Three paradigms of AI integration in education: AI-directed, AI-supported, and AI-empowered

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The review adopts Ouyang and Jiao's (2021) framework, which "frames the educational role of AI along three paradigms that reflect increasing levels of learner agency": AI-directed (learner-as-receiver, rooted in behaviorism, with AI structuring content and giving prescriptive feedback), AI-supported (learner-as-collaborator, grounded in cognitive and social constructivist theories, with dialogic tutoring and exploratory environments), and AI-empowered (learner-as-leader, aligned with connectivism and complex adaptive systems theory, where learners co-design learning trajectories). The review uses this framework as the coding scheme for RQ3 on theoretical conceptualization of human–AI interaction.

Accounts

• Three paradigms of AI integration in education: AI-directed learner-as-receiver, AI-supported learner-as-collaborator, AI-empowered learner-as-leader: The review adopts Ouyang and Jiao's (2021) framework, which "frames the educational role of AI along three paradigms that reflect increasing levels of learner agency": AI-directed (learner-as-receiver, rooted in behaviorism, with AI structuring content and giving prescriptive feedback), AI-supported (learner-as-collaborator, grounded in cognitive and social constructivist theories, with dialogic tutoring and exploratory environments), and AI-empowered (learner-as-leader, aligned with connectivism and complex adaptive systems theory, where learners co-design learning trajectories). The review uses this framework as the coding scheme for RQ3 on theoretical conceptualization of human–AI interaction. (Deutscher et al. (2026))

Claims

• AI in VET is predominantly implemented through behaviorist or cognitively oriented instructional designs emphasizing drill-and-practice and adaptive feedback, while approaches fostering learner agency and critical reflection remain underrepresented [+M]

Related Research Methods

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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	three-paradigms-of-ai-integration-in-education-ai-directed-ai-supported-and-ai-empowered
title	Three paradigms of AI integration in education: AI-directed, AI-supported, and AI-empowered
description	The review adopts Ouyang and Jiao's (2021) framework, which "frames the educational role of AI along three paradigms that reflect increasing levels of learner agency": AI-directed (learner-as-receiver, rooted in behaviorism, with AI structuring content and giving prescriptive feedback), AI-supported
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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