The rapid integration of generative artificial intelligence into higher education has outpaced the development of pedagogical frameworks and institutional policies, raising fundamental questions about the purposes of education in an algorithmic age. Paulo Freire’s critical pedagogy offers a powerful lens for interrogating the epistemological, ethical, and political dimensions of AI-mediated education, yet its application remains fragmented across disciplinary boundaries. This systematic review followed the PRISMA 2020 guidelines and employed a systematic literature search across the Scopus database, supplemented by backward and forward citation tracking. The search strategy combined terms related to Freirean pedagogy and liberating education with terms related to artificial intelligence, yielding a final corpus of 15 peer-reviewed open access journal articles published between 2021 and 2026, within the 2020–2026 eligibility window. Data extraction and thematic synthesis were conducted using the PEO framework, and risk of bias was assessed using appropriate tools. The synthesis identified four thematic clusters: epistemological foundations for critiquing algorithmic neutrality; redesign of assessment and governance practices; intersectional and decolonial dimensions of algorithmic harm; and psychological consequences of cognitive offloading. Key findings reveal that uncritical AI integration reproduces the banking model of education, systematically marginalizes nondominant epistemologies, and risks eroding critical thinking through cognitive debt. A critical framework organized around recognition, voice, and power, operationalized through credibility, comprehensibility, and control, is proposed. Freirean pedagogy retains transformative relevance when translated into design principles, governance mechanisms, and evaluative criteria that challenge algorithmic reductionism and center epistemic justice, relational agency, and collective liberation. The framework offers actionable guidance for equitable assessment redesign, participatory governance, and psychologically sustaining pedagogies in AI-mediated higher education.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
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