The Underline

Monday, October 12

  1. EdArXiv· Prajakt Pande

    Rethinking “In-The-Loop” Approaches in Educational AI: A Learning Sciences Perspective

    In educational AI, “In-the-Loop” framings (e.g., human-in-the-loop) are presented as systems that secure human-technology balance in enabling timely feedback and personalization. This learning sciences perspective-piece argues that the critical issue is not whether humans participate sufficiently in educational…

    osf.io
  2. EdArXiv· Le Truong Tung

    THE AI-NATIVE UNIVERSITY - THE ANU 1+4 PRINCIPLES FOR HUMAN FORMATION IN THE AGE OF AI

    Artificial intelligence (AI) makes it increasingly possible to produce sophisticated intellectual outputs without undertaking the experiences that develop corresponding human capabilities. This weakens a practical assumption of traditional university teaching and assessment: that producing an academic output normally…

    osf.io
  3. EdArXiv· Agelo Thalassinidis

    Where the Talent Goes: Field, Credential, and the Geography of Graduate Retention

    States invest heavily in producing technical graduates, yet the graduates most valued by an innovation economy are the ones least likely to stay; this study tests whether that pattern holds across credential levels, using an independent source as corroboration. Retention loss is measured by field and credential using…

    osf.io

Wednesday, October 7

  1. EdArXiv· Lu Wang et al.

    Model escalation tracks the budget per remaining task: Evidence from a shared-budget undergraduate coding assessment

    Some courses limit how much generative-AI help students can obtain in assessed work, expecting scarcity to prompt careful use. A budget shared across tasks also requires students to hold resources back for later needs, and how this shapes their decisions is unclear. In a graded coding-repair task, 125 undergraduates…

    osf.io
  2. EdArXiv· Bo Yuan and Jiazi Hu

    Bridging MOOCs, Smart Teaching, and AI-Assisted Learning: A Unified Quantitative Model

    MOOCs, Smart Teaching (ST), and AI-assisted learning support different stages of higher education, yet a unified quantitative basis for analyzing their contributions, course design, and resource allocation remains limited. This study develops a mathematical model integrating MOOC-based pre-class learning, ST-based…

    osf.io

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