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◆ Humanities and Social Sciences Communications2026-09-05· Engineering ethics

Teaching for artificial or human intelligence?: A critical perspective on assessment during the AI era

John Carr, Tania Leimbach, Tema Milstein

原始摘要(英文原文)· Original abstract
Abstract We draw upon our own experience as educators who teach into the transdisciplinary sustainability field of environment and society to start to argue that, paradoxically, to prepare our students for a future in which AI will increasingly disrupt traditional professions, sources of information, and approaches to knowledge, it is incumbent upon educators such as us to ensure learners have the opportunity to develop certain core, critical human abilities. And, in turn, this requires educators to create “AI-proof” assessments that help foster and protect the development of those abilities. Specifically, we argue learner development of what we identify as the “DEEP” capacities – the abilities to Discern, Engage, Evaluate, Produce – is essential to building a resilient, restorative, and fair future. By utilizing AI outputs to fulfill learning assessment requirements, however, students undermine their own development of mastery of the high-level DEEP skills essential to thrive in the Anthropocene. And the risk of students allocating these analytic capacities to AI chatbots – instead of actually developing DEEP skills themselves – is particularly dangerous, as the outputs of generative AI are structurally unreliable and lacking in ethical animus attuned to human and planetary survival and flourishing. Accordingly, in addition to offering a critical perspective, we suggest directions forward for educators to craft AI-proof assessments to foster student development of core DEEP skills in a time of social and environmental urgency. This piece joins a growing call from academics disrupting the uncritical adoption of AI technologies in higher education.
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Teaching for artificial or human intelligence?: A critical perspective on assessment during the AI era — 科研速览 Science Skim