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Our paper compares the correctness, efficiency, and maintainability of human-generated and AI-generated program code. For that, we analyzed the computational resources of AI- and human-generated program code using metrics such as time and space complexity as well as runtime and memory usage. Additionally, we evaluated the maintainability using metrics such as lines of code, cyclomatic complexity, Halstead complexity and maintainability index. For our experiments, we had generative AIs produce program code in Java, Python, and C++ that solves problems defined on the competition coding website leetcode.com. We selected six LeetCode problems of varying difficulty, resulting in 18 program codes generated by each generative AI. GitHub Copilot, powered by Codex (GPT-3.0), performed best, solving 9 of the 18 problems (50.0%), whereas CodeWhisperer did not solve a single problem. BingAI Chat (GPT-4.0) generated correct program code for seven problems (38.9%), ChatGPT (GPT-3.5) and Code Llama (Llama 2) for four problems (22.2%) and StarCoder and InstructCodeT5+ for only one problem (5.6%). Surprisingly, although ChatGPT generated only four correct program codes, it was the only generative AI capable of providing a correct solution to a coding problem of difficulty level hard. In summary, 26 AI-generated codes (20.6%) solve the respective problem. For 11 AI-generated incorrect codes (8.7%), only minimal modifications to the program code are necessary to solve the problem, which results in time savings between 8.9% and even 71.3% in comparison to programming the program code from scratch.

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments.

The establishment of ship trajectory prediction is critical in analyzing trajectory data. It serves as a critical reference point for identifying abnormal behavior and potential collision risks for ships. Accurate and real-time ship trajectory prediction is essential during navigation. Since the timing of automatic identification system (AIS) data is irregular, traditional methods usually use time calibration to simulate the data of uniform sequencing before analysis. Inevitably, this increases the chances of error and time delays. To address this issue, we propose a time-aware LSTM (T-LSTM) single-ship trajectory model combined with the generative adversarial network (GAN) to predict multiple ship trajectories. These analysis methods are capable of directly analyzing AIS data and have demonstrated better performance in both single-ship and multi-ship trajectories. Our experimental results show that the proposed method achieves high accuracy and can meet the practical navigation requirements of ships.

Ship-speed prediction is an emerging research area in marine traffic safety and other related fields, occupying an important position with respect to these areas. At present, the effectiveness of techniques used in in time-series forecasting methods in ship-speed prediction is poor, and there are accumulated errors in long-term forecasting, which is limited in its processing of ship-speed information combined with multi-feature data input. To overcome this difficulty and further optimize the accuracy of ship-speed prediction, this research proposes a new deep learning framework to predict ship speed by combining GANs (Generative Adversarial Networks) and LSTM (Long Short-Term Memory). First, the algorithm takes an LSTM network as the generating network and uses the LSTM to mine the spatiotemporal correlation between nodes. Secondly, the complementary characteristics linked between the generative network and the discriminant network are used to eliminate the cumulative error of a single neural network in the long-term prediction process and improve the prediction accuracy of the network in ship-speed determination. To conclude, the Generator–LSTM model advanced here is used for ship-speed prediction and compared with other models, utilizing identical AIS (automatic identification system) ship-speed information in the same scene. The findings indicate that the model demonstrates high accuracy in the typical error measurement index, which means that the model can reliably better predict the ship speed. The results of the study will assist maritime traffic participants in better taking precautions to prevent collisions and improve maritime traffic safety.

Research Questions- What was the state of accounting information systems (AIS) faculty in accounting programs of US universities and colleges (hereafter, institutions) at the peak of Dotcom? What can the artificial intelligence (AI)-era accounting education learn from its Dotcom experience? Motivation- Accounting education environment during the Dotcom-led innovations and the current AI- and Generative AI (GenAI)-led innovations bears similarities in many respects. While AIS faculty teach AIS courses where students learn information systems (IS) concepts including technology, processes and internal controls in greater detail and depth relative to other accounting courses, our literature review suggests a paucity of research on AIS faculty, especially during the Dotcom-era. AIS faculty is an appropriate proxy for the IS and information technology (IT) skills of accounting graduates' market-ready quality. Therefore, we examine AIS faculty's institutional characteristics during the Dotcom-era and consider implications for the AI-era accounting education to minimize capacity gaps, technology gaps, and resource gaps. Idea- We analyze US accounting programs for AIS faculty's (i) individual features and (ii) association with institutional features. Data- We hand-collect data, from 1998-1999 Hasselback Accounting Faculty Directory (HAFD), which is just before the Dotcom's bust and reflects the culmination of a series of actions taken by accounting programs and accounting education during the Dotcom-era. HAFD, our primary data source, provides faculty and program information in sufficient detail and granularity. Tools- We use count data econometric models corresponding to Poisson and Negative Binomial (NEGBIN) processes, since our response variable (i.e., AIS faculty) and its proxies suggest that they approximate a Poisson probability distribution. Findings- We find that doctoral programs supplying AIS faculty are public institutions and mostly in the southern states. AIS faculty are (i) less in private institutions; (ii) less in professor ranks; (iii) proportionately more with a PhD and certified public accountant (CPA) credentials; and (iv) similar in gender split, vis-à-vis all accounting faculty. AIS faculty associate positively with total accounting faculty size, accreditation and public institutions, and negatively with the presence of a doctoral program in the department. Contribution- We contribute to the existing research stream that examines accounting program quality and faculty background which proxy graduate's market-readiness. At the theoretical and usefulness level, we contribute by using accounting education's Dotcom experience to identify specific implications for the AI-era. At the methodological level, we theorize the count-data econometric features of AIS faculty and consider its five proxies, each with a different theoretical significance to associate with its factors. Significance- We discuss significance of our results by posing questions to stir debate, dialogue and discussion for devising action-based strategies that are sustainable, inclusive and equitable.

This volume is a collection of studies by some of the foremost researchers of French acquisition in the generative framework. It provides a unique perspective on cross-learner comparative research in that each chapter examines the development of one component of the grammar (functional categories) across different contexts in French learners: i.e. first language acquisition, second language acquisition, bilingual first language acquisition and specifically-language impaired acquisition. This permits readers to see how similar issues and morphosyntactic properties can be investigated in a range of various acquisition situations, and in turn, how each context can contribute to our general understanding of how these morphosyntactic properties are acquired in all learners of the same language. This state-of-the-art collection is enhanced by an introductory chapter that provides background on current formal generative theory, as well as a summary and synthesis of the major trends emerging from the individual studies regarding the acquisition of different functional categories across different learner contexts in French.