This research project investigates the impact of Prompt Engineering, a key aspect of ChatGPT, on college students' information retrieval in flipped classrooms. In recent years, an increasing number of students have been using AI-based tools such as ChatGPT rather than traditional research engines to learn and to complete course assignments. Despite this growing trend, previous research has largely overlooked the influence of prompt engineering on students' use of ChatGPT and effective strategies for improving the quality of information retrieval in learning settings. To address this research gap, this study examines the information quality obtained from ChatGPT in a flipped classroom by evaluating its effectiveness in task completion among 26 novice undergraduates from the same major and cohort. The experimental results provide evidence that proficient mastery of prompt engineering improves the quality of information obtained by students using ChatGPT. Consequently, by acquiring proficiency in prompt engineering, students can maximize the positive impact of ChatGPT, obtain high-quality information, and enhance their learning efficiency in flipped classrooms.

Diagnosing aesthetic perception plays a crucial role in deepening our understanding of student creativity, emotional expression, and the pursuit of lifelong learning within art education. This task encompasses the evaluation and analysis of students' sensitivity, preference, and capacity to perceive and appreciate beauty across different sensory domains. Currently, this assessment frequently relies on subjective evaluations of student artworks. There are two limitations: 1) the diagnosis is possibly limited by instructors' bias; and 2) the heavy workload of instructors for conducting comprehensive assessments. These limitations motivate us to ask: Can we automatically and objectively conduct aesthetic perception diagnosis? To this end, we propose an innovative deep hybrid framework, AestheNet, to automatically evaluate aesthetic perception by analyzing numerous collected student paitings. More specially, we first utilize Convolutional Neural Networks (CNNs) to extract the significant features from the student artworks. Then, we employ the Transformer model to capture the intricate relationships among multiple aesthetic perception dimensions for objective diagnosis. Finally, we validate the effectiveness of the framework by creating a new dataset consisting of 2,153 paintings drawn by 675 students. These paintings are annotated by human experts from 77 dimensions based on domain expertise. Extensive experiments have shown the effectiveness of AestheNet in aesthetic perception diagnosis. AestheNet is dedicated to to overcoming the subjectivity inherent in traditional assessment methods, providing a new, quantifiable, and standardized way to evaluate aesthetic perception. This research not only opens up new perspectives in understanding students' aesthetic development during the art education process but also explores the innovation of using artificial intelligence (AI) technologies in the assessment of art education.

With the rise of new 3D representations like NeRF and 3D Gaussian splatting, creating realistic 3D scenes is easier than ever before. However, the incompatibility of these 3D representations with existing editing software has also introduced unprecedented challenges to 3D editing tasks. Although recent advances in text-to-image generative models have made some progress in 3D editing, these methods either lack precision or require users to manually specify the editing areas in 3D space, complicating the editing process. To overcome these issues, we propose Edit3D, an innovative 3D editing method designed to enhance editing quality. Specifically, we propose a multi-turn editing framework and introduce an attention-driven open-set segmentation (ADSS) technique within this framework. ADSS allows for more precise segmentation of parts, which enhances the editing precision and minimizes interference with pixels in areas that are not being edited. Additionally, we propose a fine-tuning phase, intended to further improve the overall editing quality without compromising the training efficiency. Experiments demonstrate that Edit3D effectively adjusts 3D scenes based on textual instructions. Through continuous and multiple turns of editing, it achieves more intricate combinations, enhancing the diversity of 3D editing effects. Code is available at https://github.com/PeterouZh/Edit3D.

This paper focuses on the clique problem of a weighted graph where weights of vertices can be either positive or negative (WG-PN). Two objectives, the size and the total weight of a clique, are considered. In particular, the larger size in terms of set inclusion and the higher total weight a clique is, the better it is. This problem is termed BiOWCP which is a multi-objective combinatorial optimization problem that contains conflict objectives. We show that BiOWCP cannot be translated into the clique problem of a weighted graph with positive vertex weights (WG-P) via a straight forward transformation, which highlights the difficulty of BiOWCP. We propose a heavy perturbation (HP)-based NSGAII (nondominated sorting genetic algorithm II) algorithm HP-NSGAII for the problem, where the perturbation is done by either improving a selected elitist with a local search procedure or swapping its left and right parts. Benchmarks for BiOWCP were adapted from the DIMACS set. Extensive experiments were carried out for different ratios of the heavy perturbation effort to the total search effort. Computational results show that HP-NSGAII is superior to NSGAII on many problems with respect to the hyper volume indicator and the set coverage indicator.