We present a reproducible pipeline that aligns CS1 textbook sections with problems from a public dataset via a Knowledge Component (KC) -a single conceptual skill required for problem solving- ontology. It assigns KCs to sections and problems, respects the prerequisite order to avoid inserting problems too early, and generates tips for not-yet-taught concepts. We evaluate three KC assignment strategies: embedding-only, embedding with a Large Language Model (LLM) tie-breaker, and direct LLM assignment. We find direct assignment matches or exceeds human annotators. Our results show that constrained LLMs can enrich CS1 textbooks with curriculum-aware practice problems.

Research has shown study habits and skills to be correlated with academic success, calling for a deeper comprehension of these behaviors and processes to design effective interventions for struggling students. Chatbots have recently been used as a persuasive technology to help support behavioral change, making them an intriguing design space for students' study habits and skills. This paper investigated the feasibility of using chatbots for promoting behavioral change of college students majoring in Computer Science (CS). We conducted semi-structured interviews with CS peer-tutors and surveyed university freshmen to understand students' study habits and identify technical intervention opportunities. Inspired by the findings, we designed StudyBuddy, a chatbot prototype deployed in Slack that periodically sends tips, provides assessments of students' study habits via surveys, helps the students break down assignments, recommends academic resources, and sends reminders. We evaluated the usability of the prototype in-depth with 8 students (both first-year and senior students) and 5 course instructors followed by a large scale evaluative survey (n=117) using video of the prototype. Our research identified important design challenges such as building trust and preserving privacy, limiting interaction costs, and supporting both immediate and long-term sustainable support. Likewise, we proposed design recommendations that demonstrate context awareness, personalize the experience based on user preferences, and adapt over time as students mature and grow.

Genome sequencing technologies have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand mechanisms of disease and predict the effects of medical interventions, high-throughput data must be integrated with demographic, phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods must infer relationships between these data types. We recently proposed a pipeline (CausalMGM) to achieve this. CausalMGM uses probabilistic graphical models to infer the relationships between variables in the data; however, CausalMGM's graphical structure learning algorithm can only handle small datasets efficiently. We propose a new methodology (piPref-Div) that selects the most informative variables for CausalMGM, enabling it to scale. We validate the efficacy of piPref-Div against other feature selection methods and demonstrate how the use of the full pipeline improves breast cancer outcome prediction and provides biologically interpretable views of gene expression data.