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Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education
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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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“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
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“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
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“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
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“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]
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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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Metadata
- Authors
- Lameras, P., & Arnab, S.
- Affiliations
- Coventry University (GB)
- Corresponding author
- Lameras, Petros
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2021-12-29
- Volume
- 13
- Issue
- 1
- Pages
- 14
- ISSN
- 2078-2489 (ISSN-L)
- 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
- 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
- Social Sciences › Education › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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