四六级、考研英语同源外刊精读《高等教育内部参考》五大人工智能认知误区

     We are ready for an exciting set of conversations about AI and higher education. We want to talk with faculty⁽¹⁾ about what Ethan Mollick’s idea of “co-intelligence” means in practice in the classroom. We need to be engaging the employers of our graduates to redesign the curriculum⁽²⁾. We are even ready for difficult conversations like what happens when a professor wants a class to use AI but some students have ethical⁽³⁾objections⁽⁴⁾.

      我们已准备好围绕人工智能与高等教育展开一系列富有价值的探讨。我们计划与高校教职人员探讨伊桑・莫利克提出的 “协同智能” 理念在课堂教学中的实际应用；同时联动毕业生用人单位，共同推进课程体系重构；我们也愿意直面各类棘手议题，例如：若教师要求课堂使用人工智能工具，而部分学生出于伦理考量表示反对，该如何妥善处理。

     These conversations are happening among the influencer class of AI writers and thinkers, but not as much in the day-to-day work with faculty. This past year, we’ve realized there are a set of conversations and interventions⁽⁵⁾ we have to move beyond if we want to get to the good stuff.

       这类深度探讨仅局限于人工智能领域的网红博主与学术研究者群体，并未广泛融入高校教职人员的日常工作。过去一年我们意识到，若想推进高等教育高质量发展，就必须跳出固有讨论框架，摒弃低效的干预方式。

       The artificial intelligence era in higher education seems too new for it to be overrun⁽⁶⁾ with myths and outdated⁽⁷⁾ advice, but here we are. Popular blogs highlight archaic tricks for detecting AI writing, and teaching and learning websites recommend tactics⁽⁸⁾ that have not worked for more than a year. Some of this is to be forgiven—we have never seen a technology move this fast, so adaptation is ongoing rather than a one-off event.

        高等教育领域的人工智能时代尚处初期，本不该充斥着各类认知误区与陈旧建议，但现实已然如此。热门博客仍在宣扬落后的 AI 文字检测手段，教学平台推荐的教学策略也早已失效一年以上。这类现象情有可原：人工智能技术的迭代速度前所未有，因此社会与教育界的适配调整是持续过程，而非一次性的短期行动。

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