The gap between the rapid development of AI in education
Research gap analysis derived from 3 education papers in our local library.
The gap
The gap between the rapid development of AI in education and the insufficient ethical frameworks guiding its use in higher education. The lack of understanding of how faculty members bring principles of transparency, fairness, privacy, and
Evidence profile
Sourced from the limitations and future work and stated research gap of the source papers, classified as general, spanning 3 journals.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Academic Applications of Generative Artificial Intelligence Tools: A Scoping Review (2026) · International Journal for Digital Society · doi
Articles were not appraised for scientific rigor, in accordance with standard scoping review practices. We chose to look at the areas of the articles that specifically addressed advantages, disadvantages, and ethical issues within doctoral programs. By intentionally excluding articles on general AI, we limited our search to articles that specifically use generative AI in doctoral level programs. Due to this perspective, data on higher education programs that touch on ethical issues of AI may have been overlooked. Further research on formulating clear institutional policy guidelines and implementing effective compliance practices is recommended.
generallimitationsevidence 5/5Keywords: articles programs practices specifically ethical issues doctoral appraised scientific rigor accordance standard scoping review chose - Data Quality and the Trust–Interpretability Paradox: Toward a Mid-Range Framework for AI Adoption in Educational Information Systems (2026) · Journal of Information Technology Education Research · doi
The findings suggest that responsible AI adoption in higher education requires a balanced socio-technical approach that combines strong data foundations with attention to ethical and organizational factors that foster trust. Future research should include longitudinal studies, cross-institutional valida- tion, and qualitative investigations to better understand how contextual factors influence AI adoption, trust, and interpretability.
generalfuture workevidence 5/5Keywords: adoption factors trust suggest responsible higher education requires balanced socio technical approach combines strong foundations - Ethically situated AI integration in higher education: A mixed-methods latent profile analysis of faculty adoption (2026) · Education and Information Technologies · doi
The gap between the rapid development of AI in education and the insufficient ethical frameworks guiding its use in higher education. The lack of understanding of how faculty members bring principles of transparency, fairness, privacy, and accountability into teaching decisions. The need for a more integrated approach to examining faculty AI adoption, combining ethical, pedagogical, and institutional perspectives.
generalstated research gapevidence 5/5Keywords: gap between rapid development education insufficient ethical frameworks
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