The Power of Starting Where We Are: A Montessori 6–12 Curriculum for the Age of AI — Resources and References (Montessori Aotearoa New Zealand Webinar, September 23, 2026)
Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 3 references
Education Methods and Practices
Abstract
Resources and references accompanying the webinar The Power of Starting Where We Are: A Montessori 6–12 Curriculum for the Age of AI, presented for the 6–12 teachers of Montessori Aotearoa New Zealand on September 23, 2026. The document gathers every resource named in the session: A Cosmic Story: The Coming of Technology (Teacher's Edition and Read Aloud Edition, free on Zenodo), the Class Posters for each environment from 0–3 through 12–18 together with the 0–18 classical vocabulary page, the Padlet of screen-free practical life materials, the two-part vocabulary card sets, the AI-agent creation guide with its child-rights filter, the student Environmental Impact Tracker, and the interactive dashboard of the mapping research, alongside references for Aotearoa New Zealand, including the Ministry of Education's Generative AI guidance for schools, NZQA's Guidance on the acceptable use of artificial intelligence, and the Netsafe Kit, and the full international reference list from the session.
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...
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
This paper proposes adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint.
Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick et al.· Neural Information Processin...· 70 citations· ⚡5
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
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