2026/06/23 by Faheem Ullah, Babar Shah, Ahmed Saeed Alshehhi +2 · 1 voice
Computer Science · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI)
paper · pdf · doi:10.1609/aaaiss.v9i1.42913
openalex publication_date 2026/06/23 · openalex created_date 2026/06/24 · openalex updated_date 2026/07/14
Artificial intelligence (AI) is being increasingly integrated in academic research with notable improvement in efficiency while simultaneously raising ethical concerns such as bias, transparency, accountability, and integrity. While current studies examine these issues in isolation, there is a lack of unified research connecting everyday research practices, university policies, and AI‑ethics tools. To fill this gap, we aim to synthesize ethical challenges of AI-assisted research from scientific, institutional, and technical perspectives. By conducting a systematic literature review of 16 peer-reviewed studies using predefined selection criteria to identify trends, themes, and research gaps, it was found that publications on AI ethics have grown rapidly since 2023, largely linked to generative AI with key concerns clustered around bias, explainability, accountability, and data privacy. In contrast, research on higher education policy and practical ethics tools remains limited. The adoption of responsible AI in research will require stronger institutional policies, cross-disciplinary frameworks, and the creation of accessible and practical ethics tools.