Demystifying AI
The urgency of a critical stance on the use of AI systems in education
Bibliographic Data
| ID | 21373997 |
|---|---|
| Authors | Dagmar Mercedes Heeg (0000-0002-0929-109X, Dialyse Centrum Groningen), Lucy Avraamidou (0000-0001-9693-4438, University of Groningen, corresponding author) |
| Year | 2026 |
| Publication date | 2026-04-08 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Prospects (JOURNAL) |
| Journal identifiers | ISSN: 0033-1538 • E-ISSN: 1573-9090 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11125-026-09760-4 |
| OpenAlex | W7152010859 |
| Language | EN |
| References cited | 46 |
The education field has generally embraced artificial intelligence (AI) systems for their (undocumented) potential to reduce teacher workload, address inequalities, and enhance learning through personalization. However, issues such as algorithmic bias, monoculturalism, discrimination, use of private data, and access to AI systems raise concerns about exacerbating learning inequalities, especially for minority students. We argue that researchers in education have largely failed to critically address the limitations of AI systems, and that a critical stance framed within critical AI literacies is urgently needed to ensure responsible use, if any, of AI systems in the classroom. We first examine how AI systems have been conceptualized for educational purposes. Through engagement with related literature, we discuss what has been overlooked by the mystifying of AI and the failure to engage more thoroughly and critically with existing literature on the social nature of learning. Following that, we provide an overview of contemporary AI literacy frameworks through a comparative approach and discuss their potential and limitations. We conclude with a set of recommendations for future research, teacher education, and curriculum design, framed within a critical stance to foster fair, ethical, and responsible use, where appropriate, of AI systems in education
Critical literacy · Curriculum · Field (mathematics) · Literacy · Set (abstract data type) · Social system · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Teaching and Learning Programming
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| Highly cited | No |
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