Core Viewpoint - The article discusses a recent experiment at Hong Kong University of Science and Technology where an AI-powered glasses equipped with ChatGPT-5.2 took a final exam, achieving a score of 92.5, outperforming over 95% of human students, raising questions about the validity of traditional educational assessment methods [1][4][6]. Group 1: Experiment Overview - The AI glasses, developed by a team led by Professors Zhang Jun and Meng Zili, were designed to "cheat" in a controlled exam environment for the course "Computer Network Principles" [7][8]. - The AI glasses utilized a combination of hardware and software, including a camera for capturing exam questions and a cloud-based model for generating answers [13][12]. - The experiment aimed to evaluate the performance of AI in a traditional academic setting, highlighting the potential challenges to existing educational evaluation systems [5][6]. Group 2: Performance Metrics - The AI glasses scored 92.5, with perfect scores in multiple-choice and single-page short answer questions, and a high score in multi-page short answer questions [14]. - The performance demonstrated strong reasoning capabilities, even in complex questions that required contextual understanding [14][15]. - The experiment revealed that the AI could effectively complete the reading, understanding, and answering process, raising concerns about the relevance of traditional assessment methods [22][23]. Group 3: Implications for Educational Assessment - The success of the AI in the exam challenges the effectiveness of current educational assessments that focus primarily on standardized answers [22][33]. - The article suggests that traditional assessments may not adequately measure critical skills such as problem-solving, creativity, and understanding, which are essential in real-world scenarios [39][43]. - There is a growing need to shift the focus of educational evaluations from merely providing correct answers to assessing the reasoning processes and understanding behind those answers [40][48].

Core Insights - The experiment conducted at Hong Kong University of Science and Technology demonstrated that an AI-powered smart glasses, equipped with the ChatGPT-5.2 model, could successfully complete a final exam in Computer Network Principles, achieving a score of 92.5, placing it in the top 5 among over 100 human students [1][9][12]. Hardware Selection - The research team evaluated 12 mainstream commercial smart glasses, including products from Meta, Xiaomi, and Rokid, to find a suitable device for the experiment [3][4]. - The final choice was Rokid smart glasses due to their superior SDK and development flexibility, which were essential for the experiment's requirements [5][7]. AI Model Selection - The team selected OpenAI's latest model, ChatGPT-5.2, for its strong response speed and general knowledge capabilities, which were crucial for the exam performance [7]. Exam Process - The exam process involved the AI glasses capturing questions via a camera, transmitting them to the cloud for processing, and then displaying the answers back on the glasses for the student to copy [9][12]. - The AI achieved full marks in multiple-choice and single-page short answer questions, and performed well in multi-page short answer questions, showcasing strong reasoning capabilities [9][12]. Technical Limitations - The experiment revealed significant limitations in current commercial AI glasses, particularly regarding power consumption and camera clarity, which directly affected the AI's performance [12][13]. - The glasses' battery dropped from 100% to 58% within 30 minutes under exam conditions, indicating a need for better power management for prolonged use [12]. Educational Implications - The results raised questions about the validity of traditional educational assessment methods, which focus primarily on standardized answers, as AI can excel in these areas [13][14]. - The experiment highlighted the need for a shift in educational evaluation from merely assessing final answers to understanding the reasoning processes and learning paths of students [25][29].