Skip to content

Assessment twins: two deliberately linked assessment components addressing the same outcomes through different modes of evidence

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 theoretical), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article names and substantively describes assessment twins as a redesign approach for the GenAI era. Twins are conceptualised as "two deliberately linked components that address the same learning outcomes through different modes of evidence, scheduled closely together to allow for cross-verification". The pairing lets instructors triangulate evidence across formats so that validity can be checked even when one format is vulnerable to generative AI misuse.

Design Implications

Context

Requirements

  • The two components must address the same learning outcomes and be scheduled closely together to allow cross-verification.

Constraints

  • The authors acknowledge challenges including resource intensity, equity concerns, and the need for empirical validation.

Target Learners

  • higher education students

Learning Goals

  • course learning outcomes assessed through GenAI-vulnerable formats

Claims

Examples

-

Key Sources

  • Jasper Roe, Mike Perkins, Louie Giray. (2026). Assessment twins: An approach for strengthening assessment validity in the age of generative AI. Journal of Applied Learning and Teaching, 9(2). https://doi.org/10.37074/jalt.2026.9.2.3
Page metadata
Field Value
type design
id assessment-twins-dual-linked-components
title Assessment twins: two deliberately linked assessment components addressing the same outcomes through different modes of evidence
description The article names and substantively describes assessment twins as a redesign approach for the GenAI era.
status review
generated by process:settle-candidates on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — jasper-roe-2026

Frontmatter is the page's machine-readable record — see the Schema & Guide.