This article explores the potential impact of artificial intelligence (AI) and machine learning on higher education. It overviews current generative AI capabilities and argues for ethical frameworks to address issues such as bias. The article advocates for a multidisciplinary governance approach involving institutional stakeholders by examining…

Despite widespread agreement that privacy in the context of education is important, it can be difficult to pin down precisely "why" and "to what extent" it is important, and it is challenging to determine how privacy is related to other important values. This chapter seeks to better understand the concept of privacy in…

This chapter begins with an overview of the history of the Association for Institutional Research's use of ethical guidelines. The Association reworked these principles during 2019, the next chapter discusses the rationale and process for this rework. Next, AIR's new Statement of Ethical Principles is presented and discussed, and finally the…

In this chapter, we walk the reader through a set of scenarios that administrators might face when using different types of data on campus. We also provide guiding questions to help institutional research professionals explore how to think about these scenarios with ethics in mind.

Institutional researchers can be confronted with ethical quandaries. Pressures from their institutions can conflict with professional standards and general ethical requirements. Analyses that appear to be neutral can conceal hidden assumptions that are unintentionally harmful to those individuals whom institutions have a responsibility to protect.…

Ethical issues are structurally present in the design and choice of data; the interaction between problem, model, and intervention; and the scientism that supports data analytics' claims to authority. In this chapter, I argue against three social assumptions behind common implementations of learning analytics: data realism, technological…

Traditional institutional research systems may limit who is counted and how they are counted because of limitations associated with disability classification, self-disclosure of disability status, and accessibility limitations inherent within some data-collection methods. As postsecondary institutions work toward improving access for disabled…

This chapter addresses how IR/IE both responds to and leads in our institutions and across higher education in measuring and improving student success. We introduce a new student success measurement framework in the context of internal and external facing needs, we define the importance of actionable information to inform decision-making, and we…

In this chapter, we explore the contexts within which institutional researchers must try to ensure the ethical use of data. We also identify challenges institutional research professionals can face when navigating college culture while trying to ensure the ethical use of data. Finally, we identify potential solutions for improving institutional…

Institutions are applying methods and practices from data analytics under the umbrella term of "learning analytics" to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their…

This new edition of Successful School Leadership brings in the latest evidence and material to what has remained a popular publication. While the fundamentals of what drives successful school leadership remain the same, new evidence further supports the arguments put forward by Christopher Day and Pam Sammons back in 2016. The growing interest in…

Working with partners including the Department of Education at Oxford University, the Centre for Equity in Education at the University of Manchester, the University of Glasgow, the University of Nottingham and the Hong Kong Institute of Education, "Education Development Trust" has commissioned a series of reviews of international…