Curriculum-Instruction-Assessment triad taxonomy of AI educational purposes in VET

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 review), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description

Building on Wang et al. (2024) and Dai et al. (2025), the review organizes AI applications in VET "into the main categories of the Curriculum-Instruction-Assessment-Triad (Pellegrino, 2012)", plus holistic system-level functions. The curriculum domain covers curriculum creation and analysis; the instruction domain distinguishes seven pedagogical functions (instructional planning, adaptive learning, intelligent tutoring, conversational agents, content curation, instructional creation, authentic simulation); the assessment domain covers intelligent assessment and profiling/prediction; holistic functions include administration and guidance. Table 1 maps each purpose to AI types (ML, NLP, rule-based, computer vision) and example applications.

Accounts

• Curriculum-Instruction-Assessment triad taxonomy of AI educational purposes in VET: Building on Wang et al. (2024) and Dai et al. (2025), the review organizes AI applications in VET "into the main categories of the Curriculum-Instruction-Assessment-Triad (Pellegrino, 2012)", plus holistic system-level functions. The curriculum domain covers curriculum creation and analysis; the instruction domain distinguishes seven pedagogical functions (instructional planning, adaptive learning, intelligent tutoring, conversational agents, content curation, instructional creation, authentic simulation); the assessment domain covers intelligent assessment and profiling/prediction; holistic functions include administration and guidance. Table 1 maps each purpose to AI types (ML, NLP, rule-based, computer vision) and example applications. (Deutscher et al. (2026))

Claims

• The review reports AI curriculum-design tools identify learning gaps and equity-analysis tools examine differential policy impacts on student populations [+W]

Related Research Methods

• Working classification of chatbot and language-model technologies for AI coaching
• Classification framework distinguishing global quality, global process quality, quality pedagogical interactions, and quality interactions related to curriculum content

Key Sources

• Deutscher, V., Thomann, H., Zlatkin-Troitschanskaia, O., Weyland, U., Abele, S., Danek, A. H., Greiff, S., Rausch, A., Seeber, S., Seifried, J., & Winther, E. (2026). Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness. Computers and Education: Artificial Intelligence 11 (2026) 100628. https://doi.org/10.1016/j.caeai.2026.100628

Page metadata

Field	Value
type	research-method
id	curriculum-instruction-assessment-triad-taxonomy-of-ai-educational-purposes-in-vet
title	Curriculum-Instruction-Assessment triad taxonomy of AI educational purposes in VET
description	Building on Wang et al.
status	review
generated	by process:settle-candidates on 2026-10-12
trust	unverified — no one has confirmed this page's content

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