人均不到3元！被AI作弊逼急的教授玩“邪修”：“花105元，给全班36人办了场AI口试”

Core Insights - The article discusses the challenges and innovations in evaluating student performance in the context of AI advancements, particularly focusing on the shift from traditional assessments to AI-driven oral examinations [1][2][3]. Group 1: AI in Education - The traditional method of assessing students through written assignments has become ineffective due to the availability of AI tools that can assist students in completing their work [2][3]. - The introduction of AI-driven oral exams aims to evaluate students' real understanding and reasoning abilities, as it requires them to think on their feet without AI assistance [3][4]. Group 2: Implementation Challenges - Scaling oral exams presents logistical challenges, especially with larger class sizes, making coordination of exam schedules difficult [4][5]. - The use of AI to facilitate oral exams can streamline the process, allowing for personalized questioning and structured workflows [5][6][7]. Group 3: AI Oral Exam Structure - The AI oral exam consists of two main parts: discussing the student's project and analyzing a randomly selected case study, which tests their knowledge retention and application [9][10]. - A structured workflow with multiple AI agents is employed to ensure a smooth examination process, including identity verification and targeted questioning based on project details [11][12]. Group 4: Cost and Efficiency - The implementation of the AI oral exam system resulted in a total cost of $15 for 36 students, significantly lower than the estimated $750 for traditional human-led assessments [13][14]. - The average duration of the oral exams was 25 minutes, with a notable finding that shorter exams did not correlate with lower scores, indicating efficiency in understanding [32]. Group 5: Feedback and Assessment Quality - The AI system provides detailed feedback on students' performance, highlighting strengths and areas for improvement, which is more comprehensive than typical human feedback [29][30]. - The AI scoring system showed a high degree of consistency among different models, with a notable improvement in scoring accuracy after models reviewed each other's assessments [22][24]. Group 6: Student Reception - Student feedback indicated a preference for traditional assessments, with many feeling that AI oral exams increased pressure, yet a majority acknowledged that these exams better assessed their understanding [33][35]. - The article concludes that while the core idea of AI-driven assessments is promising, further refinement of execution details is necessary to enhance the student experience [35][36].