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Permanent record · RIR–3018

Mapping Future Educational Landscapes Through Causal Layered Analysis of Contemporary Fiction Narratives

Causal layered analysis of fiction provides a unique lens for uncovering deep-seated assumptions about the future of education. Future research could expand this analysis to include diverse cultural perspectives on AI-driven learning.

Open to researchQualified 80/100P4 provenance
Primary research question

How do underlying myths and metaphors in education fiction shape current perceptions of AI in learning?

Knowledge gap

What remains worth asking

The source suggests that while fiction analysis is useful, it remains useful to test how these narrative layers correlate with actual educational policy shifts.

Potential contribution

Why it may matter

This research provides a deeper understanding of the cultural drivers influencing educational technology adoption.

Academic placement

OECD fields and topic tags

Futures StudiesEducation PolicyCultural Studies

Scope: Educational policy and cultural discourse on technology. · Method signals: Causal Layered Analysis, Content analysis, Narrative inquiry

Possible study pathways

One question, different levels

Research master’s

Educational futures and narrative analysis

Doctoral

Cultural studies of AI in education

originalityModerate
methodologyModerate
Data accessAccessible
ethicsAccessible

Qualification signal

80/100

  • Focus on the intersection of narrative and policy.
  • Open-access scholarly source and DOI metadata verified

Provenance

Research Idea Registry curation

  • DOI and bibliographic metadata independently resolved
  • Open-access status verified
  • The research direction is transparently marked as AI-inferred
The public contributor code contains no name or account email.

APA 7 source

Gidiotis, I. (2026). Speculative futures of artificial intelligence in education: A causal layered analysis of education fiction. Futures, 176, 103762. https://doi.org/10.1016/j.futures.2026.103762

Paper abstract and discussion context; AI-inferred direction

Open source ↗