While it may feel like the point in the semester when things start to get a little chaotic, thankfully, that’s not the kind of “mad” we’re talking about this week. And hopefully, The HEat Index brings a bit of calm to your already full day. In this week’s issue, we look at the results of a recent survey of student success administrators, then turn our attention to real examples of AI use by faculty. We’ll wrap up with a few thoughts on why the Machine Learning, AI & Data (MAD) Landscape is worth a closer look. 

After reading today’s issue, share your thoughts about faculty adoption of AI at your institution in the comments! 

What Student Success Leaders Think 

From Student Success Leaders Worry About Affordability, AI and Diversity | Inside Higher Ed

Inside Higher Ed’s second annual Survey of College and University Student Success Administrators offers an in-depth look at the priorities and perspectives shaping student success work today. 

Our Thoughts  

Student success leaders feel good about undergraduate quality and say success is a priority, which are excellent results. Unfortunately, a persistent gap remains between data and decisions. Only about a third report their institution uses student success data to drive action, which aligns with NASPA’s finding that leaders still need dashboards that translate information into next steps. The real headline is not “more reports.” It is creating a clearer flow of information that shortens the distance between a question and an action. 

For me, that idea of actionable information sits at the heart of the 2026 EDUCAUSE Top 10. While the word data appears regularly throughout the list, the deeper message is about surfacing insights to make the data usable. What institutions truly want is information that is contextualized, processed, and accessible, so users feel confident making better decisions that lead to stronger outcomes for their communities.  

Perhaps that is why connection serves as the central theme of this year’s Top 10. It is not just about connecting people but also connecting data across the campus. If student success is truly the priority, then connecting SIS, LMS, advising notes, and basic-needs signals into question-driven views is the work. That is how you close the aspiration gap and turn insight into impact without adding noise. 

Faculty Adoption of AI 

From AI Has Joined the Faculty | The Chronicle of Higher Education

More instructors are adopting AI in the classroom with differing degrees of success.    

Our Thoughts  

What I like about this piece is the range of faculty voices. You hear a religion scholar using AI to translate dense ideas for first-year students, a medievalist who built an AI-assisted textbook to reclaim class time, a business lecturer who uses AI to scale pre-class feedback, a communications professor who stress-tested AI on grading and found it wanting, and faculty who tried tutoring bots and saw mixed results. These stories matter because they reflect what’s happening on real campuses right now, not in theory, but in practice. 

Zoom out and the adoption numbers explain the tension. Tyton Partners polling shows that large shares of instructors are already using generative AI to create course materials, design activities, and build rubrics, and those rates continue to climb. AI is also moving into the platforms faculty touch every day, like Canvas, which is embedding model-powered experiences inside the LMS. At the same time, leaders in other sectors warn about “workslop,” the polished but low-value output that creates more cleanup for colleagues. Higher education can avoid that trap by pairing usage with clear standards for quality, transparency, and attribution. 

This is where hearing stories from the trenches should become part of the evidence base. Collect short use cases each term and document what problem the instructor tried to solve, how they set it up, what changed for students, and where the approach soared or fell short. This kind of transparent, human-led process not only shows students how and why AI is used but also keeps grading accountable and treats qualitative faculty experience as data that can guide the next iteration. 

Data Market Map 

From Bubble & Build: The 2025 MAD (Machine Learning, AI & Data) Landscape | Matt Turck

Matt Turck and team break down the state of the market for machine learning, AI, and data products and companies.        

Our Thoughts  

If you’re a regular reader of this blog series, you might be thinking “Wait a minute…didn’t you already try to sneak this MAD thing past me once? I come here for higher ed news.” And you’d be right. But hear me out and let me make a case for why this market map and accompanying blog post are worth your time.  

First, Turck’s MAD landscape is more than a hype collage. It’s a quick, visual way to see where the industry is headed and who the major players are in any space, especially when it comes to vendors that act on data and the merging of data. As we saw in the EDUCAUSE Top 10, that’s exactly where campuses are trying to make progress this year in student support, teaching workflows, and service operations. 

Second, vendors are moving quickly, so staying informed has become part of the job. If you haven’t guessed, there’s a bit of an AI arms race as companies work to add AI features to their products. Some will do it responsibly; others will just cram it in (and you probably already know which is which). Either way, the addition of AI may accelerate value or outpace governance at your institution. The MAD helps you spot the major players so you can approach vendor conversations with confidence, particularly around privacy and security.  

In the end, the value for higher education is not the logos. It’s gaining a clearer view of where AI and data are merging, how that aligns with your campus technology goals, and which vendor moves deserve your attention this semester. So yes, this absolutely belongs in a higher ed blog. The choices we make about AI and data will shape teaching, advising, and support for years to come. Read the map, circle what matters, and move one thing forward this academic year.