人类还是人工智能？教学助教与机器人的设计思维评估比较
Human or AI? Comparing Design Thinking Assessments by Teaching Assistants and Bots
AI总结：
本研究比较了教学助教与AI在评估设计思维教育学生海报中的表现，发现教师更偏好助教评分，但AI在一致性与反馈效率上具有一定优势。
摘要：
随着设计思维教育在中等和高等教育中的发展，教育者面临评估结合视觉和文本元素的创意作品的挑战。传统的基于评分表的评估方法繁琐、耗时且不一致，因为依赖于在大型多班次群体中的教学助教（TA）。本文提出了一项探索性研究，调查人工智能辅助评估与教学助教辅助评估在评估设计思维教育学生海报方面的可靠性和感知准确性。与33名新加坡教育部（MOE）学校教师进行了两项活动：（1）比较AI生成的评分与TA评分在三个关键维度上的差异：同理心和用户理解、识别痛点和机会、以及视觉沟通；（2）检查教师对AI分配、TA分配和混合评分的偏好。结果显示，对于同理心和痛点，教师和AI评分之间统计上的一致性较低，而视觉沟通方面略有更高的一致性。教师在十个样本中的六个样本中偏好TA分配的评分。定性反馈突显了AI在形成性反馈、一致性和学生自我反思方面的潜力，但也提出了关于其在捕捉上下文细微差别和创造力洞察方面的局限性的担忧。本研究强调了需要整合计算效率与人类洞察力的混合评估模型的必要性。这项研究为关于在创意学科中负责任地采用人工智能的讨论做出了贡献，强调了在可扩展和教学上合理的评估中，自动化与人类判断之间的平衡。
英文摘要：
As design thinking education grows in secondary and tertiary contexts, educators face the challenge of evaluating creative artefacts that combine visual and textual elements. Traditional rubric-based assessment is laborious, time-consuming, and inconsistent due to reliance on Teaching Assistants (TA) in large, multi-section cohorts. This paper presents an exploratory study investigating the reliability and perceived accuracy of AI-assisted assessment compared to TA-assisted assessment in evaluating student posters in design thinking education. Two activities were conducted with 33 Ministry of Education (MOE) Singapore school teachers to (1) compare AI-generated scores with TA grading across three key dimensions: empathy and user understanding, identification of pain points and opportunities, and visual communication, and (2) examine teacher preferences for AI-assigned, TA-assigned, and hybrid scores. Results showed low statistical agreement between instructor and AI scores for empathy and pain points, with slightly higher alignment for visual communication. Teachers preferred TA-assigned scores in six of ten samples. Qualitative feedback highlighted the potential of AI for formative feedback, consistency, and student self-reflection, but raised concerns about its limitations in capturing contextual nuance and creative insight. The study underscores the need for hybrid assessment models that integrate computational efficiency with human insights. This research contributes to the evolving conversation on responsible AI adoption in creative disciplines, emphasizing the balance between automation and human judgment for scalable and pedagogically sound assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
