12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

AI Literacy Training

The AI Literacy Training theme comprises 1,126 papers in this corpus published between 2002 and 2026. Work here is dominated by Mixed Methods, Survey, Literature Review. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Mixed Methods176 (16%)
  • Survey169 (15%)
  • Literature Review143 (13%)
  • Conceptual131 (12%)
  • Case Study114 (10%)
  • Qualitative113 (10%)

Research domains

  • Doctoral Training719 (64%)
  • AI Governance177 (16%)
  • Human-AI Collaboration95 (8%)
  • Academic Writing35 (3%)
  • Scholarly Infrastructure9 (1%)
  • Research Productivity4 (0%)

Frequent sub-topics

educational innovation and human-AI collaboration learning design · 1LLM workforce skills and preparedness in public health · 1experiential learning with visual AI tools, business student AI education · 1student GenAI use patterns and mindfulness · 1generative AI literacy development in higher education · 1student motivation and psychological need satisfaction with AI learning partners · 1student experiences with generative AI in higher education, ethical awareness · 1Iterative prompt optimization for human-AI alignment in generative AI learning systems · 1

Open research gaps

  • The authors identify that empirical evidence regarding faculty readiness and adoption patterns remains scarce within the Middle Eastern context, and suggest future research should examine how to bridg
  • The authors identify the need for empirical validation studies comparing learning outcomes and academic integrity metrics between the proposed intentionally under-specified task approach and conventio
  • The abstract does not explicitly identify future research gaps or directions beyond the recommendations for professional development frameworks.
  • The authors identify that 'little is known about students who deliberately choose not to use such tools, even when permitted,' and suggest future research should examine how institutional policies can
  • Need for methodological diversity in future research

Representative papers

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