The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments.

Interactive teleoperation offers an intuitive pathway for human–robot interaction, yet many existing systems rely on dedicated sensors or wearable devices, limiting accessibility and scalability. This paper presents a vision-based teleoperation framework that enables real-time control of an articulated robotic arm (five joints plus a gripper actuator) using human hand tracking from a single, typical laptop camera. Hand pose and gesture information are extracted using a real-time landmark estimation pipeline, and a set of compact kinematic descriptors—palm position, apparent hand scale, wrist rotation, hand pitch, and pinch gesture—are mapped to robotic joint commands through a calibration-based control strategy. Commands are transmitted over a lightweight network interface to an embedded controller that executes synchronized servo actuation. To enhance stability and usability, temporal smoothing and rate-limited updates are employed to mitigate jitter while preserving responsiveness. In a human-in-the-loop evaluation with 42 participants, the system achieved an 88% success rate (37/42), with a completion time of 53.48 ± 18.51 s, a placement error of 6.73 ± 3.11 cm for successful trials (n = 37), and an ease-of-use score of 2.67 ± 1.20 on a 1–5 scale. Results indicate that the proposed approach enables feasible interactive teleoperation without specialized hardware, supporting its potential as a low-cost platform for robotic manipulation, education, and rapid prototyping.

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training.

This article presents a mathematical analysis of the geometric continuity of industrial robot trajectories, highlighting the influence of continuity conditions on velocity, acceleration, and vibration profiles. This study proposes an original approach: isolating geometric continuity as an independent parameter and the comparative evaluation of the continuity levels C¹, C², and C³ using a series of pre-computed kinematic metrics: vibration energy, acceleration variability, and trajectory curvature stability. The methodology is validated by numerical simulations performed with RoboDK for trajectory generation and by post-processing in Python for metric evaluation. The results indicate that C¹ trajectories exhibit discontinuities in the higher-order derivatives, which lead to undesirable kinematic behaviors, while C² continuity represents the minimum requirement for an acceptable, stable motion in industrial applications. Higher-order continuity, C³, brings greater regularity to the trajectories, but the practical advantages are limited and relatively insignificant for standard industrial applications.

Learning Analytics (LA) can support student success through dashboards and early-support interventions, but adoption depends on students’ willingness to allow educational data use under privacy and data-protection requirements. This study examines predictors of students’ willingness to allow educational data use for LA in higher education, focusing on perceived benefits, perceived risks, control and transparency expectations, and institutional trust. A cross-sectional survey was administered to engineering students (N = 109); after an instructed-response attention check, N = 102 valid responses were retained. Composite Likert constructs (BENEFIT, RISK, CONTROL, TRANSPARENCY, TRUST) and two willingness outcomes were analyzed: academic-support LA (WILL_ACAD) and broader aggregated institutional reporting under safeguards (WILL_BROAD). Willingness was high in both scenarios, and the paired difference did not reach statistical significance. Regression models showed that institutional trust was the strongest predictor of willingness across both use cases; perceived benefits additionally predicted willingness for academic-support LA, while perceived risk was a positive predictor in the broader-use model. Descriptive results indicated that students prioritize human review before any action affecting a student and strong security measures as key safeguards. These provide initial evidence to inform privacy-aware learning analytics governance in similar technical-university contexts; broader generalization across higher education requires replication across disciplines and institutions.

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by spline interpolation in joint space (cubic or quintic interpolation), so that the distances traveled are shorter. The principle of least action is used as a theoretical foundation trying to find the best cost function in terms of trajectory lengths using. In the pursuit of minimizing this cost function, an iterative method is applied. Initial trajectories are split into multiple internal nodes that are displaced little by little from their initial positions, recomposing trajectories that pass through these displaced nodes at every iteration. The purpose of this paper is to demonstrate that by post-processing trajectories initially generated by the usual spline interpolation in joint space, alternative, shorter variants can be obtained.