We study instructor perspectives on LLM-generated programming feedback in an introductory Python course. LLM tutors predominantly offered debugging help, while human instructors preferred more diverse feedback types, including conceptual reminders, revisiting the problem, and examples. Cases where LLM tutor feedback diverged from human instructors' intent required major edits with different feedback types, while cases with closer alignment needed only minor changes with similar feedback types. Findings highlight the need for LLM tutors to reflect on instructor intent to ensure pedagogically aligned feedback.

Large Language Models (LLMs) are increasingly used as programming tutors, but their feedback is often generic and prone to solution leakage. To address these issues, we present MisconceptionTutor, which grounds feedback in common student misconceptions. Through both pre-deployment analyses and a real-classroom deployment, we find that even simple prompting frameworks can meaningfully steer tutor behavior to be more pedagogically oriented and noticeably more satisfying to students.

Artificial Intelligence (AI) has impacted the world tremendously in the last decade, causing an increased demand for accessible AI education globally. Students benefit from studying AI earlier in the curriculum; however, AI courses can require a range of prerequisites, which can be structured differently in various educational contexts. In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and compare it with that of US Research-1 institutions. There are many similarities between AI, ML, and DS courses in Canada and the US. For example, DS courses tend to be more accessible earlier in the CS curriculum compared to AI and ML. However, there are key differences between the two countries, with Canadian AI, ML, and DS courses generally being a part of a longer prerequisites chain, and Canadian CS departments offering fewer DS courses. Still, both Canadian and US institutions find innovative ways to introduce AI earlier in the curriculum, including via interdisciplinary courses and specialized courses with few prerequisites. This study corroborates earlier work in recognizing diversity in curricular frameworks in North America and recommends curricular revisions and early academic advising to ensure access to AI courses.

Large Language Models (LLMs) can be used to automate many aspects of the educational field. In this paper, we look into the benefits of automating responses to student questions in course discussion forums using our Retrieval-Augmented Generation (RAG)-based LLM pipeline (Edison). Our research questions are:

Identifying Knowledge Components (KCs) in computer science education improves curriculum design and teaching strategies. We introduce a framework using Large Language Models to identify KCs from programming assignments automatically. Our framework helps educators align assignments with course objectives. GPT-4 identifies relevant KCs well, though there's a low match with expert-generated KCs at the course level. At the problem level, performance is lower, but key KCs are reasonably identified.

This Research Full paper focuses on the challenges in artificial intelligence, machine learning, and data science education—referred to as “artificial intelligence” courses here-after-often characterized by extensive prerequisites that limit student access. We analyze the course structures and prerequisites of these courses in computing departments at 50 Research-1 institutions in the United States, recognized for their “Very High Research Activity.” Our methodology involves analyzing course syllabi to examine the structure and prerequisites of these courses, using open coding to develop a unified codebook to identify prerequisites and determine the earliest exposure levels for students. A clustering analysis was also conducted to identify common and differing curriculum approaches among institutions. Results show that data science courses require less initial exposure, while artificial intelligence and machine learning courses require more prerequisites. Standard requirements for artificial intelligence courses include basic data structure (Computer Science 2) and algorithms, with machine learning courses requiring more mathematics preparation. Moreover, public institutions offer advanced courses with more prerequisites compared to private institutions. Overall, this study recognizes considerable diversity in curricular frameworks across Research-1 institutions and encourages institutions to revise curricula to broaden access to artificial intelligence education and increase participation in research.

This study utilizes large language models to extract foundational programming concepts in programming assignments in a CS1 course. We seek to answer the following research questions: RQ1. How effectively can large language models identify knowledge components in a CS1 course from programming assignments? RQ2. Can large language models be used to extract program-level knowledge components, and how can the information be used to identify students' misconceptions? Preliminary results demonstrated a high similarity between course-level knowledge components retrieved from a large language model and that of an expert-generated list.

This study investigates the Computational Thinking skill differences among novice programmers in relation to gender. Block-based visual programming languages such as Scratch particularly benefit K-12 programmers because they learn how to code intuitively. Our study analyzed 124 (62 males, 62 females) Scratch projects on the Scratch website, categorized projects on the basis of each user's gender and project type, and compared their Computational Thinking scores. The results of this study suggest that project types preferred by males require more programming construct reflected in the Computational Thinking score than that of females. Because gender differences appear by project type, project type presumably influences the gender gap in scores.