<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>The Underline · EdArXiv</title><description>Underlining what matters in higher ed and ed tech: news, vendor updates, and commentary from across the field, in one place.</description><link>https://theunderline.netlify.app/source/edarxiv/</link><item><title>Rethinking “In-The-Loop” Approaches in Educational AI: A Learning Sciences Perspective (EdArXiv)</title><link>https://osf.io/preprints/edarxiv/ydzcf_v1/</link><guid isPermaLink="true">https://osf.io/preprints/edarxiv/ydzcf_v1/</guid><description>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…</description><pubDate>Tue, 13 Oct 2026 00:15:43 GMT</pubDate><dc:creator>Prajakt Pande</dc:creator><category>Research</category></item><item><title>THE AI-NATIVE UNIVERSITY - THE ANU 1+4 PRINCIPLES FOR HUMAN FORMATION IN THE AGE OF AI (EdArXiv)</title><link>https://osf.io/preprints/edarxiv/er8dy_v1/</link><guid isPermaLink="true">https://osf.io/preprints/edarxiv/er8dy_v1/</guid><description>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…</description><pubDate>Mon, 12 Oct 2026 19:50:31 GMT</pubDate><dc:creator>Le Truong Tung</dc:creator><category>Research</category></item><item><title>Where the Talent Goes: Field, Credential, and the Geography of Graduate Retention (EdArXiv)</title><link>https://osf.io/preprints/edarxiv/gwfpx_v1/</link><guid isPermaLink="true">https://osf.io/preprints/edarxiv/gwfpx_v1/</guid><description>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…</description><pubDate>Mon, 12 Oct 2026 19:39:03 GMT</pubDate><dc:creator>Agelo Thalassinidis</dc:creator><category>Research</category></item><item><title>Model escalation tracks the budget per remaining task: Evidence from a shared-budget undergraduate coding assessment (EdArXiv)</title><link>https://osf.io/preprints/edarxiv/ve2f8_v1/</link><guid isPermaLink="true">https://osf.io/preprints/edarxiv/ve2f8_v1/</guid><description>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…</description><pubDate>Wed, 07 Oct 2026 20:35:26 GMT</pubDate><dc:creator>Lu Wang et al.</dc:creator><category>Research</category></item><item><title>Bridging MOOCs, Smart Teaching, and AI-Assisted Learning: A Unified Quantitative Model (EdArXiv)</title><link>https://osf.io/preprints/edarxiv/ba69n_v2/</link><guid isPermaLink="true">https://osf.io/preprints/edarxiv/ba69n_v2/</guid><description>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…</description><pubDate>Wed, 07 Oct 2026 19:47:28 GMT</pubDate><dc:creator>Bo Yuan and Jiazi Hu</dc:creator><category>Research</category></item></channel></rss>