13 Nuggets of AI Wisdom for Higher Education Leaders
Actionable AI Guidance for Higher Education Leaders
Recently, I have spent quite a bit of time on advisory boards, consulting, advising higher ed leaders and institutions, and giving presentations and workshops on generative AI in higher education.
Based on my observations, here’s what I would do as a higher ed leader to navigate the current AI landscape:
Lead from the coffee machine. As ridiculous as this may sound, you need to pick up cues from professors and staff on where they are with AI, what excites them, and which barriers they are confronted with. You’ll want to seek out opportunities to measure the temperature of your organization informally. Some things are best said in meetings and other formal settings. Other things are not.
Also, lead from the classroom. Sit in on random classes across departments. Complete assignments, even. Get a feel for when and how the wheels skip the movements in the cog. Talk to students about their goals and challenges. The deeper your understanding of the student experience, the better informed your decisions will be.
Forget about AI detection software. There is no magic software that can detect AI plagiarism, and chances are there never will be. What we are seeing now only makes everyone miserable: students end up being falsely accused of plagiarism, and professors get caught off guard with a technology they may not necessarily know the implications of using. So many things about AI are hype and opinions, but the problems with using AI detection software isn’t one of them. It is well documented that AI detection software doesn’t work. This excellent post by Stefan Bauschard provides additional resources and documentation on the matter.
Incentivize faculty AI innovation with AI. Not only does this approach show recognition and appreciation, but faculty innovators are key to making change happen. The best universities know this and have incentivized faculty innovation in teaching and research for years. They back up this commitment with policies, funding, and processes that give faculty freedom and support to transform education at both the individual course and institutional levels.
Invest in people first, then technology. Whether you come to see AI productivity as a cost saver or as an abundance driver that can fuel your sustainable competitive advantage, AI should enhance human potential, not replace it. Be as transparent as possible about how AI will impact jobs (you probably don’t know yet - but it’s on everyone’s mind); AI success stems from people. They must be the priority.
Don’t insist on solutions when people bring up problems. This is a solid general principle for leadership in higher ed, and particularly so regarding AI. Nobody has the right answers right now. You need to not only accept this, but also lean in and make this fact an explicit part of the conversation to generate new ideas and ways forward.
Deal with uncertainty. Most leaders prefer to know what’s around the corner. And in higher education, ensuring stable operations and output is what most leaders were trained (and are still incentivized) to do. But higher education today requires a willingness, not only to embrace, but to proactively initiate and forcefully drive creative processes. This is hard because higher education has exceedingly long product life cycles (students, research, etc.) and because it entails messy experimentation and non-linear thought processes as essential ingredients for synthesizing new ideas. Try to use the ambiguity to your advantage and spur creative problem-solving.
On teaching, learning, and assessment. AI has captured the attention of all institutional stakeholders. Capitalize to reimagine pedagogy and evaluation. Rethink lectures, examinations, and assignments to align with workforce needs. Consider incorporating Problem-Based Learning, building portfolios and proof of work, and conducting oral exams. And use AI to provide individualized support and assess real-world skills.
Actively engage students. Don't make assumptions about what students need or think. I am frequently surprised by the student perspectives on AI, and you would probably be too. Continuously engage students via focus groups, design sessions, advisory boards - or more informally by chatting in the hallway. Deepen your understanding of how students interact with technology, and empower their voices to guide your institutional process.
Get out of the Ivory Tower with AI implementations. When launching AI chatbots, digital advising systems, or analytics dashboards, rigorously test with real users first. Expect hiccups and false starts. Be ready to ditch what fails and build on what shows promise. Make small bets before going all in. With emerging tech, you will inevitably waste money and time, but doing nothing might just put you out of business.
Bridge the divide between the liberal arts and STEM. Admittedly, this will take time. But my point is this: AI is often perceived as solely a technical field. In reality, realizing the promise of these technologies requires both strong technical skills and critical thinking abilities. Your leadership team should seek to break down academic silos between liberal arts and STEM departments. Incentivize diverse perspectives. Build programs that intertwine humanities and technology. This cross-pollination will produce well-rounded students equipped for the AI era.
Don’t forget the water bottle. Think long and hard about the ethical and environmental consequences of AI. As a leader, your students (and staff) will hold you accountable for an equitable, transparent and fair use of the technology. This is your new reality.
Get your institutional AI strategy in place, right now. For all intents and purposes, for the reasons above, and for general organizational clarity and transparency, this item is no longer optional. Enough said.
All of this is not really about new technology.
While AI in higher education certainly involves new technology, it's equally about adopting a curious, human-centric, and humble approach in your institution and communities.
It’s about empowering learners in new, human-oriented and innovative ways.
Happy leading, exploring and innovating with AI.
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Agreed with all of the above and especially glad you highlight the environmental impacts of AI. I don't think we've seen nearly enough of a discussion on this topic yet, and as we think about the implications of equity and AI it's paramount that we consider the possible harm to our environment and equity implications of that harm, too.
Thanks for this, Jeppe.
However, I missed the intended link at the end of Step 3 to Stefan Bauschard’s post on AI plagiarism detectors.
His post is easy enough to find without the link (and very informative as well), but anyway…