This qualitative multiple case study follows two EAP teachers: one novice and one experienced over 24 months to explore how they enacted professional agency during significant educational reforms at a Sino-British university in China. The study is situated within a context of “flux and transformation” , a period marked by the cancellation of Year 2 EAP courses and the rapid integration of Generative Artificial Intelligence (GenAI). The study traced the co-evolution of emotions, identity negotiations, and agentive actions over 24 months (2023–2025) using critical reflective narratives, semi-structured interviews, and informal communication logs. The findings reveal that agency is jointly mediated by emotion and identity. The experienced teacher demonstrated adaptive agency, reframing anxiety as a catalyst for professional growth and constructively integrating the teacherresearcher identity. In contrast, the novice teacher exhibited strategic compliance coupled with internal resistance, as negative emotional turbulence and identity fragmentation blocked meaningful identity negotiation. The study proposes the “Emotion-Identity-Agency Nexus in Flux,” a non-linear model illustrating how macro-political forces (e.g. neoliberal Key Performance Indexes (KPIs), curriculum cuts, GenAI disruption) are filtered through teachers’ emotional and identity resources, shaping whether agency manifests as adaptation, compliance, or resistance. Our findings point to the need for emotional scaffolding for novice teachers, differentiated performance evaluation policies, and a reconceptualization of the teacher-researcher role in EMI contexts.
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 of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
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
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
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6