Conference: IEEE ICALT 2025 - 25th International Conference on Advanced Learning Technologies

Location: Changhua University, Taiwan

Abstract:

To deal with programming plagiarism, a number of similarity detectors have been developed. However, many of them are either not user-friendly or require local installation. We present a web-based similarity detector that is user-friendly and requires no local installation. The detector is expanded from our previous work, whose performance has been proven. Unlike existing web-based similarity detectors, ours is not commercialized. Further, it offers several similarity measurements and enhanced privacy. According to a controlled experiment with 24 tutors or staff, our similarity detector is effective and efficient. Participants with the detector can identify all copied submissions in which the completion time is reduced by at least two-thirds.

Co-Author

Conference: ICICL 2024 - 28th International Conference on Interactive Collaborative Learning

Location: Budapest University of Technology and Economics (BME), Hungary

Abstract:

Generative Artificial Intelligence (GenAI) is transforming education, especially engineering education. Despite its benefits, GenAI introduces a new form of plagiarism where students claim GenAI results as their own work. To foster the development of automated detectors for GenAI-based plagiarism, we observe two key characteristics of GenAI generated programs in introductory programming concepts: uniqueness and correctness. Automated detectors can focus on these aspects to iden tify GenAI-based plagiarism. According to our observation of an offering of introductory programming course, we found that GenAI-generated programs were unique (80%) and accurate (81%) for introductory pro gramming assignment tasks. They could be more unique yet less accu rate on advanced or uncommon assignment tasks. GenAI’s high accuracy shows the urgency of GenAI policies, literacy, and the development of automated detectors

Author

Conference: ICSTHE 2024

Location: Krida Wacana Christian University, Jakarta

Abstract:

Urban mobility faces challenges from population growth and private vehicle reliance, leading to congestion and pollution. ParkSphere, a WhatsApp-based chatbot, addresses these issues by providing real-time parking availability and reducing search time, traffic congestion, and emissions. Testing showed high user satisfaction, and future updates aim to enhance interactions with generative AI and add parking reservations.

Author

Journal: JAIC 2024 - Journal of AI Computing

Location: Politeknik Negeri Batam, Indonesia

Abstract:

The research demonstrates that students utilizing ChatGPT for API testing achieved higher test scores than those relying on traditional documentation, albeit with longer task completion times. While participants reported enhanced confidence and effectiveness, concerns regarding over-reliance on the tool and limited conceptual understanding were identified. These findings suggest that ChatGPT is a valuable educational resource complemented by independent problem-solving skills.

Author

Conference: KONSTELASI 2024

Location: Atma Jaya, Yogyakarta

Abstract:

Office management is vital for seamless organizational operations. To tackle the complexities of this role, we've developed a comprehensive super application that integrates various office administration features. Built on the Laravel framework, this super application ensures stability, security, and scalability for seamless integration of complex features. Implementing this application is expected to reduce administrative task time and increase productivity.The design and prototype validation process involved user testing and feedback from stakeholders, resulting in an innovative, stakeholder-validated solution that significantly contributes to office administration management. This solution enhances office administration efficiency and effectiveness, paving the way for future similar applications.

Author

Conference: IEEE TALE 2023 - Teaching and Learning Conference

Location: Auckland, New Zealand

Abstract:

In programming education, plagiarism and misuse of artificial intelligence (AI) assistance are emerging issues. However, not many relevant studies are focused on web programming. We plan to develop automated tools to help instructors identify both misconducts. To fully understand the issues, we conducted a controlled experiment to observe the unfair benefits and the characteristics. We compared student performance in completing web programming tasks independently, with a submission to plagiarize, and with the help of AI assistance (ChatGPT). Our study shows that students who are involved in such misconducts get comparable test marks with less completion time. Plagiarized submissions are similar to the independent ones except in trivial aspects such as color and identifier names. AI-assisted submissions are more complex, making them less readable. Students believe AI assistance could be useful given proper acknowledgment of the use, although they are not convinced with readability and correctness of the solutions.

Co-Author

Journal: Springer Nature - Discover Computing

Role: Co-Author

Abstract:

In engineering education, Generative Artificial Intelligence (GenAI) might be misused to complete assessments with limited understanding. On courses that allow the use of GenAI, students might also forget to acknowledge its assistance. There is a need to identify such assistance. We present an automated detector with over-uniqueness and sample matching. GenAI-assisted submissions are identified based on their uniqueness and their similarity to a GenAI sample. Unique to our GenAI detector, it requires no training data and/or dedicated rules for each programming/scripting language. Further, the method can be integrated into any existing similarity detectors to identify plagiarism. The detector covers five similarity measurements, two similarity modes, and eight programming/scripting languages. Our evaluation of four data sets with thousands of submissions shows that our detector is effective (71% MAP). However, many factors can affect its effectiveness, including submission length and student attempts to align the code. Combining both mechanisms does not result in higher effectiveness, yet it takes longer to process.

Conference: IOCES 2026 - 1st International Online Conference on Education Sciences

Location: Basel, Switzerland (MDPI)

Abstract:

Large Language Models (LLMs) can help students learn effectively. However, its use can introduce several issues, including ethical breaches and environmental harms. While ethical breaches have been extensively addressed, the environmental harms receive little attention. LLMs require substantial computational power, harming the environment. One LLM prompt results in a carbon footprint four times that of a Google search. There are a few studies that focus on informing LLMs’ environmental harms in education. However, many of those are designed as course materials or general guidance. Students might not have time to learn the course materials and may find the guidance limited in relevance to their own context. We present an online platform that bootstraps LLMs, promoting the sustainable use through mindful prompting and gamification. Mindful prompting is encouraged by applying course-relevant pre-prompts, setting minimum prompt length, limiting one prompt per minute, and recommending the reuse of similar prompt responses. The first two aim for effective prompt interactions, while the last two aim to minimise environmental harm. Gamification is applied by reducing the incentive for students using LLMs excessively. For each assessment, students will get 100 game points if their use of LLMs (measured in the total number of words in prompts and responses) is no more than 25% higher than the average. Otherwise, they will have game points deducted based on a proportion of excessive use. The platform is being evaluated in an informatics course (60 students) via a quasi-experiment. Our platform will be evaluated based on students’ awareness of the sustainable use of LLMs, the number of reused prompt responses, the number of words in prompts and responses, and their perception of the platform. Other indirect metrics will also be discussed, including academic performance, prompt analysis and response similarity.

Location: Amsterdam, Netherlands (Elsevier B.V.)

Abstract:

In learning programming, students are often focused on the correctness of the programs. However, other important aspects should be considered, including code ethics, quality, and efficiency. This platform supports students in learning about these aspects through their own submissions. For each submission, a comprehensive report will be provided, showing instructors’ expectations, simulated similarities, obvious similarities, the likelihood of AI-generated code, code quality issues, and the likelihood of inefficiency. Gamification is applied to promote engagement. More game points will be obtained by responding to relevant quizzes and submitting original, high-quality, and efficient programs.