Artificial intelligence in higher education has expanded worldwide with the growth of AI technologies. The emergent applications of artificial intelligence in higher education (AIEd) in course preparation, delivery, and administration as well as in learning assistants and prediction for the sake of learners are widely considered important innovations that can change education worldwide. The Armenian case was examined via a survey that was conducted at the country’s leading universities to investigate whether and how faculty use AIEd. Although the results indicate that many Armenian teaching faculty use AIEd in narrow capacities and acknowledge the potential usefulness of AIEd in the practical administration of Armenia’s higher education, the use of AIEd is not deep.

Twitter has been a prominent forum for academics communicating online, both among themselves and with policy makers and the broader public. Elon Musk’s takeover of the company brought sweeping changes to many aspects of the platform, including public access to its data; Twitter’s approach to censorship and mis/disinformation; and tweaks to the affordances of the platform. This article addresses a narrower empirical question: What did Elon Musk’s takeover of the platform mean for this academic ecosystem? Using a snowball sample of more than 15,700 academic accounts from the fields of economics, political science, sociology, and psychology, we show that academics in these fields reduced their “engagement” with the platform, measured by either the number of active accounts (i.e., those registering any behavior on a given day) or the number of tweets written (including original tweets, replies, retweets, and quote tweets). We further tested whether this decrease in engagement differed by account type; we found that verified users were significantly more likely to reduce their production of content (i.e., writing new tweets and quoting others’ tweets) but not their engagement with the platform writ large (i.e., retweeting and replying to others’ content).

How can political science scholars use visualization and mapping tools to refine the development of research on complex theoretical concepts? Literature mapping, a powerful method commonly used in the natural sciences to visualize scientific landscapes, is not yet widely used in political science. This study illustrates the capabilities of this method by analyzing visual maps of academic research on the term “organizing” in the context of political action. We describe our multistep methodological approach for generating the maps and demonstrate how they can be analyzed to produce insights about themes, potential gaps, canonical literature, and levels of dialogue across research areas. We conclude by outlining future research possibilities generated by this study’s literature mapping approach.

How can political scientists rigorously evaluate the predictive power of theories? Many peer-reviewed political science articles include predictions about future outcomes, and scholars make predictions on social media and other public forums. The prevalence of predictions suggests that scholars recognize the utility of leveraging theories for this purpose; however, the predictions often are not made in a manner that allows for rigorously evaluating their accuracy. Building on the increasing popularity of study preregistration in the social sciences, this article proposes “prediction registration” as a means for scholars to publish falsifiable, systematic, and verifiable theory-based predictions. Increasing the rigor of predictive theory testing can advance often-circular debates about accuracy and presents a “win-win” for scholars who aim to test the predictive power of theories. With a more rigorous approach, correct predictions would better demonstrate a theory’s ability to forecast outcomes, and missed predictions would reveal information that can be used to calibrate the theory.

Understanding how institutions shape outcomes is an important skill for students of American political science. Simulations in which students participate in mock elections structured by real-world institutions can serve as an effective tool to aid students’ learning. This article describes a model for a simulated 2020 Democratic Iowa caucus. Following official procedures, students engage in debates about which candidate to support and ultimately come to a final decision for their simulated precinct. The simulation helps students to understand the institutions that structure Iowa’s caucuses and assists them in forming conclusions about the strengths and weaknesses of those institutions. This simulation was implemented in a course on political parties and elections in the Spring 2023 semester, where it proved to be an enjoyable exercise for students. It was reimplemented in the Spring 2024 semester as one of several simulated presidential nominations, and it proved to be a helpful teaching tool.

Whereas significant attention has been devoted to online/blended teaching and related tools, open GenAI chatbots and large language models and writing programs have received comparatively less attention as instruments that impact our teaching and assessment methods. The pedagogies of political science and international relations somehow trail behind in understanding and addressing them. For those who are teaching these subjects, it is of great importance to come to terms with AI and its impact on how we should assess students. This article describes an eight-week laboratory during which we experimented with and discussed ChatGPT’s utilizations with our students. The goal of the laboratory was to revise which type of learning objectives our teaching should have and which type of assessment methods are best suited for an environment in which GenAI is present and used regularly. Should we ban it or should we instead focus on teaching students how to use it responsibly and ethically, as well as developing a shared understanding of what GenAI is good for (if anything at all)? Consequently, which learning objectives and assessment methods should we adopt?