PKU Explorations in AI-Enhanced Teaching
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As artificial intelligence and technology rapidly advance, Peking University has been actively advancing digital innovation in higher education through systematic initiatives. Positioning itself at the forefront of educational digitalization, PKU faculties are leveraging AI and other advanced technologies to develop inquiry-based learning models that integrate intelligent systems into classroom settings while modernizing traditional pedagogies.
This collection showcases research papers documenting Peking University's explorations in AI-enhanced teaching. All reports adhere to academic ethics protocols, including anonymized data processing procedures.
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Item 人工智能助推“城市交通与土地利用”课程教学 AI-Enhanced Teaching of Urban Transportation and Land Use(2025) Zhang, HongmouContext: This study was conducted within an advanced undergraduate course, Urban Transportation and Land Use, at Peking University. The course examines how transport systems and land use influence each other, and how this relationship affects urban development, sustainability, and equity. Given the technical nature of the course—requiring model building, data analysis, and programming—large language models (LLMs) such as ChatGPT were introduced to help students understand complex concepts and technical methods. Aims: The project aimed to examine how AI tools could enhance student learning in this domain, focusing on: (1) which parts of the learning process benefited from LLM support; (2) where the tools are limited; and (3) whether the use of AI creates confusion or distractions. Methods: In the Fall 2023 semester, LLMs were integrated into two major homework assignments on transport modelling and land use analysis. Students were asked to use tools like ChatGPT or Baidu Ernie Bot, and reflected on the process, including prompt design, comparisons between AI and manual outputs, and classroom discussions. Instructors collected and analysed these reflections to evaluate the role of AI tools in learning. Findings: Students generally found LLMs useful for understanding concepts, exploring modeling methods, and especially for coding and debugging. In the homeworks, AI tools improved efficiency in handling repetitive data tasks. However, they were less effective with “local” data or facts, and sometimes produced inaccurate or made-up results in tasks like image generation. Implications: The study shows that AI tools can reduce barriers in technically demanding courses and support both theoretical and practical learning. It also suggests that instructors should design assignments to distinguish between tasks suitable for AI support and those requiring independent thinking.Item 利用大语言模型支持不同的学习任务——《信息资源建设》课的实证研究 Supporting different learning tasks with large language models – a field experiment in the course information resource development(2025) Liang, XingkunContext: The undergraduate course "Information Resource Development" at Peking University addresses the development and management of various types of information resources. It faces two persistent teaching challenges: the abstract nature of foundational concepts and the difficulty of simulating practical tasks within limited class time. With the rise of large language models (LLMs) such as ChatGPT, there is growing interest in exploring their role in improving learning engagement and personalisation. Aims: This study explored how LLMs could enhance learning in the course, focusing on two core issues: making abstract content more engaging and supporting students in developing practical skills. A further aim was to examine how the integration of AI tools might contribute to students' AI literacy within the context of professional education. Methods: A randomised controlled experiment was conducted with 37 students across four tasks reflecting different learning goals: factual knowledge, theoretical understanding, causal reasoning, and critical thinking. Students were divided into an AI-supported group using the LLM ‘Wenxin Yiyan (Ernie Bot)’ and a control group using traditional resources. Learning outcomes were assessed using t-tests and regression analysis. Post-experiment interviews explored students’ strategies and experiences. Findings: Students using the LLM performed significantly worse on tasks requiring factual accuracy and critical thinking. AI often generated inaccurate data and produced repetitive viewpoints. However, no significant differences were observed in tasks involving theory or causal reasoning, where LLMs offered quick overviews and illustrative examples. Interview data reflected overall cautious attitudes toward AI, with students noting both potential and limitations. Implications: The findings suggest that current LLMs may be helpful for introductory exploration of theoretical content but less effective for tasks requiring precision or original thought. Teachers might learn that thoughtful integration of AI tools depends on task type and critical guidance.Item 人工智能与新技术在《计算机建筑制图》课程中的应用与实践 Applications of artificial intelligence and new technologies in a computer-aided architectural drawing course(2025) Zhang, JianweiContext: The study explores the integration of artificial intelligence (AI) and emerging technologies in the Computer-Aided Architectural Drawing course for undergraduate students specializing in architectural heritage at the School of Archaeology and Museology. Traditional methods, focused on software tools, have become outdated due to rapid technological advancements. The course reform addresses the need for updated teaching methods and content to better align with modern industry demands, particularly in heritage preservation and architectural archaeology. Aims: The primary aim is to enhance the teaching content and methods by incorporating AI and technologies such as VR (Virtual Reality), 3D printing, panoramic photography, and visualization tools. Specific goals include improving teaching efficiency, increasing student engagement, and connecting course content to practical applications in real-world projects, such as heritage protection and architectural design. Methods: The revised course design adds new components alongside traditional software training. Key technologies include Twinmotion for architectural visualization, VR for immersive learning, photography-based 3D modelling, panoramic photography, and Stable Diffusion for AI-generated graphics. Teaching combines lectures, hands-on practice, VR lab sessions, and practical applications like 3D printing. Hardware and software upgrades ensured compatibility with the new tools. Findings: The implementation showed significant improvements in teaching outcomes. Students developed practical skills in architectural visualization, 3D modelling, and AI-assisted drawing, applying their knowledge to real-life projects such as campus heritage preservation. Engagement levels increased, with some students investing in personal hardware for independent learning. The cross-application of technologies, such as VR and 3D printing, enriched students' understanding and interest in modern tools, fostering proactive exploration of new technologies. Implications: The study demonstrates that integrating AI and emerging technologies can transform technical courses, enhancing student engagement and aligning learning with practical applications. Teachers in similar fields might find value in incorporating interactive tools like VR and AI to create immersive and future-ready learning environments.