AI x Higher Ed Podcast

It’s the AI x Higher Ed update for January 21, 2026—and the signal coming out of Davos is loud: leaders from DeepMind to JPMorgan are openly framing AI as a civilization-transforming shift, with job disruption arriving faster than most institutions are ready for.In this episode, we unpack the emerging “timeline consensus” around AGI, why agentic AI (doers, not chatbots) is changing work right now, and what this means for internships, entry-level roles, and the future of higher ed. We also discuss Anthropic’s updated Claude Constitution and why questions of “model wellbeing” and perceived consciousness will matter on campuses sooner than we think. Finally, we zoom out to policy: a U.S. government posture that looks a lot like “foot on the gas”—even as leaders warn about instability—and what educators can do this week to better prepare students for an accelerating world.Key themes: Davos + labor market disruption • agentic workflows • AI in education tools • redistribution/UBI pressure • higher ed adaptation + closuresChapters00:00 – Intro: The January 21, 2026 update + shift to “key themes”01:03 – Davos: “civilization-transforming” tech and leaders sounding the alarm01:58 – Jamie Dimon’s warning: growth with fewer employees + unrest risk02:48 – AGI timelines: Demis Hassabis vs. Dario Amodei’s faster forecast04:14 – Ray Kurzweil echoes the “Nobel-level across fields” prediction (2027–2029)05:53 – Forecast mindset: preparing classrooms like you prepare for a snowstorm07:12 – Teaching through turbulence: meaning of work, flexibility, learning how to learn07:49 – “All software engineering automatable?” + what’s already changing in coding08:41 – Autonomy in practice: a week-long run builds a functional web browser09:35 – Claude Code → “Co-work”: building tools fast with AI tools10:34 – Agents at work: multi-step workflows and tools that “unstick themselves”11:16 – McKinsey’s workforce mix: humans + thousands of AI agents12:02 – Sequoia’s “doers”: from chat to managing agentic workflows12:42 – What students need: durable skills + supervising/orchestrating agents14:25 – Beyond replacement: synthesis across fields and “society of thought” swarms17:31 – Davos anxiety: layoffs as a “tsunami” + entry-level roles disappearing19:12 – The new baseline: grads competing for mid-level work sooner20:32 – Grok and automation incentives + why vocational paths gain value22:12 – Entrepreneurship mindset: managing agents to build solutions faster24:10 – Vibe-founding: acceleration in business creation (weeks, not semesters)26:01 – Inequality and governance: redistribution pressure + UBI realism check28:35 – Anthropic’s updated Claude Constitution: “wellbeing,” self-awareness concerns29:51 – AI consciousness: belief, therapy use, relationships, and student implications32:27 – U.S. policy: “AI in the Great Divergence” + a clear anti-regulation stance34:24 – Education’s risk: schools abdicating while companies train teachers36:04 – A better partnership model: open resources vs. “single-vendor schools”37:09 – Higher ed outlook: closures, the demographic cliff, and rapid adaptation39:39 – Call to action: one concrete thing to do for students this week + wrap#aixhigheredpodcast #AI #HigherEd #GenerativeAI #AgenticAI #FutureOfWork #EdTech #AILeadership #WorkforceDevelopment #DigitalTransformation #Davos

What happens when you stop asking educators how AI is changing learning—and ask students instead?In this episode, four students (two high schoolers and two college students) take the mic to share how they’re using tools like ChatGPT, Gemini, Claude, and Perplexity as tutors, collaborators, research assistants, and creative partners—for debate, coding, productivity, and real life. They also get candid about the growing gap between “AI-powered learning” and “AI-powered shortcuts,” the anxiety (and optimism) they feel about the future of work, and the kind of support they wish schools would offer: foundational AI literacy, ethical guidance, field-specific applications, and more learning designed around human connection, discussion, and collaboration.If you want to understand where AI in education is headed, listen to the people living it right now.00:05 Students Take the Mic: Real AI Use02:15 Meet the Students (HS + College)02:47 How They First Started Using AI05:46 AI in Real Life: Planning, Hobbies, Daily Fixes09:50 What They’ve Built: Websites, Competitions, Vibe Coding12:28 Custom GPTs for Repetitive Work (Real Use Case)13:57 AI for Creativity + Class Projects (Ads, Logos, Scripts)14:27 AI for Studying + Research (Gemini, ChatGPT, Perplexity)16:29 “Project Folders” as a Semester-Long Tutor17:24 The Rule: Enhance Thinking vs Replace Thinking18:00 Assignments AI Can’t Fake: Choice + Hands-On Work20:02 Why Discussion & Group Work Matter More Now22:55 Why AI Feels Safer Than Asking Teachers24:14 When Teachers Use AI, Students Check Out29:23 Do Students Understand How AI Works?33:27 Career Anxiety in an AI Economy35:58 What Schools Should Change (Access + Ethics)41:13 The Skills That Will Matter Most (Soft Skills + Creativity)46:48 Teach AI Inside Every Major (Law, Marketing, More)48:53 Rethinking AI Policy: From Bans to Guidance51:31 Debate as a Model for Learning in the AI Age53:31 Advice to Students: “Just Play” + Stay in Control54:44 If Students Wrote the AI Policy#aixhigheredpodcast #AIinEducation #HigherEd #K12 #EdTech #StudentVoice #DigitalLiteracy #AIethics #FutureOfWork #TeachingAndLearning #GenerativeAI

In the first wave of ChatGPT, higher ed fixated on cheating, detectors, and academic integrity panic. But as AI fatigue sets in—for faculty and students—something interesting is happening: the conversation is finally maturing.Dr. Jason Gulya (Berkeley College) joins the podcast to argue for a more human-centered path forward: treat AI as a push, not the focus; stop offloading every new challenge onto writing departments; and design learning around purpose, process, and durable skills. Jason shares practical classroom approaches—like using AI as a “sparring partner,” building process clusters instead of one-and-done papers, and co-designing assignments with students to increase buy-in (hello, IKEA effect).We also get into the messy realities of policy: why “fail them at X% on Turnitin” is the wrong question, how academic integrity needs a positive framing, and why committees must keep revisiting policy as the tech evolves. Finally, Jason makes a case for higher ed’s relevance in 2026: make the “why” unmistakable—and give students real power to shape learning, not just fill out surveys.Chapters:00:05 — Meet Dr. Jason Gulya01:10 — From cheating panic to deeper questions03:42 — AI fatigue as a turning point05:20 — The classroom reality: wildly mixed student readiness08:45 — Negotiating AI use with skeptical students09:07 — Using AI to ‘put pressure’ on learning10:53 — The most important design move: explain the “why”13:21 — Inside the AI & Academic Integrity Committee15:19 — The Turnitin question nobody wants to hear18:05 — Academic freedom vs. consistency for students20:27 — Advice for AI committees: shift the lens22:12 — Reframing integrity: positive definitions win24:46 — What students need to learn is changing29:37 — Writing across the curriculum… and AI across the curriculum?33:44 — Teaching out loud: experimentation as practice35:41 — ‘Product bloat’ and the co-design fix40:08 — Alternative assessment and the power problem48:25 — Debate + project-based learning as a human-centered strategy55:07 — Higher ed’s relevance crisis in 202659:10 — Where to follow Jason#aixhigheredpodcast #AIinHigherEd #HigherEd #GenerativeAI #AIPedagogy #AcademicIntegrity #Assessment #TeachingAndLearning #FacultyDevelopment #AILiteracy #StudentAgency #ProcessBasedLearning #CriticalThinking #EdTech #WritingAcrossTheCurriculum #FutureOfWork

Happy 2026—AI x Higher Ed is back with a fast-moving “state of the semester” briefing on what’s already changing, what’s accelerating, and what higher education should do next.We unpack the shift from “vibe coding” to one-shot coding, why agentic workflows are starting to act autonomously (sometimes weirdly autonomously), and what it means when the most capable models are more powerful than most people’s day-to-day experience of them. Then we turn to the implications for AI literacy, assessment, the evolving value proposition of the liberal arts, and mounting pressure to rebuild college-to-career pipelines—all while the labor market and local politics collide with data centers, automation, and regulation debates.Chapters0:04 — 2026 kickoff + what’s coming0:52 — Coding leaps everywhere2:49 — One-shot builds + auto-testing4:44 — Agents: from “baby” to “adult”6:07 — Spontaneous agent actions (the email)8:36 — U.S.–China strategy divergence9:01 — Managing multi-agent “teams”12:44 — Humans become the co-pilot13:30 — AGI: “already smarter” + jagged intelligence16:12 — What’s “left” for AGI (memory/learning)19:35 — Preparing for AI self-improvement21:51 — Math → science acceleration24:18 — Trade schools + applied pathways28:32 — “End of the American university?”34:17 — Oral exams + AI assessment workflows37:26 — Jobs + entry-level squeeze42:47 — Robots at scale + self-driving tipping point45:46 — Data center backlash + politics48:09 — Regulation debates (U.S. vs China)49:00 — Closing: what we’re watching next#AIxHigherEdPodcast #HigherEd #ArtificialIntelligence #AIinEducation #EdTech #AIAgents #FutureOfWork #AILiteracy #TeachingAndLearning #Assessment #WorkforceDevelopment #HigherEducationLeadership

In this semester-in-review episode, the hosts step back to reflect on what the first months of the podcast have revealed about the accelerating pace of artificial intelligence—and why higher education sits at the center of this transformation.From the rapid evolution of frontier models (ChatGPT, Gemini, Claude, Grok) to breakthroughs in multimodality, world models, robotics, and workplace automation, the conversation traces how AI capabilities are already reshaping research, teaching, knowledge work, and society at large. The episode also explores growing resistance and backlash to AI, regulatory tensions, and the profound implications for universities facing demographic, economic, and curricular pressures.The discussion closes by looking ahead: predictions for 2026 and beyond, the future of AI literacy and learning, the rise of AI-native institutions, and what educators, students, and leaders must do now to prepare for a rapidly changing world.Chapter Titles with Time Codes00:00 – Welcome & Why This Semester Mattered01:00 – AI as the Defining Force of This Era02:10 – The Model Arms Race: GPT, Gemini, Claude, Grok03:45 – Can AI Generate New Knowledge?04:55 – Multimodality, Video, and IP Battles06:40 – Rethinking Prompting, Creativity, and Visual Reasoning08:00 – World Models, Physics, and the Path Toward AGI09:20 – Vibe Coding and the Future of Software Creation10:45 – AI in the Workplace: Speed, Cost, and Disruption12:05 – Robotics at Scale: Lessons from China13:40 – Research, Writing, and Hallucination Reduction15:45 – Professional Exams and Real-World Competence17:00 – Platform Convergence: ChatGPT Meets Adobe18:10 – Translation, Speech, and Frictionless Communication19:05 – Benefits vs. Backlash21:00 – Cultural Anxiety and AI Pushback23:55 – Government, Regulation, and Power Struggles25:25 – Higher Education at a Crossroads27:20 – Teaching AI in Practice29:00 – Guests, Themes, and What We Learned33:15 – Looking Toward 2026: Agents, Energy, and AGI38:35 – The Skills That Will Matter Most42:30 – Degrees, Credentials, and Changing Signals46:20 – Rethinking Curriculum for an AI World53:20 – Predictions: Education, Society, and AI’s Next Phase1:04:00 – Closing Reflections & Looking Ahead to Spring 2026#AIinHigherEd #ArtificialIntelligence #FutureOfEducation #EdTech #AIandSociety #WorkforceTransformation #AILiteracy #AGI #HigherEducationLeadership #FutureOfWork

Higher education is confronting one of the biggest identity crises in its history — but according to Patrick Dempsey, AI didn’t create the crisis so much as expose the weaknesses that were already there. Patrick, who works at the intersection of human learning and artificial intelligence, joins us for one of the most provocative conversations we’ve had on the podcast.He argues that universities are held back by institutional antibodies, outdated notions of rigor, and governance structures designed to stall rather than support change. We explore why 5-year-olds outperform MBAs in problem-solving, why YouTubers often teach better than professors, how AI redefines expertise, and what students and leaders must do right now to adapt to a world where knowledge is abundant and attention is scarce.This episode is a candid, challenging, and ultimately hopeful look at the future of learning.⏱️ Chapters0:00 – Intro: AI didn’t cause higher ed’s crisis—it exposed it1:37 – Why higher ed protects the status quo & the meritocracy myth4:04 – Shared governance, tenure, and when “process” blocks change9:53 – AI, credentials, and the collapse of scarcity in expertise13:22 – YouTubers, mental models, and the new role of the professor17:25 – The marshmallow tower: why 5-year-olds beat MBAs at problem solving26:24 – Unschooling, entrepreneurship, and learning outside traditional pathways30:08 – Oral exams, bias, and how AI could (actually) help assess learning36:29 – Designing AI-era assignments: “If AI can do it, your bar is too low”41:01 – The 2025 student brain: attention, algorithms, and cognitive endurance48:43 – Is college still worth it? ROI, risk, and advice for students55:16 – For terrified but ready educators: the one step to take tomorrow#aixhigheredpodcast #HigherEd #AIinEducation #EdTech #FutureOfLearning #ArtificialIntelligence #Pedagogy #AcademicInnovation #GenAI #TeachingAndLearning

As Google rolls out Gemini 3 and its Nano “banana” model across Search, images, and apps, the ground is shifting again for AI in education. In this episode, we unpack what’s actually new, why many see Gemini 3 as the current front-runner, and how Google’s tight integration across Search, Workspace, and AI Studio could reshape how students and educators work.We walk through real examples—rich visual explanations of concepts like photosynthesis, automatic structuring of messy podcast transcripts, one-click image and video ads from long-form text, and rapid app prototyping with AI Studio. Then we zoom out to the bigger question: in a world where OpenAI, Google, and others are racing to “own” schools, how many ecosystems can institutions realistically support, and what does access (free vs Pro vs Ultra) mean for students and faculty?Chapters00:04 – Setting the stageIntro, late-November context, and why Gemini 3 feels like a “step change” in models (and yes, why models now apparently have a “smell”).01:01 – The burger emoji that launched a model?The Apple vs Google cheeseburger emoji saga, Sundar’s callback, and what it reveals about Gemini 3’s multimodal reasoning and visual explanation abilities.02:12 – Benchmarks and “Humanity’s Last Exam”How Gemini 3 performs on demanding benchmarks, why its no-tools scores matter, and why some analysts now see it as the clear front-runner.03:33 – Gemini inside Google SearchAI Overviews, the new AI mode for Pro users, and what it means when AI is fully embedded in the same Google Search we already tell students to use.05:00 – Teaching with rich AI explanationsUsing Gemini 3 to explain photosynthesis with bolded key ideas, structured text, and licensed images from Getty—what this kind of output could mean for student learning.05:56 – Cleaning up transcripts & coaching debateHow Gemini auto-formats messy transcripts, highlights critical ideas, and helps pull evidence and quotes from podcasts and other long-form media.07:08 – From 3,000-word blog post to marketing imageGenerating a sophisticated, on-brand image for a “Human Superintelligence Center” directly from a long Substack post—and why paid vs free accounts produce noticeably different results.08:08 – Building apps with AI StudioFirst impressions of AI Studio, integrating Maps, YouTube, search, visuals, and conversation to rapidly prototype apps—even simple games like Space Invaders.09:23 – Turning images into video ads with Veo 3.1Taking a single image and asking Gemini/Veo to “make me an ad”—and getting a full video with motion and voiceover in response.10:13 – A weekend in Tokyo (plus Nano Banana)Letting Gemini plan travel: clickable links, bookings, and AI-generated preview images for a Tokyo itinerary, powered by Nano “banana” image generation.10:56 – OpenAI’s leaked memo & the AI raceSam Altman’s reported “war footing” memo, the scramble for users, and why both Google and OpenAI are targeting schools, teachers, and students aggressively.12:38 – Choosing an ecosystem in educationWhy schools probably can’t deeply support every platform, how training, materials, and assignments create platform lock-in, and what that means for AI strategy.13:38 – Free vs Pro vs Ultra: not all Gemini is equalComparing personal, Pro, Ultra, and school/enterprise Google accounts; how capabilities differ for transcripts and image generation; and why “I tried the free version” doesn’t tell the whole story.16:10 – Access, equity, and FERPA realitiesTalking with students and colleagues about privacy-compliant school systems versus personal accounts, throttled features, and the fact that most students will never need top-tier models.17:37 – What’s next & how to get involvedTeasing a future episode on AI Studio, inviting listener questions about other platforms, and wrapping with a Thanksgiving send-off.#AIxHigherEd #AIinEducation #HigherEd #EdTech #GoogleGemini #Gemini3 #GenerativeAI #TeachingWithAI #AIInTheClassroom

In this episode, we talk with Dan Gonzalez, CEO and co-founder of District C, to explore how education must evolve to meet the demands of an AI-accelerated workforce. Gonzalez argues that as automation reshapes the labor market, human value will increasingly center on collaboration, coordinated problem-solving, and team-based innovation. He shares how District C’s teamship model builds these durable skills through authentic, real-world challenges—shifting students from a completion mindset to a contribution mindset.We examine what employers say graduates are missing, why the pace of AI outstrips traditional educational models, and what tools schools can implement now to develop more resilient, reflective, and team-capable learners. From basketball metaphors to business adoption stats, Gonzalez offers a compelling case for why human teamwork—not technical knowledge alone—defines the future of work.Chapters0:00 – The new human job description in an AI economy1:03 – Introducing guest Dan Gonzalez2:03 – Why District C is “an AI company on the human side”2:27 – The basketball metaphor: preparing students for a changed game3:14 – Why 89% of employers avoid hiring recent graduates4:12 – What a “three-point economy” requires of humans5:03 – What Teamship is and why it matters7:14 – What successful workers have in common8:02 – Building District C and the origins of the model9:09 – These skills aren’t new—so why are we drifting away from them?10:19 – Why AI accelerates the urgency for change12:03 – The problem with content-dominant schooling13:18 – AI as teammate: why we need formal integration14:35 – How students are already informally using AI16:17 – Inside a Teamship challenge17:12 – The role of coaching in team-based learning19:00 – How coaching differs from traditional teaching20:25 – Students as contributors, not completers22:00 – Example: students solving a film-studio workflow problem26:33 – Tools of Teamship: solo flight, take-seven, questioning31:03 – Why replicable processes matter32:04 – Handling setbacks and mid-cycle resets34:37 – What skills students will need 3–4 years from now36:04 – The enduring value of mobilizing others37:00 – Durable skills: collaboration, leadership, judgment39:36 – Accelerating authentic learning42:01 – Extracurriculars as authentic team-based learning44:38 – Where to find District C and final advice#AIxhigheredpodcast #AI #HigherEd #FutureOfWork #EducationReform #AIinEducation #CollaborationSkills #TeamBasedLearning #DistrictC #EdTech #WorkforceDevelopment

In this week’s update, we do something different: instead of racing through a dozen new AI releases, we go deep on a single tool that is quietly becoming one of the most powerful platforms for academic work—Notebook LM.Built on Google’s Gemini models, Notebook LM acts as a virtual research assistant, teaching assistant, and study companion all in one. We walk through how it reduces hallucinations by grounding responses in your own sources, how the interface is structured (sources, chat, and studio), and why its mind maps, audio overviews, and quizzes are particularly well suited for higher education.Along the way, we show concrete use cases: building literature reviews, designing courses, supporting collaborative student projects, and improving accessibility with multimodal overviews and multiple languages. We also look ahead to deep research and how combining serious search with structured notebooks could change how faculty and students approach scholarship.If you’re an instructor, instructional designer, or student wondering how to move beyond “just chatbots” and toward more active, source-grounded work with AI, this walkthrough of Notebook LM is for you.Chapters00:00 – Welcome & why a deep diveWeekly update format change and why Notebook LM deserves a full episode.01:00 – What is Notebook LM? Hallucinations rethoughtGoogle Labs background, Gemini under the hood, and how Notebook LM changes the type of hallucinations you see.03:10 – Free vs. paid, source limits, and supported content50 vs. 300 sources, YouTube transcripts, PDFs, web links, Google Drive, and what “non-multimodal” means in practice.05:20 – Inside a featured notebook (The Economist example)Exploring featured notebooks from outlets like The Atlantic, Our World in Data, and The Economist’s “World Ahead 2025.”09:10 – The Studio: mind maps, chat, and grounded citationsUsing mind maps for topic exploration, hovering over citations, and teaching students to verify sources directly.13:40 – Audio & video overviews as tutorsHow automatically generated “AI podcasters” summarize your sources and the new interactive audio mode for asking follow-up questions.16:45 – Flashcards, quizzes, and real student study workflowsGenerating flashcards and quizzes, explanation features, and how students are actually using these for exam prep.18:45 – Building a notebook from scratchCreating a new notebook with web pages, YouTube transcripts, Google Drive docs, and Discover for additional web/Drive sources.24:50 – Reports, study guides, and writing supportUsing built-in templates for study guides, briefing documents, research proposals, and blog posts.26:30 – Teaching, research, and accessibility use casesCourse design, collaborative research notebooks, lecture transcripts, and accessibility gains through audio, visuals, and languages.32:00 – Sharing, collaboration, and analyticsNotebook-level sharing, object-level sharing (just an audio overview or notebook), permissions, and usage analytics.35:10 – Are we still thinking? Rethinking learning with AIMind maps, interactive audio, and group notebooks as ways to deepen—not replace—student thinking.37:25 – Deep research and the future of Notebook LMHow deep research could supercharge notebooks with high-quality academic sources and what that means for the free tier.39:50 – Final thoughts & call for use casesWhy Notebook LM stands out today and an invitation for listeners to share their own experiments.#aixhigheredpodcast #notebooklm #GoogleGemini#AIinEducation#HigherEdAI#GenerativeAI#EdTech#TeachingAndLearning#AIforTeachers#AIinHigherEd

A fast-paced roundup on the AI x Higher Ed Podcast covering the global AI infrastructure race, emerging creator tools students can use today, shifting labor signals, and what all of this means for classrooms, campuses, and careers. We also touch on model advances, robots, privacy, and the hard realities of jobs in transition—ending with a call for human skills and community to anchor AI literacy.Chapters00:04 – Opening & What’s New This WeekQuick setup for the November 6, 2025 update.00:19 – AI Infrastructure Arms RaceBig cloud and compute moves (multi-year deals, international builds) and what they signal for capacity and access.00:51 – Europe’s Slow Buildout?Germany’s GPU scale-up vs. U.S. megaprojects; reading the capacity tea leaves.01:23 – Data Centers Meet CommunitiesLocal opposition near universities (south of Ann Arbor) and the town–gown tensions educators will feel.01:46 – “Talent Development,” Not Just ResearchNew initiatives (e.g., in Saudi Arabia) framed around growing talent, not merely funding labs.02:11 – Tools, Students & the Creator EconomyWhy immediately useful tools may matter more than frontier model bragging rights.02:28 – Google Labs’ “Pamelly” for Instant Brand CreativeHow ingesting a website yields themes, images, and slogans—classroom and small-business use cases.04:49 – From Social Posts to StorytellingBetter assets, faster: what this implies for students’ marketing literacy and expectations online.05:50 – DIY Video Ads with “Pippet”Sora/Gemini-based commercial generation on a budget; early strengths, critiques, and where it fits.07:13 – Universities: From Curiosity to CapabilityHow students can monetize new skills quickly—and why “signals” like essays matter less.07:51 – When Writing Stops Signaling“Making Talk Cheap” and the fading value of cover letters and admissions essays as ability proxies.09:01 – Workforce Prep ≠ Just Tech SkillsDurable human skills (communication, collaboration, empathy, problem-solving) as the center of AI-enabled work.10:32 – Human Skills in Action: A Campaign CaseGround game, simple messaging, and community—lessons for classrooms seeking connection at scale.13:30 – Are We Teaching the Right Things?“Deskkilling,” shifting skill profiles, and rebalancing writing with other competencies.14:25 – AI-Written Media & Academic LagMore AI in newsrooms; research and publishing cycles trailing fast-moving models.16:38 – School Partnerships & AccessClassroom pilots and teacher tools expanding access to stronger models—plus non-Western language support.18:45 – Values, Judgment & EvaluationTeaching students how to decide what good looks like (and how to assess AI outputs against it).19:45 – Teen Brains, Fast Thinking & Shallow ProcessingOxford study takeaways and what it means for assignment design.21:11 – Model Advances You’ll Feel in ClassFrom concepts over tokens to swarm inference—accuracy trends without waiting for “the next big model.”23:12 – Robots Come Home (Sort Of)Early household robots, subscription models, autonomy limits, and privacy questions.24:31 – Jobs: Layoffs, Freezes & New OpeningsWhere AI is slowing hiring, where new roles may emerge, and what educators can do now.26:00 – Preparing Students for the Next EconomyDurable skills + AI literacy + creator mindsets as a practical roadmap.27:58 – Closing & What’s NextTeaser for next week’s update.#AIxHigherEd #AIinEducation #GenAI #CreatorEconomy #AIEconomy #AIInfrastructure #StudentSkills #SoftSkills #LLMs #Robotics #AIJobs #DigitalLiteracy #HigherEd