⚡️Assessment Unlocked: High‑impact, higher clarity

High‑Impact Practices promise transformational learning—but assessing their impact consistently and equitably remains a challenge. This week’s post shows how GenAI can help institutions evaluate HIPs more effectively without turning meaningful learning into shallow metrics.

🌟 Introduction

High‑Impact Practices—like undergraduate research, internships, service learning, and capstones—are often celebrated and under‑assessed. Evidence exists, but it’s scattered, qualitative, and hard to compare. GenAI can help assessment teams organize, interpret, and connect HIP evidence at scale while keeping human judgment in the driver’s seat. The goal isn’t to mechanize reflection—it’s to finally make it usable.

Key takeaway: If HIPs are high impact, their assessment should be high quality.

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📚 Background

High‑Impact Practices (HIPs), as articulated by Kuh and AAC&U, are educational experiences consistently associated with improved student engagement, persistence, and learning across diverse student populations (Kuh, 2008; AAC&U, 2013). HIPs are especially powerful when they are intentionally designed, well‑scaffolded, and equitably accessible.

From an assessment standpoint, HIPs present a paradox. They generate rich learning evidence—reflections, portfolios, projects, community products—but that evidence is difficult to analyze systematically. NILOA has emphasized that institutions must move beyond counting participation toward evaluating learning quality and equity of access (NILOA, 2022). Similarly, AAC&U urges institutions to connect HIP participation to meaningful learning outcomes using authentic assessment approaches such as VALUE rubrics (AAC&U, 2015).

Scholarship in assessment highlights that HIP evaluation requires mixed methods and faculty interpretation to preserve validity (Banta & Palomba, 2015; Kuh et al., 2015). Yet time, staffing, and data fragmentation often limit what campuses can realistically analyze. As a result, HIP assessment frequently defaults to participation rates or student satisfaction rather than learning evidence.

Recent guidance from teaching and learning centers suggests that GenAI can support HIP assessment by assisting with qualitative synthesis, rubric alignment, and cross‑artifact pattern detection—when paired with faculty review and governance structures (Harvard Bok Center; Vanderbilt Center for Teaching). In this role, AI acts as a sensemaking accelerator, not a learning judge.

From a validity lens, this matters. Evidence only supports claims when it is interpreted systematically, transparently, and collaboratively. GenAI can help institutions do that work more consistently—without lowering standards.

Key takeaway: HIP assessment fails most often from scale problems, not from lack of evidence.

References

• Kuh, G. D. (2008). High‑impact educational practices.
• AAC&U. (2013). High‑impact practices.
• AAC&U. (2015). VALUE rubrics.
• NILOA. (2022). Equity in assessment.
• Banta, T. W., & Palomba, C. A. (2015). Assessment essentials.
• Kuh, G. D., et al. (2015). Using evidence of student learning.
• Harvard Bok Center for Teaching and Learning. AI guidance.
• Vanderbilt Center for Teaching. Generative AI resources.

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🛠️ Best practices & tips

Here’s how campuses are responsibly using GenAI to assess HIPs:

• 🧩 Align artifacts to outcomes first
  Ask GenAI to map reflections or projects to specific PLOs or VALUE rubric dimensions. Humans confirm alignment.
• 🏷️ Use AI for first‑cycle qualitative coding
  Let GenAI suggest initial themes across reflections or portfolios, then refine them with faculty norming.
• 📊 Connect participation to learning evidence
  Use AI to cluster outcomes by HIP type (internship, research, service learning) to see where learning patterns differ.
• 🔍 Surface equity patterns
  Prompt GenAI to compare learning themes across demographic or access groups (with proper data governance).
• 📝 Draft narrative summaries for review
  Generate draft improvement narratives that faculty edit and approve.

Quick win: Add a short AI‑assisted synthesis section to your HIP annual report.

Key takeaway: HIP assessment improves when learning evidence becomes interpretable.

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🏫 Example or case illustration

Setting: A regional university evaluating undergraduate research and service‑learning experiences for accreditation.

Participation data looked strong. Learning claims were harder to support. The assessment office had hundreds of student reflections and project summaries but limited staff time.

They piloted a GenAI‑assisted workflow. After anonymizing data, they asked the model to:

• code reflections using VALUE rubric dimensions,
• summarize dominant learning themes, and
• highlight differences between research and service‑learning experiences.

The friction point was concern about oversimplification. Faculty worried nuance would be lost. To address this, the team required every theme to be paired with multiple student excerpts and held a short validation session.

Faculty refined codes, merged two themes, and rejected one entirely. The final analysis revealed that service‑learning strongly supported civic engagement outcomes, while undergraduate research more strongly supported inquiry and communication.

Resolution: The institution could now defend its HIP impact claims with evidence—not just participation counts.

Key takeaway: GenAI didn’t evaluate learning—it made learning evidence manageable.

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🔮 What’s next

Next week, we’ll explore AI‑supported learning outcomes mapping across complex curricula.
Prep action: Pull one program map that feels accurate but not very informative.

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❓ Question of the day

Which High‑Impact Practice on your campus has the strongest stories—but the weakest assessment evidence?

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🚀 Call to action

This week, select one HIP and try an AI‑assisted qualitative synthesis of 20 student artifacts. Review results with a faculty partner before drawing conclusions.