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This study explores the application of artificial intelligence (AI) in qualitative research, specifically examining how large language models (LLMs) can be utilized to code qualitative data and identify relationships among coder-defined themes. The approach is particularly useful for cases where researchers have previously-identified themes and hypotheses but lack the resources to code a large corpus of data manually. We outline a multi-step methodological framework grounded in qualitative research traditions, whereby researchers first conduct manual coding using a grounded theory approach (Charmaz, 2006; Glaser & Strauss, 1967) on a subset of the data. The resulting codes are then applied to the remaining data using a model-assisted process that integrates natural language processing, AI-based text classification (Noah et al., 2024), and topic identification. Lastly, this is followed by statistical analyses to test hypotheses and expected patterns, providing a robust approach to ensure reliability and accuracy. We illustrate this process through the systematic application of locally-run AI for coding interview transcripts related to graduate students’ experiences in four Ph.D. programs at a large research university. We demonstrate how AI can improve the efficiency, consistency, and scalability of qualitative research without sacrificing confidentiality. This study highlights the potential for AI to enhance qualitative research processes while addressing challenges related to nuance and interpretation. 

The importance of creating diverse and equitable environments in higher education has gained growing recognition in recent years (Allen, 2005). While individual-level bias training has shown limited efficacy, this study proposes that program structure—characterized by clear, transparent, and uniformly applied standards, expectations, and norms—may be a more effective route to equity. Leveraging data from a large U.S. public university, we present evidence from multi-level modeling that demonstrates the positive relationship between program structure and equity-related outcomes, including psychological well-being and academic performance. Notably, these effects appear to disproportionately benefit women and underrepresented minority students, suggesting that structure may be particularly impactful for marginalized students, who are often excluded from informal informational networks within their departments. This research contributes to the ongoing dialogue on practical strategies for achieving equity in higher education, offering an alternative to individual-focused interventions. We discuss the theoretical implications for research on marginalized groups and provide actionable recommendations for practitioners. The study highlights the potential of structural approaches in fostering more equitable and inclusive learning environments in higher education. 

Abstract Background The lack of racial diversity in science, technology, engineering, and mathematics (STEM) disciplines is perhaps one of the most challenging issues in the United States higher education system. The issue is not only concerning diverse students, but also diverse faculty members. One important contributing factor is the faculty hiring process. To make progress toward equity in hiring decisions, it is necessary to better understand how applicants are considered and evaluated. In this paper, we describe and present our study based on a survey of current STEM faculty members and administrators who examined applicant qualifications and characteristics in STEM faculty hiring decisions. Results There are three key findings of the present research. First, we found that faculty members placed different levels of importance on characteristics and qualifications for tenure track hiring and non-tenure track hiring. For example, items related to research were more important when evaluating tenure track applicants, whereas items related to teaching and diversity were more important when evaluating non-tenure track applicants. Second, faculty members’ institutional classification, position, and personal identities (e.g., gender, race/ethnicity) had an impact on their evaluation criteria. For instance, we found men considered some diversity-related items more important than women. Third, faculty members rated the importance of qualifications with diversity, equity, and inclusion (DEI)-related constructs significantly lower than qualifications that did not specify DEI-related constructs, and this trend held for both tenure track and non-tenure track faculty hiring. Conclusions This study was an attempt to address the issue of diversity in STEM faculty hiring at institutions of higher education by examining how applicant characteristics are considered and evaluated in faculty hiring practices. Emphasizing research reputation and postdoctoral reputation while neglecting institutional diversity and equitable and inclusive teaching, research, and service stunt progress toward racial diversity because biases—both implicit and explicit, both positive and negative—still exist. Our results were consistent with research on bias in recruitment, revealing that affinity bias, confirmation bias, and halo bias exist in the faculty hiring process. These biases contribute to inequities in hiring, and need to be addressed before we can reach, sustain, and grow desired levels of diversity. 

This research paper describes a study designed to help inform STEM faculty hiring practices at institutions of higher education in the U.S., where over the past two decades, diversity statements have become more popular components of application packages for faculty jobs. The purpose is to explore the ways and extent to which diversity statements are utilized in evaluating faculty applicants. The research questions are: (1) To what extent do universities equip search committees to evaluate applicants’ diversity statements? (2) What are STEM faculty’s perspectives of diversity statements in job applications? This paper is derived from a larger two-phase sequential mixed methods study examining the factors current faculty members and administrators consider important when hiring new STEM faculty. During the first phase, we deployed a nationwide survey to STEM faculty members and administrators who have been involved in faculty searches, with 151 of 216 respondents answering questions specific to diversity statements. About 29% of survey respondents indicated their departments required diversity statements; 59% indicated their institutions did not provide guidance for evaluating them. The second phase was a phenomenological study involving interviews of 25 survey respondents. Preliminary analyses of interview data indicated that a little more than half (52%) of participants’ departments required a diversity statement. Of the departments that required diversity statements, a little more than half used a rubric for evaluation, whether as part of a larger holistic rubric, or as a standalone rubric. For some departments that did not require diversity statements, applicants were required to discuss diversity within their other application materials. Regarding faculty members’ perceptions of diversity statements, some felt that diversity statements were necessary to assess candidates’ beliefs and experiences. Some noted that when diversity is discussed as part of another document and is not required as a stand-alone statement, it feels like the candidate “slaps on a paragraph” about diversity. Others viewed diversity statements as a “bump” that gives candidates “bonus points.” A few faculty felt that diversity statements were “redundant,” and if applicants were passionate about diversity, they would organically discuss it in the other required documents. Many shared frustrations regarding the requirement and evaluation practices. Most participants indicated their postings provided applicants with little to no guidance on what search committees were looking for in submitted diversity statements; they felt it would be beneficial for both the search committee and the applicants to have this guidance. Shared through a traditional lecture, results from this study may be used to help inform strategies for recruiting faculty who are committed to diversity - and ideally, equity and inclusion - and for addressing equity in faculty hiring.