Auditable assessment redesign for teaching readiness in preservice teacher education under generative AI
Year: 2026
Type: article
Abstract: Generative AI is rapidly reshaping educational practice, yet in preservice teacher education it remains unclear how pedagogical reasoning can be evaluated when AI-assisted outputs become increasingly polished and accessible. This issue is especially salient in programs that prepare future teachers to translate digital tools into classroom-ready pedagogical decisions. This study examined how an undergraduate teacher-education program redesigned assessment to address the gap between AI exposure and teaching readiness under AI-allowed conditions. Using a convergent mixed-methods evaluation design, we triangulated curriculum documents with student, graduate, employer, and course-evaluation surveys to identify recurring priorities for improvement. Across sources, convergent evidence highlighted recurring priorities related to hands-on guidance, practicum and micro-teaching alignment, reusable exemplars, data-informed diagnosis, and explicit boundaries for responsible AI use. These were subsequently synthesized into three linked redesign priorities concerning teaching-facing transfer, auditable process evidence, and clearer AI-use boundaries, which informed an auditable assessment redesign comprising task templates, evidence trails, an analytic rubric, and practicum-oriented mapping. To strengthen the evidence base beyond self-report and document analysis, two raters independently scored 24 sampled final artifacts, yielding acceptable agreement for the rubric total score (ICC(2,1) = 0.77). A micro-teaching evidence pack was also used as an enactment proxy, alongside two cross-institution implementations that provided preliminary evidence of scoring defensibility, classroom feasibility, and cross-site workability. Rather than making causal impact claims, the study offers feasibility-oriented evidence that assessment redesign under AI-allowed conditions can make pedagogical reasoning more visible, more assessable, and more closely connected to practice-oriented performance in preservice teacher education. The findings contribute to current discussions of AI-aware teacher preparation and assessment redesign by showing how process-visible evidence can support more credible inferences about teaching readiness than product-only evaluation alone.
Source: Discover Education
Author: Hua Liu
Institution: Shanghai Normal University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,390
OpenAlex ID: W7225613638