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

Responsible AI Use

The Responsible AI Use theme comprises 2,458 papers in this corpus published between 1991 and 2026. Work here is dominated by Conceptual, Literature Review, Qualitative. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Conceptual429 (17%)
  • Literature Review304 (12%)
  • Qualitative291 (12%)
  • Position Paper204 (8%)
  • Experimental203 (8%)
  • Empirical Study193 (8%)

Research domains

  • AI Governance825 (34%)
  • Human-AI Collaboration753 (31%)
  • Research Integrity537 (22%)
  • Doctoral Training91 (4%)
  • Academic Writing71 (3%)
  • Bias & Epistemic Risk57 (2%)

Frequent sub-topics

algorithmic harm and digital social pollution from automated decision-making · 2industry-academic collaboration in context of generative AI · 1AI-assisted amateur research; epistemic authority boundaries; autoethnography · 1AI awareness and adoption in library institutions · 1AI-friendly assignment design for undergraduate education · 1Institutional AI fluency and faculty training · 1genai_acceptance_and_adoption · 1AI Guard-Down Effect and cognitive offloading in cybersecurity and privacy behaviors · 1

Open research gaps

  • The authors identify a gap in understanding how ecotoxicology journals communicate transparency and open science practices through their author guidelines, particularly regarding FAIR and CARE princip
  • The abstract does not explicitly identify specific research gaps or future directions beyond the general applicability statement that the materials "can support work on AI adoption, policy, and method
  • Not explicitly stated in the abstract.
  • The authors identify gaps in understanding GenAI adoption challenges in hospitality and tourism research. They highlight the need for discussion on "how GenAI can promote publishing within the hospita
  • The authors identify gaps in current teacher training programs regarding: (1) training in ethical AI frameworks, (2) security preparedness, (3) algorithmic responsibility, and (4) the need for compreh

Representative papers

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