讲座预告：A Turing Test for Academic Evaluation: Detection, Grades, and Student Satisfaction in a Field Experiment

讲座时间：2026年7月14日15：00 - 17：00

讲座地点：节约楼208

摘要：

We study whether AI evaluation can pass a behavioral Turing test in a consequential academic setting. In a semester-long field experiment, undergraduates in two economics courses complete multiple essay assignments, each randomly graded by one of three AI systems or one of three human teaching assistants, with students blinded to grader identity. After receiving their grade and written feedback, students report satisfaction and guess whether their grader was human or AI. We find that students cannot identify their grader type above chance (52.1% accuracy; p = 0.229 using a two-sided binomial test), and detection failure is uninformative: neither the grade received nor any observable feature of the evaluation predicts students’ guesses. A naive comparison shows that human-graded assignments produce higher satisfaction, but this gap decomposes entirely into two channels: the grade received and students’ beliefs about who graded them. Actual grader identity has no independent effect on satisfaction once these channels are controlled, and the grade–satisfaction slope is identical across grader types. These results establish behavioral substitutability under source uncertainty: AI evaluation is not merely undetected—it is functionally equivalent to human evaluation in the eyes of those being evaluated. The apparent satisfaction cost of AI grading reflects grade differences and attribution, not grader identity itself.

主讲人介绍：

王思宁，任职于凯斯西储大学，担任经济学助理教授。研究方向涵盖计算经济学、行为经济学与博弈论。近期研究聚焦教学模式创新，运用人工智能与数字化技术优化教学效果，探究人工智能重塑教育的路径，助力学生在智能时代培养批判性思维与科学决策能力。他依托跨学科研究思路，兼顾经济学理论拓展与教学方法革新，培养适配未来职场与社会发展的复合型人才。