Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

Lameras, Petros; Arnab, Sylvester

doi:10.3390/info13010014

Cited by 364Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

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Lameras, Petros, Arnab, Sylvester·2021·Information

DOI 10.3390/info13010014

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Abstract

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models.

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The integration of AI into everyday teaching practice remains complex, uneven, and highly context-dependent.
“Despite these anticipated benefits, the integration of AI into everyday teaching practice remains complex, uneven and highly context-dependent (Lameras & Arnab, 2021).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Lameras & Arnab, 2021
The Introduction describes teachers’ decision to use AI as complex and ill-defined. Sections 3.3 and 3.6.2 also say that AI-supported teaching must account for the activity’s context and teachers’ skills. The paper does not establish that integration into everyday teaching practice is uneven. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Teachers' interpretations of AI align with constructivist perspectives that position teachers as facilitators.
“Such interpretations align with constructivist perspectives in which teachers position themselves as facilitators who guide learning rather than merely transmit knowledge (Lameras & Arnab, 2021;Vygotsky, 1978).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Lameras & Arnab, 2021
The findings resonate strongly with constructivist and sociocultural approaches, which conceptualize learning as an interactive and mediated process shaped by tools, social interaction, and professional judgment.
“From a theoretical perspective, the findings resonate strongly with constructivist and sociocultural approaches, which conceptualize learning as an interactive and mediated process shaped by tools, social interaction, and professional judgment (Lameras & Arnab, 2021;Vygotsky, 1978).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Lameras & Arnab, 2021
An instructional design model-either new or old-contributes to scientific knowledge.
“Taking into account the abundance of new technological means and tools (e.g., Spatiotis et al. [17]), such as automated artificial intelligence systems, digital educational games, and virtual reality worlds, an instructional design model-either new or old-contributes to scientific knowledge (e.g., Lameras and Arnab [18]; and Soto [12]).”
From A Comparative Study of the ADDIE Instructional Design Model in Distance Education (2022) · cites this paper as Lameras and Arnab [18]
Section 3.4 discusses pedagogical and learner models as ways to represent knowledge for adaptive teaching, but does not say that an instructional design model—new or old—contributes to scientific knowledge. A search for that claim found no passage in our copy that addresses it. (LLM, conf=0.72)
Experimental — accuracy not yet measured

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“It has even been described as the “Cinderella of the AI story”, alluding to its underdeveloped and often ignored status in education (Lameras & Arnab, 2021).”
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Authors: Lameras, P., & Arnab, S.
Affiliations: Coventry University (GB)
Corresponding author: Lameras, Petros
ORCID: Lameras, Petros
Arnab, Sylvester
Venue: Information
Publisher: MDPI AG
Published: 2021-12-29
Volume: 13
Issue: 1
Pages: 14
ISSN: 2078-2489 (ISSN-L)
DOI: 10.3390/info13010014
OpenAlex: W4200613005
Cited by: 291
References: 105
Citations by year: 2026: 55 · 2025: 95 · 2024: 87 · 2023: 37 · 2022: 17
FWCI: 31.87
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Information (publishedVersion, cc-by)
Pure (Coventry University) (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: British Academy (SRG1920 / 100529)
Topics: Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: artificial intelligence in education, teacher role, teacher reflection, ethical use of AI, student model

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Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

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