Challenges related to the rapid development of AI-related policies and the need for longitudinal studies to examine changes over time

The gap

Evidence profile

Sourced from the stated challenges and conclusions of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 4 journals. Those papers have been cited 15 times in total.

Supporting evidence — 4 representative gaps

• Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship (2026) · AI & Society · doi

  The paper identifies the challenge of developing a trustworthy AI framework that addresses research gaps and language bias. The study highlights the challenge of integrating Chinese-language scholarship into existing literature reviews on trustworthy AI and human-AI trust. The paper notes the challenge of developing a meta-level, systematic comparison that captures both the shared structural features and the distinctive challenges and governance responses across domains.

  generalstated challenges
  Keywords: paper identifies challenge developing trustworthy framework addresses research
• Artificial Intelligence in Higher Education—Ethical Policies Mapping (2026) · Education Sciences · doi

  The study identifies challenges related to the rapid development of AI-related policies and the need for longitudinal studies to examine changes over time. The analysis reveals challenges in ensuring the consistent and explicit formalization of ethical AI governance across institutions. The study highlights challenges related to the use of generative AI tools, including the potential erosion of critical thinking and creativity.

  generalstated challenges
  Keywords: study identifies challenges related rapid development ai-related policies
• Generative AI in Accounting and Auditing: A Literature Review and Future Agenda (2026) · Advances in Economics, Management and Political Sciences · doi

  Future research should focus on: verifying the reliability of artificial intelligence through empirical case studies, carrying out interdisciplinary talent training to strengthen human- computer collaboration, establishing a unified evaluation framework to regulate artificial intelligence behavior, and integrating adaptive governance of ethics and compliance from the beginning of design. Framework; Third, the governance standards put forward by the artificial intelligence audit are insufficient.

  generalconclusionsevidence 5/5
  Keywords: artificial intelligence framework governance future focus verifying reliability empirical case carrying interdisciplinary talent training strengthen
• Cervical Cancer Screening in the HPV-Vaccinated and Digital Era: Reassessing Strategies in Light of Artificial Intelligence and Evolving Risk (2025) · Cancers · cited 15× · doi

  The paper identifies challenges related to algorithmic bias, generalizability, and regulatory oversight in the use of artificial intelligence in cervical cancer screening. The paper notes that the integration of artificial intelligence in screening requires rigorous regulatory approval, ethical safeguards, and data governance structures. The paper highlights the need for representative training data to avoid reinforcing existing disparities in screening access and outcomes.

  generalstated challengesevidence 5/5
  Keywords: paper identifies challenges related algorithmic bias generalizability regulatory

Questions about this gap