12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
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Research theme

AI Literacy Training

The AI Literacy Training theme comprises 1,092 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 Methods174 (16%)
  • Survey169 (15%)
  • Literature Review138 (13%)
  • Conceptual119 (11%)
  • Qualitative109 (10%)
  • Case Study107 (10%)

Research domains

  • Doctoral Training697 (64%)
  • AI Governance173 (16%)
  • Human-AI Collaboration91 (8%)
  • Academic Writing34 (3%)
  • Scholarly Infrastructure8 (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 the need for empirical evidence on AI's educational value in computing curricula and the pedagogical integration of universal digital accessibility. They note the research provide
  • The abstract does not explicitly identify future research gaps or directions beyond the recommendations for professional development frameworks.
  • The authors identify the need for empirical validation of the proposed LLM competency framework before implementation, and suggest that actual adoption behavior, clinical competence, and ethical appli
  • The authors identify that "little is known about how future teachers experience such tools across tasks," suggesting a gap in understanding how teacher education students perceive and interact with ge
  • The authors identify several future research directions: (1) Quantitatively testing the key drivers that shape students' usage of ChatGPT for learning; (2) Examining long-term consequences, both posit

Representative papers

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