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TRUVACE RECORD VERSION
record: TRV-2026-1890
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-19T10:41:55.878300Z
status: published
lens: g_space
sector: education
headline: From Chaos to Consistency: A Human-Centered Framework for AI-supported Assessment at a Mission-Driven Liberal Arts College
dek: This practitioner inquiry reports on a pilot at a small Catholic liberal arts college that used generative artificial intelligence (GenAI)-supported tools to help faculty complete program-assessment documentation during a structured Education Day. Grounded in Catholic social teaching and human-centered AI principles, the pilot tested a framework organized around human judgment, ethical stewardship, and formative human-AI partnership. Survey results from the pilot cohort (N = 10; item-level valid responses varied…
gain_title: In a pilot of ten faculty, AI-supported tools used within a human-judgment framework deepened thinking about evidence of student learning and won unanimous intent to keep using them.
problem_title: (none)
trace_subject: (none)
gain_reading: In a pilot of ten faculty, AI-supported tools used within a human-judgment framework deepened thinking about evidence of student learning and won unanimous intent to keep using them.
gain_evidence: unanimous agreement that the process deepened thinking about evidence of student learning | unanimous intent to use AI-supported tools in future cycles
problem_reading: (none)
problem_evidence: (none)
quick_read: A small Catholic liberal arts college piloted generative AI tools to help faculty complete program-assessment documentation during a single structured Education Day. The approach was built on Catholic social teaching and human-centered AI principles, stressing human judgment, ethical stewardship, and partnership between people and AI.

The pilot matters because it offers an early model for mission-driven institutions weighing AI in routine academic assessment work. The evidence is thin, though: the cohort was very small and the results come from self-reported faculty perceptions, so they say little about whether assessment quality or student learning improved.
limitation: The survey cohort was only ten faculty and item-level valid responses varied, so the endorsement reflects a very small, single-institution sample.
tag: Evidence-backed gain
key_points: The pilot took place at a small Catholic liberal arts college during a structured Education Day. | The framework rested on human judgment, ethical stewardship, and formative human-AI partnership. | The survey cohort was ten faculty, and item-level valid responses varied. | Qualitative analysis found five themes, including human-in-the-loop validation and data governance. | Authors suggest faculty formation, verification checkpoints, and governance should precede broad rollout.
rundown: The pilot is a practitioner inquiry rather than a controlled trial. Faculty used GenAI-supported tools to complete program-assessment documentation, a task that can be inconsistent across departments, and the framework was tested in that setting.

Survey responses showed strong endorsement. Every respondent agreed the process deepened thinking about evidence of student learning, and all intended to use AI-supported tools in future assessment cycles.

Qualitative findings grouped into five themes: efficiency and cognitive scaffolding, human-in-the-loop validation, platform reliability and trust, data governance, and institutional support infrastructure. Reliability, trust, and data governance appear as concerns alongside the efficiency benefits.
sources:
- peer_reviewed | Intersection: A Journal at the Intersection of Assessment and Learning | https://doi.org/10.61669/001c.172640 | 2026-10-19
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