The rapid proliferation of generative artificial intelligence (GenAI) and large language models (LLMs) has fundamentally transformed the landscape of modern higher education, prompting a critical paradigm shift in how academic integrity is conceptualized. This scoping review synthesizes 20 cutting-edge academic publications from 2026 (retrieved via Google Scholar) to examine the evolution of academic integrity from mechanical plagiarism detection and punitive surveillance toward comprehensive institutional, pedagogical, and ethical redesign. The synthesis reveals that the primary systemic catalyst for academic misconduct remains the pervasive “publish or perish” culture and quantitative assessment paradigms that prioritize performance over moral growth. Contemporary scholarship moves beyond identifying individual infractions to address systemic vulnerabilities, student uncertainty, data privacy, and the degradation of higher-order cognitive skills (analyzing, evaluating, creating) resulting from unreflective AI reliance. To mitigate these threats, the review highlights four key integrative strategies: (1) adoption of ecosystemic models (e.g., A. Maleki’s Higher Education Integrity Ecosystem) linking governance, assessment, cultural norms, and individual agency; (2) implementation of process-oriented, authentic assessment frameworks (e.g., S. Lehane, A. Wright and P. Fenton’s Model for Practice and P.D.N. Ncube et al.’s PRISMA-based multi-stage evaluations) that render automated cheating obsolete; (3) operationalization of the AI3 model that integrated AI, academic integrity, and assessment innovation (C. DeLuca, L.Volante and M. Holden), and (4) proactive educational initiatives, AI literacy, and epistemic pluralism to safeguard neurodivergent learning trajectories. Ultimately, the academic community is transitioning from technocratic panic to building ethically resilient, adaptive educational ecosystems aligned with UN Sustainable Development Goals (SDG 4 and SDG 16).
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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