Search for: All records

Novice programming students frequently engage in help-seeking to find information and learn about programming concepts. Among the available resources, generative AI (GenAI) chatbots appear resourceful, widely accessible, and less intimidating than human tutors. Programming instructors are actively integrating these tools into classrooms. However, our understanding of how novice programming students trust GenAI chatbots-and the factors influencing their usage-remains limited. To address this gap, we investigated the learning resource selection process of 20 novice programming students tasked with studying a programming topic. We split our participants into two groups: one using ChatGPT (n=10) and the other using a human tutor via Discord (n=10). We found that participants held strong positive perceptions of ChatGPT's speed and convenience but were wary of its inconsistent accuracy, making them reluctant to rely on it for learning entirely new topics. Accordingly, they generally preferred more trustworthy resources for learning (e.g., instructors, tutors), preferring ChatGPT for low-stakes situations or more introductory and common topics. We conclude by offering guidance to instructors on integrating LLM-based chatbots into their curricula-emphasizing verification and situational use-and to developers on designing chatbots that better address novices' trust and reliability concerns. 

Indigenous communities remain among the most underrepresented groups in computing and STEM fields, facing systemic barriers to equitable participation in computer science (CS) education. This study examines how Indigenous-serving teachers, through a sustained professional development (PD) program, design and implement culturally responsive computing (CRC) curricula in Indigenous-serving schools. Guided by the research question: How does sustained CS professional development inform the design of culturally responsive computing curricula by experienced CS teachers in Indigenous-serving schools? We employed a natural language processing (NLP) data fusion approach that integrates text mining and qualitative thematic analysis to investigate how teachers incorporate Indigenous knowledge into computing instruction. Our findings reveal three emergent themes in teacher learning and lesson design: Creating opportunities to access culture through computation, Leveraging Research and Critical Thinking Skills to Critically Engage Students with Computing, and Reflection, refinement, and professional growth through ongoing collaboration. These themes underscore the impact of CRC on bridging cultural traditions with computing, fostering engagement, and enhancing Indigenous students’ sense of belonging in CS. By supporting teachers in developing culturally relevant lessons that integrate storytelling, traditional arts, and computational thinking, this research contributes to the broader discourse on inclusive CS education. This study informs future efforts to expand Indigenous student participation in computing by highlighting the role of culturally sustaining pedagogy in professional development and curriculum design. 

Large Language Model (LLM) conversational agents are increasingly used in programming education, yet we still lack insight into how novices engage with them for conceptual learning compared with human tutoring. This mixed‑methods study compared learning outcomes and interaction strategies of novices using ChatGPT or human tutors. A controlled lab study with 20 students enrolled in introductory programming courses revealed that students employ markedly different interaction strategies with AI versus human tutors: ChatGPT users relied on brief, zero‑shot prompts and received lengthy, context‑rich responses but showed minimal prompt refinement, while those working with human tutors provided more contextual information and received targeted explanations. Although students distrusted ChatGPT’s accuracy, they paradoxically preferred it for basic conceptual questions due to reduced social anxiety. We offer empirically grounded recommendations for developing AI literacy in computer science education and designing learning‑focused conversational agents that balance trust‑building with maintaining the social safety that facilitates uninhibited inquiry. 

BACKGROUND: Natureculture (Haraway, 2003; Fuentes, 2010) constructs offer a powerful framework for science education to explore learners’ interactions with and understanding of the natural world. Technologies such as Augmented Reality (AR) designed to reveal pets’ sensory worlds and companionship with pets can facilitate learners’ harmonious relationships with significant others in naturecultures. METHODS: At a two-week virtual summer camp, we engaged teens in inquiring into dogs’ and cats’ senses using selective color filters, investigations, experience design projects, and understanding how the umwelt (von Uexküll, 2001) of pets impacts their lives with humans. We qualitatively analyzed participants’ talk, extensive notes, and projects completed at the workshop. FINDINGS: We found that teens engaged in the science and engineering practices of planning and carrying out investigations, constructing explanations and designing solutions, and questioning while investigating specific aspects of their pets’ lives. Further, we found that teens checking and taking pets’ perspectives while caring for them shaped their productive engagement in these practices. The relationship between pets and humans facilitated an ecological and relational approach to science learning. CONTRIBUTION: Our findings suggest that relational practices of caring and perspective-taking coexist with scientific practices and enrich scientific inquiry.