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.

Background and Context. Research software in the Computing Education Research (CER) domain frequently encounters issues with scalability and sustained adoption, which limits its educational impact. Despite the development of numerous CER programming (CER-P) tools designed to enhance learning and instruction, many fail to see widespread use or remain relevant over time. Previous research has primarily examined the challenges educators face in adopting and reusing CER tools, with few focusing on understanding the barriers to scaling and adoption practices from the tool developers’ perspective.

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.

External help resources are frequently used by novice programmers solving classwork in undergraduate computing courses. Traditionally, these tools consisted of web-based resources such as tutorial websites and Q&A forums. With the rise of AI code-generation and explanation tools, understanding how students use external resources and their roles in classroom have become especially relevant. Despite this, little research has directly investigated the extent to which students intent to use these tools and what factors influence their beliefs. It is unknown when students think it is appropriate to use these tools and what features they find valuable. Understanding these beliefs would allow instructors and researchers to better focus their efforts on what aspects of pedagogy and tool usage should be addressed. We administered a pilot vignette-style survey to introductory programming classes at an R1 University (n=45), giving students scenarios of external resource usage while questioning their attitudes, subjective norms, and their perceived behavioral control on using these external resources. We share preliminary findings on free response data, showcasing the variety of beliefs and opinions that novice programming students have on when and how much external resource usage is acceptable in the classroom. Some students felt that AI tools can provide more exact solutions than searching for help online, but also expressed that this exactness could be detrimental to their learning. Others expressed awareness that professionals use these resources, and expressed a desire to learn how to use them in a way to help their educational and career goals.

Background and Context: Open-ended programming projects encourage novice students to choose and pursue projects based on their own ideas and interests, and are widely used in many introductory programming courses. However, novice programmers encounter challenges exploring and discovering new ideas, implementing their ideas, and applying unfamiliar programming concepts and APIs. Code examples are one of the primary resources students use to apply code usage patterns and learn API knowledge, but little work has investigated the effect of having access to examples on students’ open-ended programming experience.