Data · dataset · 2024
Navigating Ethical Dilemmas in AI-Enhanced Language Education: Addressing Bias and Ensuring Inclusivity
Listed in Teesside University Research Data Repository
Purpose: This study investigates the ethical, practical, and educational implications of integrating artificial intelligence (AI) into language education.
Description
The focus is on identifying the challenges of algorithmic bias, data privacy, inclusivity, and the impact on teacher-student dynamics, aiming to provide insights that can guide the responsible and effective use of AI in education. Findings: The research reveals that algorithmic bias poses a significant threat to educational equity, potentially perpetuating existing inequalities.
Data privacy concerns are prevalent, necessitating strict regulatory measures. Inclusivity remains a challenge, with AI risks widening the educational gap between socio-economic groups. Additionally, the study highlights the potential erosion of the teacher-student relationship due to over-reliance on AI, which could undermine the human elements crucial to the learning process.
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Design/Methodology/Approach: The study employs a qualitative research methodology, utilizing thematic analysis to explore literature, case studies, and expert interviews. This approach allows for an in-depth understanding of the multifaceted issues surrounding AI in language education and provides a nuanced perspective on its implications. Originality/Value: This research contributes to the emerging discourse on AI in education by addressing the often-overlooked ethical and social dimensions.
It offers a unique perspective by examining the intersection of AI with cultural and socio-economic factors, providing valuable insights for educators, policymakers, and AI developers.
Links
Where it is published
- DOI doi.org/10.17632/9bbwcytc7j.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/9bbwcytc7j.1 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |