± HEALTH In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have b…+ CLIMATE The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, ren…± HEALTH Objectives This study aim to develop, compare and internally validate machine-learning models for predicting…+ HEALTH Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor e…− SCIENCE Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstructi…+ HEALTH Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative p…± HEALTH Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical…± HEALTH Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of d…
TruaceTracing the truth around AISaturday, September 12, 2026
Subscribe
TRV-2026-1026Certified recordPeer-reviewed

Developing validity arguments for artificial intelligence-based assessment: Balancing affordances and threats

Background Artificial intelligence (AI) is increasingly used to generate, score and interpret educational assessment, yet these applications are being adopted in a largely unregulated environment. This creates a paradox: Whereas AI systems used in clinical care are subject to formal scrutiny for safety, performance and monitoring, AI systems used to inform consequential decisions about learner progression and future clinical practice are not. Existing validity frameworks remain useful but may not fully account f…

Education · The Trace — both readings · certified 2026-09-09 · v1 · article view · machine-readable

Current reading — gain

AI systems can generate, score and interpret educational assessments that inform learner progression, with design and governance determining whether cross-cutting mechanisms function as affordances.

Current reading — problem

AI-based assessment introduces distinct validity threats across scoring, generalisation, extrapolation and implications, including contamination, instability, inequities, automation bias and deskilling when used for consequential learner progression decisions.

What this doesn’t fix

Findings derive from a conceptual review synthesizing literature rather than new empirical multi-site validation of an AI assessment system.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-1026, v1: “Developing validity arguments for artificial intelligence-based assessment: Balancing affordances and threats.” Truvace, 2026-09-09. /record/TRV-2026-1026 (accessed at citation time). sha256 3b03b3e18455a5a6…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv13b03b3e18455…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-1026 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.