The Scaffolded AI Literacy (SAIL) Framework: Results of a Delphi Study for Equitable AI Literacy Framework Design in Education
Date
Authors
MacCallum, Kathryn
Parsons, David
Mohaghegh, Mahsa
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier BV
Abstract
Developments in AI technologies and their increasing use in education have prompted ongoing interest in the development of generally applicable AI literacy frameworks to ensure equity of access to knowledge, skills, and understanding. Researchers have investigated the components of AI literacy, how they can be structured, and how they can be developed in educators and learners. Despite much work in this area, and the opportunities and challenges presented by generative AI technologies, most recent frameworks have been confined to those that simply aggregate older ideas from the literature or those that focus on non-generalisable contexts. Few existing frameworks provide novel perspectives on generic approaches to AI literacy that also support equitable, scaffolded competency development for all learners, regardless of context. In contrast, this article reports on a Delphi study that led to the creation of the Scaffolded AI Literacy (SAIL) framework, which is broadly applicable across contexts but also accessible enough to be easily assimilated into the curriculum. Unlike many other frameworks, it provides a scaffolded pathway through competency levels that can be applied across all ages and stages of education and helps to address second- and third-level digital divides. This article details how the Delphi study unfolded, the key decisions that were made during the process, the resulting framework, and how it may contribute to equitable access to AI literacies.Description
Keywords
3904 Specialist studies in education, 4601 Applied computing, 4602 Artificial intelligence, Artificial Intelligence, AI Literacy, Digital Divide, Equity, Framework, Scaffolding
Source
Computers and Education: Artificial Intelligence, ISSN: 2666-920X (Print), Elsevier BV, 100584-100584. doi: 10.1016/j.caeai.2026.100584
Publisher's version
Rights statement
2026 The Authors. Published by Elsevier Ltd. Note: This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.