Chan-Jin Chung

Chan-Jin Chung

The widespread availability of generative artificial intelligence (GenAI) has created a pressing challenge in computer science (CS) education: how to incorporate powerful AI tools into programming coursework without undermining student learning through cognitive offloading. This paper presents an assessment model that permits the use of generative AI for take-home programming assignments while enforcing individual mastery through immediate, assignment-driven written quizzes. To promote authentic learning, these in-class, closed-book assessments are weighted more heavily than the assignments themselves and are specifically designed to verify the student's comprehension of the algorithms, structure, and implementation details of their submitted code. Preliminary empirical data were collected from an upper-level computer science course to examine the relationship between self-reported GenAI usage and performance on AI-free quizzes, exams, and final course grades. Statistical analyses revealed no meaningful linear correlation between GenAI usage levels and assessment outcomes, with Pearson correlation coefficients consistently near zero. These preliminary results suggest that allowing GenAI for programming assignments does not diminish students' mastery of course concepts when learning is verified through targeted, assignment-driven quizzes. Although limited by a small sample size, this study provides preliminary evidence that the risks of cognitive offloading can be mitigated by allowing AI-assisted programming practice while verifying understanding through assignment-driven, AI-free quizzes. The findings support the responsible adoption of open GenAI policies in upper-level CS courses, when paired with rigorous, independent assessment mechanisms.

Beñat Froemming-Aldanondo, Tatiana Rastoskueva, Michael Evans et al.

Reliable lane-following is essential for automated and assisted driving, yet existing solutions often rely on models that require extensive computational resources, limiting their deployment in compute-constrained vehicles. We evaluate five low-resource lane-following algorithms designed for real-time operation on vehicles with limited computing resources. Performance was assessed through simulation and deployment on real drive-by-wire electric vehicles, with evaluation metrics including reliability, comfort, speed, and adaptability. The top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame, outperforming compute-intensive and poor generalizing deep learning approaches. These approaches demonstrated robustness across lighting conditions, road textures, and lane geometries. The findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies. Reducing computing requirements enables lane keeping to be widely deployed in vehicles as part of lower-level automation, including active safety systems.

Gregory Yeghiyan, Jurius Azar, Devson Butani et al.

This paper presents a real-time spill detection system that utilizes pretrained deep learning models with RGB and thermal imaging to classify spill vs. no-spill scenarios across varied environments. Using a balanced binary dataset (4,000 images), our experiments demonstrate the advantages of thermal imaging in inference speed, accuracy, and model size. We achieve up to 100% accuracy using lightweight models like VGG19 and NasNetMobile, with thermal models performing faster and more robustly across different lighting conditions. Our system runs on consumer-grade hardware (RTX 4080) and achieves inference times as low as 44 ms with model sizes under 350 MB, highlighting its deployability in safety-critical contexts. Results from experiments with a real robot and test datasets indicate that a VGG19 model trained on thermal imaging performs best.