Background. When AI tools can easily complete programming assignments, students face a motivational question: why invest effort in completing them independently? While prior work has examined instructor policies and usage patterns, we focus on how students themselves experience and respond to AI availability, a perspective important for designing courses that sustain engagement with programming practice.

Metaphors are powerful tools often used by instructors in CS1 courses to bridge abstract ideas and concrete student understanding. This study applies Conceptual Metaphor Theory (CMT) to analyze how instructors and students conceptualize and communicate recursion through metaphor. We analyzed transcripts from three 50-minute lectures on recursion and surveys from 34 students in a CS1 course. We identified metaphorical expressions and derived conceptual metaphors from instructor lecture transcripts. We then compare student survey responses to these conceptual metaphors. Our analysis revealed several prevalent conceptual metaphors used by instructors for recursion. Student responses showed varying degrees of alignment, suggesting some metaphors do not resonate and may be misinterpreted. This work leverages CMT to provide insights into how recursion is taught and understood. This can help instructors become more conscious of their implicit metaphorical language and reveal insights into student understanding.

Metaphors are deeply embedded in the language of computing, from 'stacks' and 'queues' to 'trees' and 'handshaking'. Such metaphorical expressions not only shape but also fundamentally reflect our understanding and conceptualization of computing concepts. Students' descriptions of computing concepts have also been shown to be richly metaphor-laden, often framing abstract notions in more tangible or accessible ways. The rich metaphorical conceptualizations set the stage for a natural application of Conceptual Metaphor Theory (CMT) in computing education research. At its core, CMT posits that metaphors are not just linguistic expressions but fundamental structures of understanding, influencing how we perceive and think about the world.

Analogies are extensively employed as a tool to facilitate conceptual understanding in computer science education. Sociocultural differences among students and students' tendency to overextend analogies pose non-trivial challenges to the effective use of analogies in computing classrooms. This study will examine the viability of scaffolded student-generated analogies to overcome such hurdles and effectively facilitate conceptual learning in a CS1 context.

The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.

Biological cells naturally exchange information for adapting to the environment, or even influencing other cells. One of the latest frontiers of synthetic biology stands in engineering cells to harness these natural communication processes for tissue engineering and cancer treatment, amongst others. Although experimental success has been achieved in this direction, approaches to characterize these systems in terms of communication performance and their dependence on design parameters are currently limited. In contrast to more classical communication systems, information in biological cells is propagated through molecules and biochemical reactions, which in general result in nonlinear input-output behaviors with system-evolution-dependent stochastic effects that are not amenable to analytical closed-form characterization. In this paper, a computational approach is proposed to characterize the information exchange in these systems, based on stochastic simulation of biochemical reactions and the estimation of information-theoretic parameters from sample distributions. In particular, this approach focuses on engineered cell-to-cell communications with a single transmitter and receiver, and it is applied to characterize the performance of a realistic system. Numerical results confirm the feasibility of this approach to be at the basis of future forward engineering practices for these communication systems.