Unveiling the shadows: beyond the hype of AI in education
Open accessAbstract
Despite the wave of enthusiasm for the role of Artificial Intelligence (AI) in reshaping education, critical voices urge a more tempered approach. This study investigates the less-discussed 'shadows' of AI implementation in educational settings, focusing on potential negatives that may accompany its integration. Through a multi-phased exploration consisting of content analysis and survey research, the study develops and validates a theoretical model that pinpoints several areas of concern. The initial phase, a systematic literature review, yielded 56 relevant studies from which the model was crafted. The subsequent survey with 260 participants from a Saudi Arabian university aimed to validate the model. Findings confirm concerns about human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethical issues, teacher development, reliability, and the consequences of AI-generated content. They also highlight correlations between various AI-associated concerns, suggesting intertwined consequences rather than isolated issues. For instance, enhancements in AI transparency could simultaneously support teacher professional development and foster better student outcomes. Furthermore, the study acknowledges the transformative potential of AI but cautions against its unexamined adoption in education. It advocates for comprehensive strategies to maintain human connections, ensure data privacy and security, mitigate biases, enhance system transparency, foster creativity, reduce access disparities, emphasize ethics, prepare teachers, ensure system reliability, and regulate AI-generated content. Such strategies underscore the need for holistic policymaking to leverage AI's benefits while safeguarding against its disadvantages.
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AI transparency enhancements could simultaneously support teacher professional development and foster better student outcomes.
“since as Al-Zahrani (2024) points out, enhancements in AI transparency could simultaneously support teacher professional development and foster better student outcomes.”
From Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review (2025) · cites this paper as Al-Zahrani, 2024
The Discussion (page unavailable) says improving AI transparency and explainability could aid teacher professional development. Results Table 7 links transparent AI to confidence in educational outcomes, but the paper does not say transparency enhancements would also improve student outcomes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThese initiatives are rarely anchored in pedagogical principles and commonly overlook critical dimensions such as ethical responsibility, mitigation of algorithmic bias, responsible use of generative models and the risk of over-reliance on automated outputs.
“Additionally, these initiatives are rarely anchored in pedagogical principles and commonly overlook critical dimensions such as ethical responsibility, mitigation of algorithmic bias, responsible use of generative models and the risk of over-reliance on automated outputs [11].”
From Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach (2026) · cites this paper as [11]
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- Authors
- Al-Zahrani, A. M.
- Affiliations
- University of Jeddah (SA)
- Corresponding author
- Al-Zahrani, Abdulrahman M.
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 10
- Issue
- 9
- Pages
- e30696
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC11087970
- OpenAlex
- W4396622564
- Cited by
- 225
- References
- 65
- Citations by year
- 2026: 116 · 2025: 101 · 2024: 8
- FWCI
- 84.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Heliyon (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, AI implementation challenges, data privacy and security, algorithmic bias, AI transparency, critical thinking, equitable access, AI ethics, teacher professional development, AI-generated content, AI reliability
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