This paper presents two complementary quantitative studies examining the integration of generative AI (GenAI) tools into programming courses in higher education. Study 1 investigated undergraduate students’ perceptions of GenAI tools, focusing on usefulness in coursework, creativity enhancement, behavioral intention to use, and concerns and critiques. Study 2 evaluated undergraduate students’ performance in correcting code generated by large language models (LLMs) during programming exams, comparing this performance to their results on instructor-designed programming tasks. Findings from Study 1 revealed generally favorable student perceptions of GenAI. Participants reported medium-to-high levels of perceived usefulness, particularly emphasizing GenAI’s potential to enhance learning efficiency and support creative problem-solving. Students also expressed strong intentions to continue using GenAI tools in their studies, while reported concerns were relatively low and centered primarily on the risk of over-reliance. Findings from Study 2 showed that students encountered significantly greater difficulty when correcting LLM-generated code compared to traditional exam tasks, highlighting the unique challenges posed by AI-generated outputs in assessment contexts. Taken together, the results suggest that while students recognize the value of GenAI tools in supporting learning and creative exploration, programming education must also focus on developing students’ skills in critically evaluating and correcting AI-generated content to ensure effective and responsible integration.

Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc. This work aims to extend our formally defined T measure to present a new measure aiming to recognize the actor’s influence by the strength of attracting new important actors into a networked community. Therefore, we propose a model of the actor’s influence based on the attractiveness of the actor in relation to the number of other attractors with whom he/she has established connections over time. Using an empirically collected social network for the underlying graph, we have applied the above-mentioned measure of influence in order to determine optimal seeds in a simulation of influence maximization. We study our extended measure in the context of information diffusion because this measure is based on a model of actors who attract others to be active members in a community. This corresponds to the idea of the IC simulation model which is used to identify the most important spreaders in a set of actors.

Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs. Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.

We present a modular, explainable LLM-agent pipeline for decision support that externalizes reasoning into auditable artifacts. The system instantiates three frameworks: Vester's Sensitivity Model (factor set, signed impact matrix, systemic roles, feedback loops); normal-form games (strategies, payoff matrix, equilibria); and sequential games (role-conditioned agents, tree construction, backward induction), with swappable modules at every step. LLM components (default: GPT-5) are paired with deterministic analyzers for equilibria and matrix-based role classification, yielding traceable intermediates rather than opaque outputs. In a real-world logistics case (100 runs), mean factor alignment with a human baseline was 55.5\% over 26 factors and 62.9\% on the transport-core subset; role agreement over matches was 57\%. An LLM judge using an eight-criterion rubric (max 100) scored runs on par with a reconstructed human baseline. Configurable LLM pipelines can thus mimic expert workflows with transparent, inspectable steps.