Most assessment efforts don’t fail because of missing data they stall because teams struggle to use what they’ve already learned. This week’s post explores how GenAI can help assessment professionals move from findings to action more efficiently, without short-circuiting faculty judgment, shared governance, or accreditation expectations.

Learning outcomes are the backbone of assessment—and also one of the most time-consuming, contentious, and quietly frustrating parts of the work. This week’s focus: how GenAI can support (not replace) faculty judgment to make learning outcomes clearer, more measurable, and more useful for assessment—without creating governance headaches or accreditation heartburn.

Rubrics are having a moment—because GenAI is exposing every fuzzy criterion we’ve ever tolerated. This week’s workflow shows how to use GenAI as a rubric debugger: catching ambiguity, improving inter-rater reliability, and tightening validity arguments without turning assessment into a robot uprising.

Mentoring programs are some of the most quietly powerful engines on any campus. When they’re built well, they boost belonging, sharpen academic confidence, and anchor students through the wobbliest semesters. When they’re not? They become coffee‑and‑chat clubs with no measurable impact. Today’s post unpacks how to design, evaluate, and continuously improve mentoring initiatives using logic models, participatory approaches, and (yes) a little AI assistance. Whether your institution is launching a new mentoring effort or refining one that’s been around since dial‑up internet, this guide offers a practical, evidence‑informed path forward.

Surveys can be magical—when they’re done well. They can illuminate student experiences, uncover instructional gaps, and give leaders the kind of clarity that spreadsheets alone just can’t offer. But when they’re done poorly? Well… let’s just say a dart-throwing octopus could produce cleaner data. Today’s post walks through the craft of survey design, from defining purpose to validating instruments and turning responses into meaningful insights. Whether you’re designing a quick pulse check or a full accreditation study, these principles will upgrade your approach.

Student success isn’t magic—it’s pattern recognition mixed with thoughtful human intervention. Every campus has students quietly drifting off course long before anyone notices, and the best retention strategies make those signals visible early enough to matter. Today’s blog explores how predictive analytics, mentoring models, and AI-aligned practices can help institutions create more responsive, equitable, and proactive environments. Whether you’re a data enthusiast, an advising lead, or a faculty member who’s noticed “the quiet fade” in your classes, this one’s designed to give you practical, campus-ready ideas.

This week, we’re diving into the art (and science) of evaluating and improving Program and Course Learning Outcomes (PLOs/CLOs). Using Fink’s Framework for Significant Learning and a little AI muscle (think LLM-powered feedback loops), we’ll explore how to transform vague verbs into vivid visions of learning. Whether you’re an assessment coordinator, a curriculum committee chair, or a first-year instructor just trying to decode Bloom’s Taxonomy — this one’s for you.

High-Impact Practices (HIPs) are like the kale of higher ed — everyone says they’re good for you, but few know how to make them taste great. Faculty and staff love the idea of transformative learning experiences, but many struggle with the “how.” This week’s blog focuses on designing HIPs that actually deliver on their promise of deeper learning, equity, and engagement — and how to assess that impact using both classical and cutting-edge (LLM-enhanced!) methods.

Program evaluation is where assessment meets strategy. It’s not just about proving success—it’s about improving it. Yet, in many higher ed settings, evaluation becomes a compliance exercise: reports are written, boxes checked, and shelves filled. This week’s blog takes a practical approach to program evaluation frameworks that help faculty, staff, and administrators connect evidence to meaningful change. From logic models to participatory and developmental approaches, you’ll learn how to pick the right framework for your goals—and how to make evaluation a tool for growth, not just accountability.

If you’ve ever read a survey and thought, “What are they even asking me?”—you’re not alone. Poorly designed surveys waste time, frustrate respondents, and lead to meaningless data. In higher education, where surveys influence program reviews, accreditation, and student success initiatives, we can’t afford vague or biased instruments. This week’s blog unpacks the anatomy of a high-quality survey—from purpose to pilot testing—and shows how AI can help refine items for clarity, alignment, and insight.

Introduction

We’ve all seen Program Learning Outcomes (PLOs) or Course Learning Outcomes (CLOs) that sound inspiring… but leave faculty, students, and accreditors scratching their heads. “Students will demonstrate leadership.” Lovely sentiment, but what does that mean in practice? Writing strong outcomes is an art and a science. This week, we’ll explore frameworks like Bloom’s Taxonomy and Fink’s Significant Learning framework, along with how Large Language Models (LLMs) can serve as editors, spot weaknesses, and suggest refinements. Strong outcomes don’t just live on paper—they drive teaching, learning, and student success.

High-Impact Practices (HIPs) are often described as the “secret sauce” of higher education—engaging assignments, applied learning, and meaningful reflection. But let’s be real: just because something is called a HIP doesn’t mean it automatically delivers deep learning. When done well, HIPs are structured, equitable, and measurable. This week, we’ll look at how to design HIPs that not only sparkle in theory but also actually move the needle on student success.