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AbstractLarge Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high‐level programming languages. Like many other university educators, those teaching programming courses would like to detect if students submit assignments generated by an LLM. To investigate grade performance and the likelihood of instructors identifying code generated by artificial intelligence (AI) tools, we compare code generated by students and ChatGPT for introductory Python homework assignments. Our research reveals mixed results on both counts, with ChatGPT performing like a mid‐range student on assignments and seasoned instructors struggling to detect AI‐generated code. This indicates that although AI‐generated results may not always be identifiable, they do not currently yield results approaching those of diligent students. We describe our methodology for selecting and evaluating the code examples, the results of our comparison, and the implications for future classes. We conclude with recommendations for how instructors of programming courses can mitigate student use of LLM tools as well as articulate the inherent value of preserving students' individual creativity in producing programming languages.

To respond to fluctuations in funding and community needs, human service organizations must develop mechanisms for designing, implementing, and solidifying agency-wide change. This article describes an agency change effort that was staff driven and behaviorally focused and included work teams of employees from all agency divisions. Organization development principles guided the work. The change effort, which focused on the agency's adoption of standards of excellence in client care, sought to disseminate, educate about, and institutionalize these standards. The change occurred through a multifaceted intervention with activities at 14 levels. We describe the nuts and bolts of the change process to fill a gap in the literature on implementing change in human service organizations. The model is applicable to many such agencies.

Recruiting a heterogeneous community sample of White and African American subjects for a research study is a challenging task. Researchers have frequently failed to recruit African Americans or to recruit racially balanced samples. This paper describes the experiences of one social work research team in recruiting a community sample of White and African American women. Specific strategies are presented as a primer for developing samples that are diverse and balanced sociodemographically. A recruitment model is proposed, including use of multiple strategies, simultaneous attention to each segment of the targeted population, and periodic midcourse correction. The results of our recruitment efforts suggest that attracting a sociodemographically balanced sample can be successful when appropriate strategies are implemented.