cheating will be rampant and learning will be shallow.

1. De-emphasize the Traditional, Isolated Essay

The take-home essay where a student is alone with a word processor is dead.

2. Introduce New, AI-Resilient Assessment Methods:

• Process-Oriented Assessments: Grade the student’s process, not just the final product. Students submit a portfolio including their initial prompts, the AI's output, and a detailed commentary on how they critiqued, verified, and improved the AI's work.
• Oral Defenses and Vivas: Require students to verbally defend their work, answer challenging questions on the spot, and explain their reasoning. This tests true understanding.
• Live, In-Class Demonstrations: Have students use AI tools in real-time to solve a problem, explaining their strategy as they go.
• Authentic, Real-World Projects: Partner with companies to have students work on real business challenges. The messiness and unique constraints of these projects make them difficult for AI to solve alone.
• "Human-Add" Annotation: Give students an AI-generated text and assess them on the quality of their annotations, corrections, and critical additions.

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IV. Structural and Institutional Changes

1. Embrace Modularity and Lifelong Learning

• Micro-credentials: Break down degrees into smaller, stackable micro-credentials and skills certificates (e.g., "AI for Marketing Analytics," "Ethical AI Governance"). This allows for faster upskilling.
• University as a Lifelong Hub: Offer alumni ongoing access to the latest AI workshops and courses. Your relationship with the university shouldn't end after four years; it should be a career-long partnership for reskilling.

2. Mandatory, High-Quality Internships and Co-ops

Real-world application is no longer a "nice-to-have." Universities must build deep partnerships with industry to ensure every student gets experience applying their augmented skills in a professional setting.

3. Train the Faculty

Universities must invest heavily in training their own professors. Most faculty are not experts in AI. They need dedicated time and resources to learn the tools and, more importantly, to redesign their courses and assessments effectively.

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Conclusion

The university that thrives in the AI era will be one that is agile, interdisciplinary, and brave enough to abandon outdated traditions. It will stop asking students "What do you know?" and start asking, "What can you do with what the AI knows?" The goal is to graduate students who are not just ready for their first job but are equipped with the adaptive, critical, and creative capacities to thrive through multiple career transformations in an AI-powered world.

Below is a practical blueprint universities can use to redesign degrees for an AI‑transformed workplace. It balances AI fluency for all students with deep domain expertise, ethical grounding, and strong human skills.

Target graduate outcomes

• AI fluency for all: data literacy, basic statistics, computational thinking, using AI tools responsibly, evaluating outputs, automating routine tasks.
• Domain mastery augmented by AI: knowing the state of the art in one’s field and how AI changes workflows, decision‑making, and value chains.
• Human skills that compound with AI: critical thinking, systems thinking, problem framing, creativity, communication, teamwork, leadership, negotiation, and ethical judgment.
• Tech–policy awareness: privacy, security, bias, intellectual property, safety, sustainability, and applicable regulations.
• Adaptability and meta‑learning: learning how to learn with AI, change agility, portfolio building, and reflective practice.

Curriculum architecture

• AI for all core (required for every degree, 6–12 credits)
  • Foundations: data literacy, statistics, probability, computational thinking.
  • Working with AI systems: capabilities/limits, prompting as problem framing, verification, automation basics, human‑in‑the‑loop design.
  • Ethics, policy, and risk: bias, safety, privacy, copyright, environmental impact, governance.
  • Productivity with AI: research, writing, analysis, media generation, coding copilots; documentation and citation of AI use.
• Domain‑integrated AI modules (embed across existing courses)
  • Each core course adds a domain‑specific AI activity: e.g., marketing uses AI for segmentation and testing; nursing uses decision support tools; civil engineering uses AI in simulation/optimization; design uses generative media with copyright literacy.
  • Capstone studios with live industry/public sector data and AI toolchains.
• Advanced pathways (stackable)
  • Minors/certificates in Applied AI/Data Science accessible to non‑CS majors.
  • Technical tracks for those needing depth: ML, MLOps, NLP, computer vision, trustworthy AI, human–AI interaction.
  • Interdisciplinary studios on AI and society, policy, and entrepreneurship.
• Lifelong learning scaffolding
  • Modular, stackable micro‑credentials aligned to frameworks (e.g., SFIA, ESCO, NICE).
  • Credit for prior learning and industry certifications.
  • Alumni access to refresher modules and AI labs.

Pedagogy and assessment

• Learning design
  • Project‑based, problem‑led courses with messy, real data.
  • Flipped and blended models using AI tutors/copilots for practice, with human facilitation for higher‑order work.
  • Interdisciplinary teamwork and cross‑cultural collaboration.
• Assessment redesign for an AI world
  • Authentic tasks and artifacts over recall; emphasize analysis, evaluation, creation.
  • Oral defenses, studios, code/writing walkthroughs to evidence reasoning.
  • Process portfolios with version control/notebooks and AI use logs.
  • In‑class practicals and capstones; fewer high‑stakes take‑home essays.
  • Clear AI use policy per assignment (Prohibited, Allowed with citation, Encouraged with disclosure) and rubrics that assess tool choice, risk assessment, and human judgment.

Work‑integrated learning and employer partnerships

• Co‑ops, apprenticeships, micro‑internships focused on AI‑augmented roles.
• Multi‑sided project marketplaces: industry/public sector bring problems; students build solutions.
• Advisory boards per discipline to refresh skills maps every 6–12 months.
• Portfolio‑first career services: GitHub/Notebooks, design reels, case write‑ups, and reflective briefs on AI use and ethics.

Faculty and organizational capability

• Upskill faculty
  • Short courses on AI pedagogy, toolchains, assessment, and ethics.
  • Communities of practice and teaching fellows who mentor peers.
  • Time and incentives: course release, micro‑grants, recognition in promotion.
• Governance
  • AI Curriculum Council with faculty, students, IT, legal, DEI, and industry.
  • Rapid review cycle for curriculum updates (e.g., every 12 months).
  • Standardized AI‑use guidelines, IP policy, and data governance.

Infrastructure and tools

• Safe, equitable access
  • Institutionally provisioned AI platforms (mix of open‑source and licensed), with privacy, accessibility, and compliance controls.
  • Sandboxed environments, GPU/CPU pools or cloud credits, model catalogs, evaluation tools, and dataset repositories with clear licenses.
  • Logging and audit trails for learning analytics with opt‑in and ethical oversight.
• Learning engineering support
  • Instructional technologists, data stewards, MLOps engineers to support courses and projects.

Ethics, safety, and policy integration

• Required course on responsible AI aligned to NIST AI RMF, ISO/IEC 23894, and regional laws (e.g., EU AI Act, GDPR).
• Practice‑oriented ethics: red‑teaming, bias audits, privacy impact assessments, safety cases, environmental impact accounting.
• Discipline‑specific regulatory modules (health, finance, education, law, creative IP).

Equity and access

• Don’t widen the digital divide: device loans, on‑campus compute, offline options, multilingual and accessibility features.
• Teach AI accessibility use cases and inclusive design.
• Monitor and reduce outcome gaps across student groups.

Program exemplars (adapt for each discipline)

• Business: AI‑augmented analytics, experimentation platforms, marketing with generative content, governance for models in decisioning.
• Health: clinical decision support literacy, data ethics, documentation with AI, human oversight; simulation labs with AI‑driven scenarios.
• Engineering: CAD/code copilots, optimization, simulation, safety cases, embedded ML, MLOps.
• Humanities and social sciences: text/data analysis, archival AI, media verification, argumentation with AI critique, policy labs.
• Education: AI‑enhanced pedagogy, assessment with integrity, learning analytics, accessibility.
• Law and policy: legal research with AI, evidentiary standards, AI regulation, IP, model liability.
• Creative arts: generative workflows, style transfer ethics, licensing, human originality.

Implementation roadmap

• Next 6 months
  • Publish institutional AI use policy and assignment‑level disclosure norms.
  • Pilot “AI for all” module in first‑year seminars; launch faculty bootcamps.
  • Stand up a safe AI sandbox and dataset library; form the AI Curriculum Council.
• 6–18 months
  • Embed domain‑specific AI activities in at least 30% of core courses per program.
  • Launch two interdisciplinary AI studios and an ethics/red‑team lab.
  • Create stackable certificates; align career services to AI‑augmented portfolios.
  • Establish 10–20 employer‑sponsored projects; secure cloud/compute agreements.
• 18–36 months
  • Make the AI core required across degrees; scale WIL/co‑ops.
  • Add advanced tracks and joint degrees; formalize continuous review cycles.
  • Publish outcomes dashboards; iterate based on evidence.

Quality and impact metrics

• Student measures: AI confidence, ethical reasoning, portfolio quality, placement in AI‑augmented roles, reduction in equity gaps.
• Teaching measures: adoption of AI‑integrated assessments, faculty participation in upskilling, course satisfaction.
• Employer measures: satisfaction, repeat partnerships, time‑to‑productivity of hires.
• System measures: compliance incidents, data/privacy breaches, sustainability metrics.

Sample learning activities you can drop into courses now

• AI‑assisted analysis with audit trail: students solve a problem, log prompts/outputs, critique and verify, and reflect on errors and bias.
• Oral defense of AI‑aided work: 10‑minute viva to explain choices, risks, and alternatives.
• Red‑team your domain: identify failure modes of an AI system in your field and propose mitigations.
• Automation mini‑project: build a small workflow using an AI copilot plus APIs/RPA; document governance and handoff.

If helpful, I can tailor a sample semester plan for a specific major, design an “AI for all” syllabus, or draft an institutional AI use policy and assignment rubric templates.

Below is a blueprint universities can adopt to redesign degrees for an AI-transformed workplace. It balances enduring human skills with practical AI fluency, built around responsible, domain-specific application.

1. Guiding principles

• T-shaped graduates: deep domain expertise plus broad AI/data fluency.
• Human-in-the-loop: teach collaboration with AI, not replacement by AI.
• Responsible-by-design: ethics, safety, legal and social impacts embedded, not siloed.
• Practice-first: authentic, industry-aligned projects with real data and constraints.
• Lifelong, stackable learning: modular credentials that can be updated as tools evolve.
• Access and equity: institution-provided tools, inclusive pedagogy, and accommodations.

2. Degree architecture (any major) A. Common AI and data core (required for all undergrads; ~12–15 credits or equivalent)

• Computational thinking for all: algorithms, probability, stats, data literacy, visualization.
• Foundations of AI and GenAI: what models can/can’t do; prompt strategies; verification.
• Responsible and legal AI: bias, fairness, privacy, IP, academic integrity, regulation (e.g., GDPR/FERPA/HIPAA), environmental impact.
• Human-AI collaboration studio: applied problem-solving with AI; decision-making under uncertainty; workflows and toolchains.
• Domain data practicum: working with messy data; documentation; versioning; reproducibility.

B. Domain-specific AI integration (within each major; scaffolded across years)

• 100-level: discipline-specific AI use cases and tool orientation (e.g., AI for literature reviews, design ideation, coding assistants).
• 200–300-level: labs and mini-projects using domain data and tools (e.g., RAG for legal research, diagnostics support in nursing simulation, AI BIM add-ins in architecture, AI-supported policy analysis).
• 400-level: capstones with industry or community sponsors; responsible deployment plans; model cards and data sheets.

C. Optional tracks/minors/certificates

• Applied GenAI with Python/JS and APIs
• AI Product Management and Design
• Data Engineering and MLOps fundamentals
• AI Governance, Audit, and Compliance
• Domain tracks (e.g., AI in Healthcare, FinAI, EdTech AI, Creative AI)

3. Learning outcomes (updated across programs) Students should be able to:

• Frame problems for AI, choose appropriate tools, and articulate assumptions/limits.
• Prompt, iterate, and chain tools to reach verifiable results; document process.
• Evaluate outputs with statistical and domain checks; conduct error/bias analysis.
• Build simple AI-enabled prototypes (no-code/low-code or code, as appropriate).
• Apply legal/ethical frameworks; produce risk assessments and mitigation plans.
• Communicate decisions made with AI and defend them to technical and non-technical audiences.
• Work effectively in diverse teams and adapt to rapid tool change.

4. Pedagogy and assessment in an AI-present world

• Teach with AI: instructors model expert AI use live; provide “AI use statements” per assignment (what is allowed, required, or prohibited).
• Shift from product-only grading to process evidence:
  • Prompt/interaction logs and rationale
  • Data cards/model cards attached to projects
  • Version control histories
  • Reflective memos on verification and ethical choices
• Assessment mix:
  • Open-AI assessments focusing on application and critique
  • No-AI assessments for core mastery (e.g., theory, mental models)
  • Oral vivas, whiteboard/code walkthroughs
  • Authentic tasks with unique datasets/problem variants
• Rubric dimensions:
  • Problem framing and tool selection
  • Prompt quality and iteration
  • Verification and testing strategy
  • Ethical/legal compliance and documentation
  • Clarity of communication and teamwork

5. Program exemplars (sketches)

• Business: Core AI + “AI in Accounting/Finance/Marketing Ops” labs; AI product management elective; governance and model risk module; capstone with a local firm using analytics or GenAI for process improvement.
• Humanities: AI for research synthesis, corpus analysis, and creative production; modules on authorship, IP, and cultural bias; public-facing digital scholarship capstone.
• Engineering/CS: Traditional theory plus modern LLM/RAG systems, prompt-to-pipeline practices, edge AI, safety, and MLOps; red-teaming and evaluation; industry-scale capstone.
• Health: Simulation with AI decision support; documentation and policy compliance; bias and safety cases; interprofessional teamwork; clinical data governance.
• Education: AI-enhanced lesson design; formative assessment with AI; privacy and child data; classroom AI policies; practicum with AI tutoring.

6. Infrastructure and tools

• Institution-managed AI sandboxes: privacy-preserving LLM access (no data retention outside policy), with logging for learning analytics and integrity.
• Data governance: approvals, consent, and de-identification workflows; secure research/teaching data enclaves.
• Integrated tooling: AI copilots in LMS/IDE/office suite; GPU/compute plans via cloud credits; support for open-source stacks when feasible.
• Accessibility: campus-provided licenses; loaner devices; low-bandwidth alternatives; AI for accommodations that respects privacy.

7. Policy and integrity

• Clear AI use policy with three zones: prohibited, permitted with citation, and required. Require an “AI assistance statement” on submissions.
• Avoid reliance on AI detectors; emphasize process evidence and oral checks.
• IP and authorship guidance for student work using AI; teach licensing of training data and outputs.

8. Faculty development and incentives

• Paid institutes/bootcamps; release time; microgrants to redesign courses.
• Communities of practice by discipline; shared assignment banks and rubrics.
• Instructional design support to rework assessments and integrate tools.
• Recognition in promotion/tenure for innovative pedagogy and open-education resources.

9. Industry and community partnerships

• Advisory boards per program to refresh curricula annually.
• Co-ops/internships and sponsored capstones with AI deliverables.
• Guest projects with real constraints (compliance, latency, cost).
• Employer-aligned microcredentials that map to job families (AI analyst, data steward, AI PM, model auditor, safety specialist).

10. Equity, ethics, and wellbeing

• Embed algorithmic fairness, accessibility, and cultural perspectives throughout.
• Provide mental health and change-management support as roles shift.
• Track and mitigate digital divide; ensure all students have safe, supported access.

11. Lifelong learning and stackability

• Stackable badges that roll into degrees; recognition of prior learning.
• Alumni upskilling subscriptions and rapid-update microcourses each semester.
• Credit-bearing workplace projects for mid-career learners.

12. Implementation roadmap

• 0–6 months: Set governance; adopt AI policy; pilot faculty training; secure tool stack; design common AI core; launch a few AI-present courses.
• 6–18 months: Roll out AI core to first-year; convert high-enrollment courses; launch domain labs and first microcredentials; establish assessment norms.
• 18–36 months: Scale capstones/industry partnerships; require AI use statements across the curriculum; evaluate outcomes; refine.
• KPIs: Student AI literacy assessment, employer satisfaction/time-to-productivity, percentage of courses with AI-present pedagogy, internship placement, diversity and access metrics, integrity incidents trending down, graduate portfolio quality.

Quick wins you can start now

• Publish AI use guidelines and assignment templates with allowed/required AI.
• Provide campus LLM access with privacy controls and tutorials.
• Run a Human-AI Collaboration mini-studio in every major this year.
• Fund 10–20 course redesigns with small grants and showcase results.

The goal is not to bolt on an “AI course” but to evolve every program so graduates can responsibly use, critique, and build with AI in their domain—and continue learning as the technology changes.

Short answer: Don’t just add “an AI course.” Build degrees around human–AI collaboration, data fluency, ethical stewardship, and continuous upskilling. Make AI a thread that runs through every discipline, with authentic, industry-linked practice and assessments that value process, judgment, and impact.

A blueprint universities can use

1. Program-wide learning outcomes (for every major)

• Data and AI literacy: understand data pipelines, basic statistics, model types, strengths/limits, reliability, and failure modes.
• Human–AI teaming: task decomposition, prompt design, tool orchestration, verification, and documentation.
• Responsible and secure AI: bias, fairness, privacy, IP, safety, accessibility, energy/sustainability, and governance aligned to frameworks like NIST AI RMF/OECD.
• Domain-specific AI application: use and critique AI in the discipline’s workflows and standards.
• Communication and leadership: explain AI-supported decisions to technical and non-technical audiences; collaborate across functions.
• Learning agility: meta-learning, experimentation, and change resilience.

2. Curriculum architecture (tiered and modular)

• AI for all (university core, 6–8 credits)
  • Data literacy and computational thinking for non-specialists.
  • GenAI fundamentals: prompting, retrieval-augmented generation, evaluation/guardrails.
  • Responsible AI: ethics, law, policy, safety by design, accessibility.
  • Tool fluency: writing, analysis, coding, design co-pilots; cloud basics.
• In-major infusion (each degree)
  • 2–4 scaffolded, required courses where students use AI on discipline tasks (e.g., clinical decision support, legal research, design ideation, supply chain optimization).
  • A discipline-specific AI lab/clinic course solving partner problems with real data.
• Optional depth tracks (stackable minors/microcredentials)
  • AI creators: ML, MLOps, data engineering, evaluation, LLM ops, prompt engineering beyond basics.
  • Human-centered AI: HCI, UX research, participatory design, accessibility.
  • AI governance and policy: compliance, audit, risk, standards, AI safety.
  • Product and venture: AI product management, experimentation, metrics, go-to-market.
• Capstone
  • Cross-disciplinary team builds, deploys, and monitors an AI-enabled solution; includes a model/system card, fairness assessment, data documentation, security/privacy plan, energy estimate, and impact reflection.

3. Pedagogy that matches AI-era work

• Studio and challenge-based learning with messy, open-ended briefs.
• “AI-visible” assignments: students must disclose and justify AI use, paste key prompts, show iterations, and validate outputs.
• Oral defenses, code/writing walkthroughs, and peer critique to verify understanding.
• Portfolios over exams: repos, notebooks, dashboards, design artifacts, model/system cards, and postmortems.
• Frequent, small, authentic tasks with feedback from both faculty and AI tutors; teach when not to trust the AI.

4. Assessment and academic integrity (pragmatic and fair)

• Rubrics reward problem framing, method selection, validation, ethics, and communication— not just final answers.
• Require an AI-use report: tools, prompts, versions, timestamps; what was accepted/edited; verification steps.
• Use process data (version control logs, notebooks) and oral checks to ensure learning.
• Clear citation norms for AI assistance; detection is unreliable—design assessments accordingly.
• Define AI-allowed, AI-limited, and AI-free activities by learning objective.

5. Work-integrated and community-engaged learning

• Industry projects, co-ops, apprenticeships, micro-internships, and service-learning with local SMEs and nonprofits.
• An “AI Clinic” or Foundry: supervised student teams deliver AI audits, prototypes, and automations for partners.
• Living labs using campus operations data (sustainability, facilities, student success) with strong privacy governance.

6. Faculty enablement and incentives

• Center for Teaching and Learning with AI: workshops, templates, prompt libraries, case banks, and instructional co-pilots.
• Course release or stipends to redesign curricula; joint appointments and industry sabbaticals.
• Shared infrastructure: cloud credits, data sandboxes, secure environments, and assistive technologies.
• Communities of practice and student fellows who support faculty in-course.

7. Infrastructure, access, and governance

• Provide institutionally-procured AI tools with privacy, security, and accessibility guarantees; include open-source options.
• Data governance and consent frameworks; IRB-style review for student/partner data projects.
• Accessibility-first design; support low-resource settings and languages; ensure equitable device and compute access.

8. Lifelong and stackable learning

• Stack microcredentials into degrees; recognize prior learning and industry certs.
• Alumni subscriptions for updates, AI bootcamps, and re-skilling pathways.
• Competency transcripts alongside grades to signal skills to employers.

9. Discipline-specific examples (illustrative)

• Business: genAI for market research, agentic workflows in operations, causal inference, decision risk, and AI product management.
• Health: clinical AI safety, bias, documentation, decision support usability, data standards (FHIR), and simulation.
• Law: AI-assisted research/drafting with verification, e-discovery, admissibility, model transparency, and professional ethics.
• Engineering: digital twins, robotics/autonomy, edge AI, verification/validation, safety cases.
• Arts and humanities: creative AI, copyright and licensing, archives and digital humanities, cultural context and critique.
• Public policy: algorithmic accountability, impact assessment, public participation, procurement.

10. What to stop and start

• Stop: bolting on a single “AI 101,” policing with detectors, over-emphasizing tool tricks, and ignoring non-technical majors.
• Start: program-level outcomes, process-centered assessment, cross-disciplinary studios, responsible AI practice, and employer-validated projects.

A 12-month implementation roadmap

• Months 0–3: Set outcomes and policy. Define AI use policy and citation norms. Map current courses to new outcomes. Choose tool stack and governance. Stand up a faculty support center.
• Months 3–6: Pilot. Launch “AI for all” module in first-year seminars. Run 3–5 infused courses across different colleges. Open the AI Clinic with a small partner set.
• Months 6–9: Scale. Train faculty cohorts. Add AI-visible assessment templates. Secure cloud credits and data sandboxes. Begin capstone redesign.
• Months 9–12: Evaluate and iterate. Collect student/employer feedback, portfolio quality, placement data. Adjust rubrics, tools, and supports. Publish exemplars and share internally.

How you’ll know it’s working

• Students can explain when to trust or reject AI outputs and show the verification they performed.
• Portfolios show real-world impact and ethical reasoning, not just polished artifacts.
• Employers report faster onboarding and better problem framing/communication.
• Increased cross-disciplinary projects, internships, and venture activity.
• Equity gaps narrow as access and supports improve.

This approach produces graduates who are not only tool-fluent, but also judgment-strong, ethically grounded, and ready to build, integrate, govern, and lead AI-enabled work.

Below is a concise blueprint universities can use to redesign degree programs for an AI-transformed workplace. It balances universal AI literacy, domain-specific application, and deep technical expertise, while updating pedagogy, infrastructure, and governance.

Guiding principles

• Make every graduate AI-capable: focus on human–AI teaming, not just coding.
• Embed responsible use: ethics, safety, privacy, and legal frameworks are first-class learning outcomes.
• Be interdisciplinary and applied: real data, real partners, real deployments.
• Update fast: modular, stackable curricula that can be revised annually.
• Ensure equity: access to tools, compute, and support for all students.

Core competencies for all students (AI literacy, 6–9 credits)

• Computational and data fluency: basic Python or no-code/low-code automation; data wrangling; visualization; descriptive statistics; uncertainty.
• Foundations of AI: what ML and LLMs are and are not; strengths, limits, hallucinations, calibration; human oversight.
• Responsible AI and policy: bias and fairness, privacy/security, IP/academic integrity, accessibility, environmental impacts; NIST AI RMF, EU AI Act basics, model cards/datasheets.
• Human–AI collaboration: prompt design, retrieval grounding, tool use, verification workflows, version control; documenting and citing AI assistance.

AI in the discipline (stackable 12–18 credits)

• Domain-specific AI methods and cases: e.g., marketing attribution and content generation; clinical decision support; digital humanities and corpus analysis; engineering optimization and simulation; public-sector procurement and audits; education technology and AI tutors.
• Experimentation and decisions: A/B testing, causal inference, survey design, cost–benefit and risk analysis with AI in the loop.
• Responsible AI lab in context: bias testing on domain datasets, red-teaming, privacy-by-design, policy drafting for the field.

Advanced technical tracks (for AI creators, 30–45 credits)

• Math and CS foundations: linear algebra, probability, optimization, algorithms, systems.
• Core ML: supervised/unsupervised learning, deep learning, NLP, vision, reinforcement learning.
• Production AI: data engineering, MLOps, cloud/distributed systems, monitoring/observability, evaluation, prompt/RAG/agentic systems, GPU acceleration, inference optimization.
• Safety and security: robustness, adversarial testing, model governance, secure-by-design, privacy techniques (DP, federated learning).
• Capstone: deploy a real system with monitoring, documentation, and ethics review.

Cross-cutting micro-credentials (1–3 credits each; stackable)

• Prompting and workflow automation for knowledge work.
• RAG and vector databases; orchestration frameworks; agents.
• Copilots for coding, writing, design, data analysis.
• AI product management and AI UX.
• AI law and policy for non-lawyers.
• Green AI and cost/energy-aware compute.

Pedagogy and assessment redesigned for AI

• Require AI in learning: structured use of AI tutors, coding copilots, and writing assistants; teach verification and reflection.
• Assess the process, not just the product: version control histories, design documents, model/dataset cards, lab notebooks, and oral defenses.
• Mix assessment modes: in-class practicals, vivas, team projects, client deliverables, and low-stakes AI-assisted drafts; use closed-resource checks sparingly.
• Academic integrity and citation: clear allowed-uses matrix; require disclosure of tools, prompts, settings, and verification steps; detection alone is not policy.
• Inclusive and accessible learning: AI-driven accessibility tools (captioning, reading support) and alternatives for students with limited compute access.

Experiential learning and partnerships

• Industry and public-sector studios: multi-disciplinary teams solving sponsor problems with real data; NDAs and governance plan included.
• Co-ops/internships focused on AI-augmented roles; externships for non-tech fields.
• Red-teaming and audit clinics: law, policy, CS, and ethics students evaluate systems together.
• Entrepreneurship and open-source: incubators, hackathons, and contribution-for-credit to AI tools and datasets.

Infrastructure and platforms

• A campus AI platform: secure, identity-integrated access to LLMs and domain tools; logs for learning analytics and compliance; sandboxed environments.
• Compute and data: GPU clusters or cloud credits; managed notebooks; experiment tracking; curated dataset library with governance and consent.
• Tooling parity: ensure all students have access (licenses, loaners, remote desktops); measure and close access gaps.

Faculty and organizational capacity

• Upskilling: summer institutes; release time; micro-credentials for teaching with AI; teaching communities of practice.
• Hiring and incentives: joint appointments (AI+X), team-teaching credit, recognition in promotion/tenure for curriculum innovation and industry collaboration.
• Curriculum governance: cross-college AI curriculum council with employer advisory board; annual review cycle.

Policy and governance

• University-wide AI use policy for learning, research, admin; aligned to NIST AI RMF and mapped to EU AI Act risk categories.
• Data and model governance: classification, retention, consent, IRB processes for AI data use; security baselines; model/documentation standards.
• Environmental stewardship: track energy/costs; guidelines for efficient training/inference.

Lifelong learning and credentials

• Stackable certificates embedded in degrees; digital badges for distinct competencies (e.g., RAG systems, AI auditing).
• Alumni upskilling pathways, weekend bootcamps, employer-sponsored cohorts.
• Credit for prior learning and recognized industry certs (cloud ML, data engineering), with academic topping-off in ethics and theory.

Examples by discipline (illustrative)

• Business: spreadsheet automation; analytics and causal lift; GenAI content factory with brand safety; AI product management; governance and procurement.
• Health: clinical AI safety, bias and calibration; imaging and NLP; HIPAA-compliant ML; human factors and workflow integration; post-market surveillance.
• Humanities/Arts: corpus methods, translation and authorship, creative AI studios, copyright and licensing, cultural analytics.
• Engineering: AI-driven design and simulation, digital twins, controls with learning components, reliability and verification, embedded inference.
• Law/Policy: algorithmic accountability, audits and impact assessments, public procurement standards, sandbox regulation, civic tech.
• Education: AI tutors and analytics, assessment design with AI, equitable deployment, classroom policy writing.

12–24 month implementation roadmap

• Months 0–3: Task force; inventory courses; define core outcomes; select secure AI platform; draft campus AI policy; set access equity plan.
• Months 3–9: Pilot AI literacy course and Responsible AI lab; faculty training; update assessment policies and honor code; launch two industry studios.
• Months 9–18: Roll out AI-in-discipline modules across majors; stand up MLOps teaching environment; micro-credentials live; co-op partnerships scaled.
• Months 18–24: Launch advanced technical tracks; establish red-teaming/audit clinic; first comprehensive review with employer feedback and student outcomes.

Metrics and continuous improvement

• Coverage: % of graduates completing AI literacy; % of majors with AI-in-discipline modules.
• Learning: performance on standardized AI competency tasks; student calibration and verification skills.
• Equity: access/utilization of AI tools across demographics; support usage.
• Outcomes: placement in AI-augmented roles; employer satisfaction; number of deployed projects; startup and open-source contributions.
• Responsibility: incidents/reports, audit findings, environmental cost per credit hour of AI courses.
• Faculty: participation in upskilling; team-taught courses; curriculum revisions adopted.

What to stop, start, and scale

• Stop: over-reliance on essays and take-home coding as sole assessment; AI bans without alternatives; siloed capstones.
• Start: required AI use with disclosure; oral defenses and process artifacts; interdisciplinary studios with real stakeholders.
• Scale: stackable credentials; secure AI access for all; faculty development tied to incentives.

This model produces T-shaped graduates: broad AI literacy for everyone, deep domain expertise, and, for some, deep technical AI creation skills. It is practical to phase in, measurable, and aligned with industry needs and responsible innovation.

Below, I'll outline a comprehensive approach for universities to redesign their degree programs in response to an AI-transformed workplace. The rise of AI is reshaping industries by automating routine tasks, enhancing decision-making, and creating new roles in areas like data analysis, machine learning, and ethical AI governance. However, AI won't replace human ingenuity; it will augment it. Universities must shift from traditional, siloed education to flexible, adaptive models that equip students with a blend of technical, human, and ethical skills. This redesign should be iterative, involving input from industry partners, alumni, and AI experts.

1. Integrate AI Literacy and Technical Skills into All Disciplines

• Make AI a Core Competency, Not a Specialization: Every degree program—whether in humanities, business, engineering, or arts—should include mandatory modules on AI fundamentals. This could cover basics like machine learning algorithms, data ethics, and tools like Python or no-code AI platforms (e.g., Google Cloud AI or Hugging Face). For example, a marketing student might learn AI-driven analytics for customer segmentation, while a biology major explores AI in genomics.
• Why? AI is permeating all sectors. According to reports from McKinsey and the World Economic Forum, by 2030, up to 45% of work activities could be automated, but new jobs will emerge in AI-human collaboration. Students need to understand AI not just as a tool but as a transformative force.
• Implementation Tip: Use modular, stackable credentials (e.g., micro-degrees or badges) that allow students to build AI expertise progressively, regardless of their major.

2. Emphasize Human-Centric and Soft Skills

• Focus on What AI Can't Replicate: Prioritize skills like critical thinking, creativity, emotional intelligence, collaboration, and problem-solving. Programs should include project-based learning where students tackle real-world problems that require human judgment alongside AI tools—e.g., designing ethical AI systems for healthcare diagnostics.
• Incorporate Adaptability and Resilience: Introduce courses on change management, lifelong learning, and mental agility. AI will accelerate job evolution, so students must learn to pivot. For instance, simulate "AI disruption scenarios" in capstone projects, where teams redesign workflows affected by automation.
• Why? AI excels at data processing but struggles with nuance, empathy, and innovation. The OECD estimates that demand for social and emotional skills will grow by 26% by 2030.

3. Adopt Interdisciplinary and Flexible Curricula

• Break Down Silos: Encourage cross-disciplinary programs, such as joint degrees in AI + Ethics, AI + Business, or AI + Environmental Science. Universities could create "AI Hubs" where students from different fields collaborate on projects, like using AI for sustainable urban planning.
• Flexible Pathways: Move away from rigid four-year structures toward competency-based education. Allow students to customize degrees with electives, internships, or accelerated tracks. Online/hybrid formats can integrate AI-driven personalized learning (e.g., adaptive platforms like Coursera or edX).
• Implementation Tip: Partner with tech companies (e.g., Google, IBM) for co-designed curricula, ensuring relevance. Use AI itself to assess and personalize student progress, such as through predictive analytics for course recommendations.

4. Embed Ethics, Responsibility, and Societal Impact

• Mandatory Ethics Training: Every program should include modules on AI biases, privacy, equity, and societal implications. Discuss real cases like algorithmic discrimination in hiring or AI in surveillance.
• Promote Responsible Innovation: Teach students to evaluate AI's environmental footprint (e.g., energy consumption of data centers) and design inclusive systems. This could involve debates, simulations, or certifications in ethical AI frameworks (e.g., from IEEE or EU AI guidelines).
• Why? As AI scales, ethical lapses can have massive consequences. Surveys from Deloitte show that 76% of executives worry about AI ethics, creating demand for graduates who can navigate these issues.

5. Incorporate Practical Experience and Lifelong Learning

• Hands-On AI Application: Shift from lectures to experiential learning via labs, hackathons, internships, and industry-sponsored projects. For example, students could work on AI models for predictive maintenance in manufacturing or content generation in media.
• Foster Lifelong Learning: Design programs with post-graduation support, like alumni access to AI upskilling courses or micro-credentials. Encourage a "learning mindset" through portfolios that showcase evolving skills rather than static transcripts.
• Implementation Tip: Leverage AI tools in education itself—e.g., virtual reality simulations for AI-driven job scenarios or chatbots for personalized tutoring. Measure success through outcomes like employability rates and alumni adaptability surveys.

Potential Challenges and Solutions

• Resource Constraints: Universities may lack AI expertise. Solution: Collaborate with MOOCs, open-source communities, or government grants (e.g., NSF funding in the US).
• Equity and Access: Ensure redesigns don't exacerbate divides. Offer scholarships for underrepresented groups in AI and make tools accessible (e.g., free cloud computing credits).
• Measuring Success: Track metrics beyond graduation rates, such as job placement in AI-related roles or student proficiency in AI benchmarks.

By redesigning programs this way, universities can produce graduates who are not just AI-literate but AI-resilient—capable of thriving in a world where humans and machines co-create value. This isn't about fearing AI displacement but embracing it as an opportunity for innovation. Institutions like Stanford (with its Human-Centered AI Institute) and MIT (with interdisciplinary AI labs) are already leading examples. If you'd like more details on a specific field or case studies, let me know!

Universities are already grappling with the rapid integration of AI into workplaces, where tools like machine learning, automation, and generative AI are reshaping jobs across industries—from automating routine tasks in finance and manufacturing to enhancing creativity in design and healthcare. Redesigning degree programs to prepare students for this AI-transformed landscape requires a shift from traditional, siloed education toward more adaptive, interdisciplinary, and skills-focused models. Below, I'll outline key recommendations based on insights from educational experts, industry reports (e.g., from the World Economic Forum and McKinsey), and emerging best practices. These suggestions aim to equip students with both technical proficiency and the human-centered skills that AI can't easily replicate.

1. Integrate AI Literacy as a Core Component Across All Majors

• Why? AI isn't just for computer science students; it's becoming ubiquitous. Every field, from marketing to medicine, will involve AI tools.
• How to Redesign:
  • Mandate foundational AI courses (e.g., "AI for Non-Technical Majors") that cover basics like machine learning principles, data ethics, prompt engineering, and tool usage (e.g., ChatGPT, TensorFlow, or no-code AI platforms).
  • Embed AI modules into existing curricula. For example, business programs could include AI-driven analytics; humanities degrees might explore AI's impact on art and culture.
  • Example: Stanford University's "AI Index" courses or MIT's interdisciplinary AI ethics tracks could serve as models.

2. Emphasize Interdisciplinary and Hybrid Programs

• Why? AI-transformed jobs often require blending domains, such as AI with environmental science for climate modeling or AI with psychology for user experience design.
• How to Redesign:
  • Create flexible degree structures, like modular majors or "stackable" credentials, where students can mix STEM with liberal arts (e.g., a "Data Ethics and AI Policy" major).
  • Foster cross-departmental collaborations, such as joint projects between engineering and sociology departments to study AI's societal impacts.
  • Encourage double majors or minors in AI-related fields, with incentives like reduced credit requirements for overlapping courses.
  • Example: Programs like Carnegie Mellon’s AI and Public Policy degree integrate technical skills with policy analysis.

3. Prioritize Hands-On, Experiential Learning

• Why? Theoretical knowledge alone won't suffice; students need to apply AI in real-world scenarios to build adaptability.
• How to Redesign:
  • Shift from lecture-heavy formats to project-based learning, where students use AI tools for capstone projects (e.g., building an AI chatbot for customer service in a business class).
  • Incorporate internships, co-ops, or "AI labs" with industry partners (e.g., collaborations with companies like Google or IBM for real datasets and tools).
  • Use simulations and gamified learning to practice AI scenarios, such as ethical decision-making in autonomous systems.
  • Example: Universities like Northeastern offer co-op programs where students work on AI projects in tech firms.

4. Focus on Human Skills That Complement AI

• Why? AI excels at data processing and pattern recognition but struggles with creativity, empathy, and complex problem-solving—skills that will define high-value roles.
• How to Redesign:
  • Build curricula around "future-proof" competencies: critical thinking, emotional intelligence, ethical reasoning, collaboration, and adaptability.
  • Include courses on AI ethics, bias detection, and responsible innovation to prepare students for debates on issues like job displacement or algorithmic fairness.
  • Integrate soft skills training through group projects, debates, and leadership workshops.
  • Example: The University of Oxford's "AI for Good" initiatives emphasize ethical AI alongside technical training.

5. Promote Lifelong Learning and Adaptability

• Why? AI evolves quickly; a degree should be a starting point, not the end.
• How to Redesign:
  • Design programs with built-in flexibility, such as micro-credentials or online modules for post-graduation upskilling (e.g., certifications in emerging AI tools like generative models).
  • Teach metacognition—how to learn new technologies independently—and include courses on trends forecasting.
  • Shift assessment from exams to portfolios that demonstrate ongoing learning, like GitHub repositories of AI projects.
  • Example: Singapore Management University's emphasis on lifelong learning through alumni access to AI courses.

6. Address Equity, Inclusion, and Infrastructure

• Why? AI can exacerbate inequalities if not handled thoughtfully, and universities must ensure all students can access these opportunities.
• How to Redesign:
  • Invest in infrastructure: Provide affordable access to AI tools, high-performance computing, and training for underrepresented groups.
  • Incorporate diversity in curricula, such as case studies on AI's impact on global south economies or gender biases in algorithms.
  • Partner with governments and NGOs for scholarships in AI fields to broaden access.
  • Example: Initiatives like the AI4All program target underrepresented minorities in AI education.

Potential Challenges and Implementation Tips

• Challenges: Resistance from faculty accustomed to traditional methods, high costs for AI infrastructure, and keeping curricula current amid rapid AI advancements.
• Tips for Universities: Start with pilot programs in one department, gather feedback from alumni and employers, and form advisory boards with industry leaders. Regularly update curricula (e.g., every 2-3 years) based on tools like the World Economic Forum's Future of Jobs Report.
• Outcomes: Graduates from redesigned programs will be more employable, with roles like AI ethicists, data strategists, or human-AI collaboration specialists in demand. Studies suggest that AI-savvy workers could see productivity gains of 40% or more.

This redesign isn't about replacing human education with AI but augmenting it to create resilient, innovative professionals. If you'd like specifics for a particular field (e.g., engineering or liberal arts), let me know!

Universities are already grappling with the rapid evolution of AI, which is transforming workplaces by automating routine tasks, enhancing decision-making, and creating new roles in fields like data science, AI ethics, and human-AI collaboration. Redesigning degree programs to prepare students for this shift requires a strategic, forward-looking approach that balances technical skills with human-centric competencies. Below, I'll outline key recommendations based on insights from education experts, industry reports (e.g., from the World Economic Forum and McKinsey), and emerging trends in higher education. These aren't one-size-fits-all but can serve as a framework for adaptation.

1. Embed AI Literacy Across All Disciplines

• Why? AI isn't just for computer scientists; it's infiltrating every sector, from healthcare to marketing. Students need foundational knowledge to understand, use, and critique AI tools without becoming experts.
• How to Implement:
  • Introduce mandatory core modules on AI basics (e.g., machine learning concepts, data ethics, and generative AI tools like ChatGPT) in the first year, regardless of major.
  • Tailor AI integration to fields: For business students, focus on AI-driven analytics; for humanities, explore AI in creative writing or cultural analysis; for engineering, emphasize AI in automation.
  • Example: Stanford University's "AI for Everyone" course could be a model, scaled to undergraduate levels.

2. Shift Toward Interdisciplinary and Flexible Curricula

• Why? AI-transformed jobs often require blending domains (e.g., AI + biology for biotech). Rigid silos limit adaptability in a job market where 85% of roles by 2030 may not exist today (per World Economic Forum).
• How to Implement:
  • Design hybrid degrees, such as "AI + [Field]" (e.g., AI + Environmental Science for climate modeling).
  • Offer modular, stackable credentials (e.g., micro-degrees or certificates) that allow students to mix AI with electives, enabling customization and easier pivots mid-career.
  • Encourage cross-departmental projects, like teaming computer science students with art majors to build AI-generated art tools.

3. Emphasize Human Skills That AI Can't Easily Replicate

• Why? AI excels at data processing and pattern recognition but struggles with empathy, ethical reasoning, and innovation. Jobs will increasingly value "human-AI symbiosis."
• How to Implement:
  • Prioritize soft skills like critical thinking, problem-solving, collaboration, and adaptability through experiential learning (e.g., case studies on AI biases or group simulations of AI ethics dilemmas).
  • Include courses on AI ethics, societal impact, and responsible innovation to prepare students for real-world challenges like algorithmic discrimination.
  • Integrate emotional intelligence training, perhaps via psychology-infused modules, to foster leadership in AI-augmented teams.

4. Incorporate Hands-On, Practical Experience

• Why? Theoretical knowledge alone won't suffice; students need to apply AI in real scenarios to build portfolios and confidence.
• How to Implement:
  • Mandate capstone projects using AI tools (e.g., building a simple chatbot or analyzing datasets with Python libraries like TensorFlow).
  • Partner with industry for internships, co-ops, or "AI labs" where students work on live problems, such as optimizing supply chains with AI.
  • Use AI-powered platforms for personalized learning, like adaptive tutoring systems that track student progress and suggest tailored resources.

5. Promote Lifelong Learning and Adaptability

• Why? AI will accelerate job churn, with skills becoming obsolete faster. Universities must shift from "one-and-done" degrees to continuous education models.
• How to Implement:
  • Structure programs with built-in upskilling paths, such as post-graduation access to online modules or alumni networks for AI updates.
  • Teach meta-skills like learning how to learn (e.g., courses on rapid skill acquisition using AI tools).
  • Adopt a "flipped classroom" model where AI handles rote learning (e.g., via automated quizzes), freeing time for discussion and application.

Potential Challenges and Considerations

• Equity and Access: Ensure redesigns address the digital divide by providing affordable AI tools and training for underrepresented groups.
• Faculty Development: Universities need to upskill professors through workshops on AI integration.
• Assessment Evolution: Move beyond exams to project-based evaluations that measure AI collaboration, such as "How effectively did you use AI to solve this problem?"
• Timeline and Piloting: Start with pilot programs in high-demand areas (e.g., business or engineering) and iterate based on feedback and labor market data.

Institutions like MIT, Carnegie Mellon, and the University of Toronto are leading with AI-focused initiatives, such as dedicated AI institutes or revised curricula. Ultimately, the goal is to produce graduates who are not just AI users but ethical innovators and adaptable thinkers. If you're affiliated with a university or have a specific field in mind, I can refine these suggestions further!

Universities are facing a pivotal moment as artificial intelligence (AI) transforms workplaces across industries, automating routine tasks, augmenting decision-making, and creating new roles while rendering others obsolete. Redesigning degree programs to prepare students for this AI-driven future requires a holistic approach that blends technical proficiency, human-centric skills, ethical awareness, and adaptability. Below, I'll outline key strategies for redesign, drawing from emerging trends in education, insights from organizations like the World Economic Forum, and examples from institutions already adapting (e.g., Stanford's AI initiatives or MIT's interdisciplinary programs). These recommendations aim to produce graduates who can thrive alongside AI, not compete against it.

1. Integrate AI Literacy and Technical Skills into Core Curricula

• Make AI foundational, not optional: Every degree program, regardless of discipline, should include mandatory modules on AI fundamentals, such as machine learning basics, data analysis, ethics in AI, and tools like Python or no-code AI platforms (e.g., Google Cloud AI or Hugging Face). For non-STEM fields like humanities or business, this could focus on AI's societal impacts rather than deep coding.
• Hands-on AI application: Shift from theoretical lectures to project-based learning where students use AI tools to solve real-world problems. For example, business students could use AI for market forecasting, while engineering students design AI-enhanced systems. Programs like Carnegie Mellon's AI degree emphasize experiential learning through labs and simulations.
• Rationale: AI is becoming ubiquitous, like electricity was in the 20th century. Graduates need to understand how to leverage AI as a tool, not fear it as a replacement.

2. Emphasize Human Skills That Complement AI

• Focus on "AI-resistant" competencies: AI excels at data processing and pattern recognition but struggles with creativity, emotional intelligence, complex problem-solving, and ethical reasoning. Programs should prioritize these through courses in critical thinking, collaboration, leadership, and adaptability. For instance, incorporate design thinking workshops or capstone projects that require human-AI collaboration.
• Soft skills integration: Use AI to enhance teaching these skills—e.g., AI-driven simulations for negotiation or ethical dilemmas. The European Union's AI strategy highlights the need for "human-centric AI," which universities can mirror by blending soft skills with tech.
• Rationale: Reports like McKinsey's "The Future of Jobs" predict that demand for social and emotional skills will rise by 26% by 2030, as AI handles routine work.

3. Adopt Interdisciplinary and Flexible Program Structures

• Break down silos: Encourage cross-disciplinary majors, such as AI + Ethics (philosophy and computer science) or AI + Sustainability (environmental science and data analytics). This mirrors real workplaces where AI intersects with fields like healthcare, finance, and arts.
• Modular and lifelong learning models: Move away from rigid four-year degrees toward stackable credentials, micro-degrees, or hybrid online/in-person formats. Allow students to update skills post-graduation through alumni access to AI courses or certifications (e.g., similar to Coursera's partnerships with universities).
• Personalized learning paths: Use AI itself to tailor curricula—adaptive platforms could recommend courses based on a student's strengths, career goals, and emerging job trends.
• Rationale: The half-life of skills is shrinking; a 2023 PwC report notes that workers may need to reskill every few years. Flexible structures prepare students for continuous adaptation.

4. Incorporate Ethics, Responsibility, and Societal Impact

• Mandatory ethics training: Every program should include modules on AI bias, privacy, job displacement, and equitable AI deployment. Case studies from real events (e.g., algorithmic discrimination in hiring) can drive discussions.
• Diversity and inclusion focus: Ensure curricula address how AI can exacerbate inequalities and train students to design inclusive systems. Collaborate with diverse experts to avoid biased perspectives.
• Rationale: As AI amplifies human decisions, ethical lapses can have massive consequences. Institutions like Harvard's Embedded EthiCS program integrate ethics directly into tech courses, setting a strong example.

5. Foster Industry Partnerships and Real-World Exposure

• Collaborate with employers: Partner with companies (e.g., Google, IBM, or startups) for internships, co-op programs, and guest lectures. This ensures curricula align with workplace needs, such as using AI in supply chain optimization or creative industries.
• Entrepreneurship and innovation hubs: Create AI-focused incubators where students develop startups or prototypes, learning to innovate with AI ethically and sustainably.
• Global and experiential learning: Include study abroad or virtual exchanges focused on AI's global applications, preparing students for a borderless job market.
• Rationale: Bridging academia and industry reduces the skills gap; a LinkedIn report shows AI skills are among the fastest-growing in demand.

Challenges and Implementation Tips

• Overcoming resistance: Faculty may need retraining in AI—universities could invest in professional development or hire AI specialists.
• Equity considerations: Ensure access to AI tools and education for underrepresented groups to avoid widening divides.
• Measurement of success: Track graduate outcomes through metrics like employment rates in AI-related roles, adaptability surveys, and alumni feedback.
• Pilot and iterate: Start with pilot programs in high-impact fields (e.g., computer science or business) and scale based on results.

By redesigning programs this way, universities can shift from producing job-specific experts to versatile, AI-fluent thinkers who drive innovation. This isn't just about surviving an AI-transformed workplace—it's about leading it. Institutions like Singapore Management University or the University of Toronto are already pioneering such models, showing that proactive adaptation yields competitive graduates. If you'd like more details on specific disciplines or examples, let me know!