{"id":1535,"date":"2026-06-14T03:18:35","date_gmt":"2026-06-13T19:18:35","guid":{"rendered":"https:\/\/aifed.now\/conference\/?page_id=1535"},"modified":"2026-06-14T03:18:35","modified_gmt":"2026-06-13T19:18:35","slug":"%e4%b8%bb%e9%a1%8c%e5%9b%9b%ef%bc%9a%e8%a9%95%e9%87%8f%e5%9b%9e%e9%a5%8b-%e9%ab%98%e7%8f%a0%e5%ae%b9-ai%e9%8c%af%e9%a1%8c%e8%a8%ba%e7%99%82%e5%ae%a4-%e4%bb%a5%e6%95%99%e8%82%b2%e5%9e%8bai","status":"publish","type":"page","link":"https:\/\/aifed.now\/conference\/?page_id=1535","title":{"rendered":"\u4e3b\u984c\u56db\uff1a\u8a55\u91cf\u56de\u994b \/ \u9ad8\u73e0\u5bb9 \/ AI\u932f\u984c\u8a3a\u7642\u5ba4\u2014\u4ee5\u6559\u80b2\u578bAI\u4fc3\u9032\u570b\u5c0f\u9ad8\u5e74\u7d1a\u793e\u6703\u9818\u57df\u500b\u4eba\u5316\u5b78\u7fd2\u8207\u5dee\u7570\u5316\u88dc\u6551"},"content":{"rendered":"<\/p>\n

AI\u932f\u984c\u8a3a\u7642\u5ba4\u2014\u4ee5\u6559\u80b2\u578bAI\u4fc3\u9032\u570b\u5c0f\u9ad8\u5e74\u7d1a\u793e\u6703\u9818\u57df\u500b\u4eba\u5316\u5b78\u7fd2\u8207\u5dee\u7570\u5316\u88dc\u6551<\/h3>\n<\/p>\n
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\"\u9ad8\u73e0\u5bb9\"<\/p>\n

\u8b1b\u8005\/Speaker\uff1a<\/strong> \u9ad8\u73e0\u5bb9, Kao Chu Jung <\/span> <\/p>\n<\/div>\n

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\u6574\u9ad4\u800c\u8a00\uff0c\u672c\u6559\u5b78\u8a2d\u8a08\u5c07\u6559\u80b2\u578b AI \u5f9e\u300c\u751f\u6210\u7b54\u6848\u300d\u8f49\u5316\u70ba\u300c\u751f\u6210\u601d\u8003\u6b77\u7a0b\u300d\u7684\u8f14\u52a9\u5de5\u5177\uff0c\u900f\u904e\u6559\u5e2b\u5f15\u5c0e\u3001\u5b78\u7fd2\u8b49\u64da\u8207\u932f\u56e0\u8a3a\u7642\u6d41\u7a0b\uff0c\u4fc3\u9032\u5b78\u751f\u6982\u5ff5\u7406\u89e3\u3001\u8868\u9054\u80fd\u529b\u8207\u500b\u4eba\u5316\u88dc\u6551\u5b78\u7fd2\u3002\u6b64\u6a21\u5f0f\u5177\u5099\u570b\u5c0f\u6559\u80b2\u73fe\u5834\u7684\u53ef\u884c\u6027\uff0c\u4ea6\u53ef\u9077\u79fb\u81f3\u570b\u8a9e\u3001\u81ea\u7136\u3001\u6578\u5b78\u7b49\u4e0d\u540c\u9818\u57df\uff0c\u4f5c\u70ba\u63a8\u52d5 AI \u7d20\u990a\u8207\u5dee\u7570\u5316\u6559\u5b78\u7684\u5be6\u52d9\u53c3\u8003\u3002<\/p>\n

\u00a0<\/p>\n\n

This teaching application, titled \u201cAI Error Diagnosis Clinic: Using Educational AI to Promote Personalized Learning and Differentiated Remediation in Upper Elementary Social Studies,\u201d addresses common learning challenges among upper elementary students. These include insufficient conceptual understanding, a lack of individualized review prior to assessments, and post-assessment corrections that often become the mere copying of correct answers. The instructional design utilizes e-Du, an educational AI learning tool, as a dialogue-based learning support system. Implemented with fifth- and sixth-grade students, the approach integrates AI into three key learning stages: daily concept clarification, pre-assessment question-based review, and post-assessment error diagnosis. Through Socratic questioning, students are guided through a learning cycle of identifying error causes, clarifying concepts, revising explanations, and practicing with similar questions.<\/p>\n

This design emphasizes that AI should not serve as a direct answer provider, but rather as a learning partner that scaffolds students\u2019 thinking, restatement, and verification. Guided by teacher-designed questioning frameworks and required learning evidence, students first identify their points of confusion, engage in dialogue with e-Du, and finally reorganize concepts in their own words before completing similar practice tasks. This structured process helps reduce the risk of students directly copying AI-generated responses. Quantitative results indicated that the class using e-Du for regular learning support demonstrated a slight improvement in Social Studies assessment scores. In contrast, the class that additionally implemented pre-assessment review and post-assessment error diagnosis showed more significant score gains, particularly among lower-performing students, highlighting the potential of this approach for differentiated remediation. Furthermore, student feedback revealed that most students recognized e-Du\u2019s usefulness in supporting understanding and problem-solving; they also became aware of the risks of overreliance on AI and gradually developed a more critical and self-regulated attitude toward its use.<\/p>\n

Overall, this teaching design transforms educational AI from a tool for generating answers into a tool for fostering thinking processes. Through teacher guidance, learning evidence, and structured error diagnosis, it promotes students\u2019 conceptual understanding, expressive ability, and personalized remedial learning. This practical model is adaptable to elementary school settings and other subject areas, such as Mandarin, Science, and Mathematics, serving as a feasible reference for promoting AI literacy and differentiated instruction.<\/p>\n

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