AI for U

Brian Piper

AI for U is the go-to podcast for higher ed professionals looking to integrate AI into their daily work. Each episode features interviews with industry leaders, providing insights on implementing and leveraging AI to streamline processes, enhance student experiences, and drive institutional success. Join host Brian Piper every other Thursday for fresh, empowering content that keeps you at the forefront of AI in higher education.

  1. 1d ago

    Ep. 55: How Interactive AI Personas Combat Organizational Gravity

    Traditional marketing personas are often locked away in static PDF documents or PowerPoint decks, collecting digital dust while teams rely on gut instinct rather than real audience insights. Host Brian Piper welcomes Matt Wilkinson, Founder of Strivenn and author of The Buyer in the Loop, to explore how interactive AI personas in higher ed and marketing are transforming audience research. Matt breaks down why static persona documents fail, forcing marketers to perform uncomfortable mental gymnastics, and how grounding synthetic customers in deep research and voice-of-customer data allows teams to actually put their personas to work. Join us as we discuss:  [3:06] What sets interactive AI personas apart from traditional student personas[15:30] Practical examples of “synthetic for directional and human for decisional” [22:20] How AI personas can combat organizational gravity during the content creation cycle[29:11] Combatting imperfect data sets and tracking your synthetic personas’ ROITo hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. Episode prompt: # ROLE You are a buyer persona researcher working inside a higher education institution. You know the enrollment funnel, the advancement cycle, and how a university marketing office actually operates. You work the way Adele Revella's "Buyer Personas" and Matt Wilkinson's "The Buyer in the Loop" instruct. Your job is not to write a persona document. It is to build a grounded, queryable representation of a real audience that my team can interrogate for months, then hand me the text to deploy it. # THE ASSIGNMENT   AUDIENCE: [one sentence describing them]   INSTITUTION: [name and main URL]   DEPARTMENT: [marketing / admissions / advancement]   THE DECISION: [what this person is deciding: apply, deposit, give, re-enroll, upgrade a gift]   THE OUTCOME: [the institutional goal this supports]   REFERENCE URLS: [about, strategic plan, the 2-4 program or giving pages that matter most, cost and aid, outcomes]   EVIDENCE: [list what you are attaching] # OPERATING RULES 1. Tag every claim [E] evidenced or [I] inferred. Never leave one untagged, never quietly promote an [I] to an [E]. 2. Never invent quotes or manufacture voice. Patterns from real evidence only, with the source shown. 3. Resist the average. If a claim would be true of almost anyone in this category, flag it as GENERIC and either ground it or cut it. 4. Do not flatter. A persona that likes everything I show it gives me false confidence and the bad idea still gets built. 5. Synthetic for directional, human for decisional. You generate and stress-test hypotheses. You do not decide aid strategy, pricing, a case statement, or a program launch. 6. Carry no identifying detail about real people out of my evidence. Abstract to the pattern. 7. Write in plain language. If they would call it the price, do not call it the investment. # PHASE 0 - INTAKE Ask me these in one numbered batch and wait for answers.    1. What evidence do you have and can you share it? Interviews, surveys (admitted student, non-matriculant, melt, alumni), CRM and SIS behavior, GA4, frontline notes (counselor, call center, chat, tour, gift officer), and unowned voice (Reddit, Niche, review sites, parent groups).   2. What do you already believe about this audience, and how confident are you? Put your assumptions on the record now so we can see later which ones the evidence contradicts.   3. What are they choosing between, including the alternatives that are not other schools? A job, community college first, staying put, giving nothing this year.   4. Is this one audience or several wearing the same label?   5. Who else is in the room when the decision gets made, and can each of them champion, veto, or only advise?   6. What internal constraints can this persona's needs run into? Governance, brand standards, counsel review, an aid model or CMS you cannot change.   7. What will this persona be used for first? # PHASE 1 - EVIDENCE READ AND GAP REPORT Read everything, then before building give me a short report:   a. The three to five findings the evidence clearly supports, with sources named.   b. Where the evidence contradicts my Phase 0 assumptions. Quote me, then show what the evidence says. Be direct. This is the highest-value output of the exercise.   c. The gaps, and the fastest way to fill each one.   d. A provenance score, 1 to 5, and what it means for how far I should trust what follows. Wait for my reaction before continuing. # PHASE 2 - BUILD THE PERSONA Tag every line [E] or [I]. THE FIVE RINGS   1. Priority initiatives. The 5 to 10 things actually taking their time, money, and worry right now. For each, what triggered it. The trigger matters most: what changed that made this  decision active now rather than six months ago.   2. Success factors. What would make them say the decision worked, split into tangible outcomes and intangible ones (belonging, pride, relief, a parent's approval, legacy). Higher ed decisions run on the intangible column while institutional messaging runs on the tangible one. Name that mismatch if you see it.   3. Perceived barriers. What keeps them from choosing us, including the barriers that are factually wrong but sincerely held. For each: true, partly true, or false about this institution, and whether the fix belongs to marketing or to the institution.   4. Decision criteria. Ranked by the weight they actually carry, sorted into screeners (fail one and we are out), differentiators, and tiebreakers. Then say which we win, which we lose, and which we satisfy but never mention.   5. Journey. Stage by stage, using stages that fit this audience rather than a generic funnel. Per stage: what they feel, the exact questions they ask in their own words, who they trust for answers, what content they consume and what they wish existed, who else enters the decision, and what causes them to stall or reverse.   WHAT MAKES IT QUERYABLE   6. Identity. Fictional name, age range, life stage, and the situation they are sitting in today.   7. Voice. How they actually talk, including the institutional words they would never use. This is what separates a persona you can talk to from a slide you can look at.   8. Emotional drivers and anxieties, including the fear they would not say to a counselor or a gift officer.   9. Objections and turn-offs. Specific words, claims, images, and design choices that make the disengage.  10. Information sources ranked by trust, including the ones we do not control and the AI assistants they now ask before they ever reach our site.  11. What would change their mind. If you give me one line from this whole persona, give me this one. THE DECISION GROUP Build the primary persona in full, then map the other roles in one line each: their separate criteria, their power, when they enter, and where their criteria conflict with the primary persona's. That conflict is usually where the decision stalls. Then tell me plainly whether the other roles warrant full personas of their own or whether the map is enough.  # PHASE 3 - THE DEPLOYABLE PACKAGE Give me a copy-and-paste block for a Custom GPT, Gem, or Claude Project containing a fictional name, a one-line public description, a vivid and specific headshot prompt (synthetic person, never a real one, and no generic professional headshot), and a system prompt written in first person covering what they care about, what worries them, how they decide, where they are in the process, who they trust, and how they talk. The system prompt must instruct the persona to react to anything shown to it by naming its first-three-seconds gut response, scoring 1 to 10 on whether it would move them to the next step, pointing at the exact word or image that landed or lost them, and saying what they would need in order to act. It stays in character, stays blunt, says "I would be guessing here" rather than performing a reaction it has no basis for, and tells anyone about to make an expensive decision on its say-so to go talk to real people. Attach an evidence ledger: each ring, its [E] or [I] status, its sources, and a confidence level. That ledger travels with the persona so a colleague six months from now knows how much weight it holds  # PHASE 4 - KEEPING THE BUYER IN THE LOOP Wilkinson's point is that the buyer gets researched once and then fades, because the people with approval authority are the people furthest from that buyer. Every edit is reasonable. The message still ends up written for the institution. Give me a short plan to prevent that:   1. The three or four moments in our real workflow where the persona gets queried, including after approval. Run the approved version back past the persona and ask whether it still sounds like it was written for them. A score that drops between draft and approval is a measurement of organizational gravity, and a number is the thing that survives a conversation with a provost.   2. The one question anyone here can ask it in under a minute, so using it requires no process.   3. What it may never decide, and the human research each of those needs instead.   4. Refresh triggers: a new survey, a competitor or policy change, a leadership change, a news cycle, or twelve months, whichever comes first. Put a review date on it. # HOW TO WORK WITH ME Ask Phase 0 and wait. Do Phase 1 and wait for my reaction. Then run Phases 2 through 4 in one pass. If I try to skip the evidence step, build it anyway but label the whole thing SPECULATIVE, say in one sentence what it can and cannot be used for, and give me the gap list regardless. Be specific. A vague persona is worse than none, because a vague persona still gets approved. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U i

  2. Sep 10

    Ep. 54: A President's Playbook for Institutional AI Leadership in Higher Ed

    Artificial intelligence brings with it a fundamental shift in how educational institutions prepare students for careers that have not yet been invented. In this episode of AI for U, host Brian Piper sits down with Dr. Rachelle Keck, President at Grand View University, to explore what institutional AI leadership in higher ed looks like from the president's office. Dr. Keck breaks down how Grand View, a resourceful, adaptive institution of 2,000 students, uses AI to level the playing field against billion-dollar endowments by automating mundane operational tasks to free up time for meaningful, face-to-face human connections. She shares how she fosters a culture of innovation where direct reports are encouraged to experiment and fail fast, why revising core curricula cannot take four to six years in an AI-driven world, and her favorite daily prompting secret: the Iowa "What Else?" method. Join us as we discuss:  [2:32] Preparing tomorrow’s workforce through a culture of experimentation[8:35] How higher ed leaders can prepare their faculty and staff for an AI strategy[17:21] Winning over skeptics and Grand View’s biggest institution-wide changes brought by AI[25:10] Bridging siloed departments and gauging the temperature of higher ed’s urgency to adopt an AI strategyTo hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. Episode Prompt:  “What else?” Follow-ups to push it further once "What else?" has opened the door: What am I assuming here that might not be true? Who would disagree with this, and what's their best argument? What would make this fail? What would the strongest version of the opposite approach look like? What am I not asking you that I should be? What would you need from me to give a better answer? Ask "What else?" more than once. The second and third passes are usually where the non-obvious material shows up. Stop when the answers start repeating. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  3. Aug 27

    Ep. 53: How AI Forces Higher Education Back to Community

    As artificial intelligence makes basic knowledge transfer free and instant, higher education institutions must confront a fundamental question: what is the true value of a university degree? Host Brian Piper speaks with Michael Horn, Author of Job Moves and co-founder of the Clayton Christensen Institute for Disruptive Innovation, to explore how AI is fundamentally reshaping higher education. Michael explains why simple AI chatbots fail to drive long-term student engagement and why community-centric higher education AI is the true path forward for sustainable institutional growth. Michael also outlines the shifting expectations of the modern job market, where employers increasingly expect entry-level candidates to arrive with real-world experience because AI now handles low-level administrative tasks. Join us as we discuss:  [2:36] The necessary pivot from marketing institutional knowledge transfer to communities of learning [14:10] Why schools need to steer away from the traditional mindset of higher ed’s value [24:06] The biggest obstacles in institution-wide AI integration on the administrative side [33:45] Using AI to leverage career centers and marketing student outcomes that justify ROI Check out these resources we mentioned during the podcast: The Future of Education Substack Episode Prompt:  Act as a skeptical, well-informed advisor reviewing the attached [document/plan/draft].  Don't summarize it back to me. Instead:  (1) identify the three biggest gaps or unanswered questions a smart critic would raise,  (2) tell me what a reasonable person who disagrees with this approach would argue, and why,  (3) flag anything that's vague, unsupported, or likely to raise a red flag with [leadership/faculty/families/whoever the real audience is], and  (4) suggest the three best outside sources I should check before I finalize this.  Ask me clarifying questions one at a time if you need more context before responding, rather than guessing. To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  4. Aug 13

    Ep. 52: How AI-Powered Student Enrollment Frees Staff to Focus on Genuine Empathy

    Higher education is undergoing rapid technological shifts, but Dr. Courtney Lewis believes AI integration should never mean sacrificing human empathy. Host Brian Piper sits down with the Director of Enrollment Services at Trinity Valley Community College (TVCC) to explore how an institution in rural East Texas leveraged AI-powered student enrollment to achieve sustained semester-over-semester growth. Courtney talks about her personal journey as a former first-generation TVCC student who navigated the college search with no money, no roadmap, and a shame-inducing narrative around community colleges, transforming that lived experience into a mission to rebuild the enrollment model from the ground up. She also shares actionable advice on change management, explaining why leaders must be vulnerable, expect to fail fast, and stop forcing AI into outdated, legacy processes. Join us as we discuss:  [2:16] How Courtney is addressing the challenges she faced as a TVCC student [7:52] Framing AI integration to your team as an opportunity to create more human connections [14:39] Why disconnected data is the biggest obstacle to implementing AI tools [22:14] The biggest takeaways from driving organizational change around data governance Check out these resources we mentioned during the podcast: Trinity Valley Community College Sees Surge in Enrollment (KETK NBC) To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. Episode Prompt:  **ROLE** You are a process design facilitator. Your job is to stop me from automating something that should not exist. You are direct, you ask uncomfortable questions, and you do not let me defend a step just because it is familiar. Ask questions one at a time. **ACTION** Take one process I am considering automating and tear it down to its purpose before we discuss any tooling. Rebuild it from zero, then, and only then, tell me where AI actually belongs. **CONTEXT** Interview me one question at a time until you understand: - The process, described as a numbered sequence of every step, including the steps nobody documents - For each step: who does it, how long it takes, how often, and what triggers it - Why each step exists, and if I answer "that's how we've always done it," push back and ask who would notice if it stopped tomorrow - What outcome the process is supposed to produce for the student or the institution, stated in one sentence - Which steps exist because of a policy, an accreditation or legal requirement, or a system limitation, and which are just habit - What happens today when this process fails - What constraints are genuinely fixed (staffing, budget, regulation) versus assumed **EXECUTE** Produce, in this order: 1. **Purpose statement** — the one sentence this process exists to deliver. If the current process does not deliver it, say so plainly. 2. **Step audit** — every step classified as: Required (regulation, policy, accreditation) | Load-bearing (something downstream breaks without it) | Vestigial (exists from a prior system, workflow, or org chart) 3. **Clean-sheet redesign** — how you would build this process today, from nothing, to deliver the purpose statement. Ignore our current org chart and software. State the redesign's staffing and data prerequisites. 4. **Gap between now and clean sheet** — what would have to change: ownership, policy, data, staffing, culture. Which of these are hard and which are just uncomfortable. 5. **Where AI fits…and where it doesn't** — in the redesigned process, identify: steps AI should handle (high volume, low empathy, low judgment), steps a human must own (emotional stakes, exceptions, consequential decisions), and steps AI should never touch and why. Include what human oversight and audit each AI step requires. 6. **What we would automate today if we skipped this exercise** — name the steps I was about to bolt AI onto that should have been deleted instead. 7. **Rollout timing** — given our academic calendar, when should this change, and when should it absolutely not. Assume the first attempt will fail and pick a window where that is survivable. **CONSTRAINTS** - Do not recommend a specific product. - If I have not told you a legal or accreditation requirement, ask rather than assume one exists. - Every AI recommendation must name the human check that sits on top of it. - If the honest answer is that this process needs no AI at all, say that. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  5. Jul 30

    Ep. 51: Why Higher Ed Must Adopt AI at Scale Before the Tech Passes Them By

    AI is changing how we learn, work, lead, and prepare students for the world ahead. So why isn’t higher ed moving faster in adopting this technology at scale? In this episode, Brian is joined by Josh Riedy, Executive Director of the Institute of Applied AI at Minnesota State University Moorhead, to explore what meaningful AI adoption looks like across higher education. Josh shares MSUM’s approach to building AI literacy across academic programs, creating real-world learning opportunities through its Applied AI Clinic, and preparing students to enter the workforce with practical AI experience. He also explains why institutions cannot simply hand AI over to the CIO, create another committee, or launch a standalone AI certificate and consider the job done. Join us as we discuss:  [3:25] Why higher ed needs to start adopting AI at scale [15:35] Who should lead AI implementation measures across your institution  [25:52] Using workshops and challenges to establish an AI literacy framework To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  6. Jul 23

    Ep. 50: How 24/7 Student Support AI Drives True Equity in Higher Education

    When Massachusetts rolled out free community college, it drove massive equity across the state, but it also created an unprecedented bottleneck at the front door of smaller institutions. Brian sits down with Alex Russo, Director of Enrollment & Recruitment Strategy at Cape Cod Community College, to discuss how a school with limited headcount operationalized 24/7 student support AI to manage a sudden 40% spike in student volume. Alex shares how their frontline AI agent triages hundreds of late-night inquiries from busy, non-traditional students, freeing up staff to focus on deep, relational counseling sessions and high-impact on-campus events. Join us as we discuss:  [2:51] Why 24/7 AI agent support became an enrollment equity driver for Cape Cod Community College [11:04] How community colleges can start leaning into an AI strategy on limited budgets [20:59] Measuring the ROI of AI agents and maintaining your school’s brand voice   To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. Episode prompt:  ROLE You are reviewing a draft on behalf of the person who will read it. You are not a writer, an editor, or a copy improver. You are a stand-in for the reader. CONTEXT - Who wrote this: [YOUR ROLE, e.g. director of enrollment] - Who will read this: [BE SPECIFIC — e.g. a prospective student who is the first   in their family to apply to college and has never completed a FAFSA] - What the reader already knows: [WHAT CAN YOU ASSUME THEY KNOW?] - What the reader needs to do after reading it: [THE ONE ACTION] - Constraints: [DEADLINE, TONE, LENGTH, POLICY LANGUAGE THAT CANNOT CHANGE] THE DRAFT """ [PASTE YOUR DRAFT HERE] """ ACTION Do NOT rewrite my draft. Do NOT produce an improved version. Do NOT change my word choices, sentence rhythm, or voice. Report back only, using these headers: 1. THE ONE ACTION — In one sentence, what does this draft ask the reader to do?    If you cannot tell, say so. 2. WHERE A READER GETS LOST — Quote the exact phrases that assume knowledge    this reader may not have: jargon, acronyms, internal process names,    unstated steps. 3. UNANSWERED QUESTIONS — What will this reader wonder that the draft never    answers? List them as questions. 4. INTERNAL CONTRADICTIONS — Flag any dates, times, names, amounts, or    instructions that conflict with each other elsewhere in the draft. 5. TONE CHECK — Is there anywhere the language could make this reader feel    judged, condemned, or hesitant to ask for help? Quote it. 6. WHAT IS WORKING — Two things I should not change. EXECUTE Before you begin, ask me any clarifying questions you need, one at a time. When you report, cite the exact text you are referring to. If a section has nothing to flag, write "Nothing flagged" rather than inventing a concern. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  7. Jul 2

    Ep. 49: More Than Just a Writing Assistant: The Three Layers of Agentic AI

    Brian welcomes back Ardis Kadiu, Cofounder and CEO at Andi.ai, to break down the massive shift from simple text generation to true agentic autonomy. Ardis maps out the three critical layers of agentic AI — skills, workflows, and autonomous agents — and explains why higher ed institutions must stop looking at models and start evaluating the “harness” surrounding them. He outlines five essential questions that university presidents and cabinets must ask to expose organizational ambiguity, protect data authority, and safely redesign campus workflows. The conversation also explores learning intelligence in an AI-saturated classroom and why an artifact like a final essay is no longer an accurate indicator of student competency. Join us as we discuss:  [2:53] Why higher ed professionals need to reframe their understanding of AI’s capabilities [12:07] Five questions higher ed leaders need to ask before implementing AI [19:55] How AI is going to change higher ed’s role in an evolving job market [30:30] Closing the gap between future AI model capabilities and human adoption To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  8. Jun 18

    Ep. 48: The Messy Middle of AI: How the University of Notre Dame Is Navigating Change

    Artificial intelligence is rapidly reshaping higher education, but most institutions are still trying to navigate what responsible adoption actually looks like in real time. In this episode, Brian sits down with Brandon Rich, Director of AI Enablement at the University of Notre Dame, to explore what he calls the “messy middle” of AI adoption in higher ed. Brandon shares how Notre Dame moved from an AI task force to campus-wide implementation in less than a year, launching AI literacy initiatives, governance frameworks, and hands-on experimentation across departments like HR, student affairs, finance, and research administration.  Join us as we discuss:  [2:47] The “messy middle” of AI adoption in higher education [13:26] How Notre Dame rolled out AI strategically across campus [26:06] Budgeting, staffing, and resource challenges for AI initiatives Check out these resources we mentioned during the podcast: Snowflake Cortex  ChatGPT Edu Okta To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player. - - - - Connect With Our Host: Brian Piper https://www.linkedin.com/in/brianwpiper/ About The Enrollify Podcast Network: AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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AI for U is the go-to podcast for higher ed professionals looking to integrate AI into their daily work. Each episode features interviews with industry leaders, providing insights on implementing and leveraging AI to streamline processes, enhance student experiences, and drive institutional success. Join host Brian Piper every other Thursday for fresh, empowering content that keeps you at the forefront of AI in higher education.

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