Recent advances in large language models (LLMs) have brought AI-based systems into higher education, raising critical concerns regarding ethical and pedagogical implications. As AI-based systems become widely integrated in educational settings, questions about these concerns become increasingly important, yet current literature on best practices is still developing. Our work aims to guide researchers and educators by exploring the challenges and opportunities of AI-based systems in computer science higher education. We conducted a PRISMA-inspired literature review (N = 45), applying qualitative thematic analysis. Our findings suggest AI can foster a judgment-free space for underrepresented groups, especially considering the often competitive and defensive climate of computer science education, yet AI overuse may simultaneously undermine students’ belief in their competencies. We examine student and educator perspectives, linguistic nuances, policy considerations, and next steps. We finalize the discussion through classroom recommendations that reject the perceived dichotomy between academic AI policy and teaching AI literacy.

Given the fast growth of on-demand transportation services and ride-sharing platforms, the concept of private vehicle ownership is rapidly declining. Although there are multiple fully-grown ride-sharing systems, they are proprietary and centrally controlled. Facilitating ride-sharing using a localized distributed coordination between the riders and the drivers is in need. However, fully distributed systems deal with a large number of variables and objectives and are often sub-optimal. In this paper, we propose a distributed ride-sharing system with multiple objectives which are often conflicting to each other. Therefore, we model it as a multi-objective optimization problem and solve it using the Ant Colony optimization technique which sports a multi-agent behavior. We critically analyze the spatio-temporal challenges posed by the ride sharing problem and define novel performance metrics to capture the underlying subtlety of the distributed system performance. An in-depth experimentation with recent large-scale single-ride taxi trip data from Chicago shows that our solution can ensure up to 79.65% success rate of ride sharing. We have shown that ride sharing is more successful during non-peak traffic hours due to less contention and a healthy balance in passenger and taxi numbers. Further, it has been observed that ride-sharing always reduces thetotal distance travelledby all the taxis and thetotal number of taxison-road; both of which positively impact road congestion and environment. The results obtained from the experiments are very much comparable to real time behaviour of taxi networks. Finally, a revenue framework is proposed to analyse nuances of the operating environment.