Many philosophers have argued that large language models (LLMs) subvert the traditional undergraduate philosophy paper. For the enthusiastic, LLMs merely subvert the traditional idea that students ought to write philosophy papers “entirely on their own.” For the more pessimistic, LLMs merely facilitate plagiarism. We believe that these controversies neglect a more basic crisis. We argue that, because one can, with minimal philosophical effort, use LLMs to produce outputs that at least “look like” good papers, many students will complete paper assignments in a way that fails to develop their philosophical abilities. We argue that this problem exists even if students can produce better papers with AI and even if instructors can detect AI-generated content with decent reliability. But LLMs also create a pedagogical opportunity. We propose that instructors shift the emphasis of their assignments from philosophy papers to “LLM dialogues”: philosophical conversations between the student and an LLM. We describe our experience with using these types of assignments over the past several semesters. We argue that, far from undermining quality philosophical instruction, LLMs allow us to teach philosophy more effectively than was possible before.

Many instructors see the use of LLMs like ChatGPT on course assignments as a straightforward case of cheating, and try hard to prevent their students from doing so by including new warnings of consequences on their syllabi, turning to iffy plagiarism detectors, or scheduling exams to occur in-class. And the use of LLMs probably is cheating, given the sorts of assignments we are used to giving and the sorts of skills we take ourselves to be instilling in our students. But despite legitimate ethical and pedagogical concerns, the case that LLMs should never be used in academic contexts is quite difficult to see. Many primary and secondary schools are cutting back their writing instruction in an effort to teach to the test; at the same time, many high-end knowledge workers are already quietly expected to leverage their productivity with LLMs. To prepare students for an ever-changing world, we probably do have to teach them at least a little bit about writing with ChatGPT.

This paper argues that the threat Large Language Models (LLMs), such as ChatGPT, pose to writing instruction is both not entirely new and a welcome disruption to the way writing instruction is typically delivered. This new technology seems to be prompting many instructors to question whether essay responses to paper prompts reflect students’ own thinking and learning. This uneasiness is long overdue, and the hope is it leads instructors to explore evidence-based best practices familiar from the scholarship of teaching and learning. We’ve known for some time how to better teach our students to think and write. Perhaps the arrival of LLMs will get us to put these lessons into widespread practice.

This paper presents a summary and discussion of an assignment that asked students about the impact of Large Language Models on their college education. Our analysis summarizes students’ perception of GPT-3, categorizes their proposals for modifying college courses, and identifies their stated values about their college education. Furthermore, this analysis provides a baseline for tracking students’ attitudes toward LLMs and contributes to the conversation on student perceptions of the relationship between writing and philosophy.

Text-generative AI-systems have become important semantic agents with ChatGPT. We conducted a series of experiments to learn what teachers’ conceptions of text-generative AI are in relation to philosophical texts. In our main experiment, using mixed methods, we had twenty-four high school students write philosophical essays, which we then randomized to essays with the same command from ChatGPT. We had ten prospective teachers assess these essays. They were able to tell whether it was an AI or student essay with 78.7 percent accuracy, which is better than the Open AI Classifier. Interestingly, however, they used criteria like argumentative and logical flawlessness and neutrality. We concluded from this that they are using an inverted Turing test and are no longer looking for rationality in machines but for irrationality in humans.

In this article, I will outline an assessment model that allows instructors to continuing assigning term papers and argumentative papers without compromising the authenticity of student assessment. This path forward relies upon a pseudo flipped classroom model in which students will complete a scaffolded term paper through a series of in-class assessments that build upon previously completed components. The final steps of completing this assignment will require producing a draft and final version of a traditional term paper outside of the classroom but only after the instructor ensures they have critically thought through the preceding components independently. The intention is to maintain the nuance, critical thinking, engagement with primary and secondary sources, and depth of content comprehension that a thoughtful paper requires, while also ensuring that students are completing the work themselves and not merely outsourcing bits and pieces of it to LLMs.

According to the World Bank, South Africa is the most unequal society in the world. It follows that teaching philosophy takes on a unique character in this country. During the initial COVID-19 outbreak, all universities were compelled to move online, entailing that the teaching of philosophy also moved online. However, because of their socio-economic realities, students faced many barriers, and this served to further marginalise already marginalised students. The university campus provides structural support to many of these students that they do not have at home—computer labs, internet access, and residences. With campuses shutting down and learning to move online, many (if not most) of our students faced significant challenges. This paper explores the challenges encountered during this time through empirical research. Five interviews were conducted with philosophy lecturers, and the data was analysed using Atlas.ti. This article therefore provides empirical insight into the challenges faced by philosophy lecturers as they taught philosophy during a pandemic in the “most unequal society in the world.”

Intercollegiate Ethics Bowl is a debate-style activity that aims to help students cultivate skills of moral deliberation. While a fair amount has been written about the pedagogical benefits of Ethics Bowl, relatively little attention has been given to potential ethical criticisms of the activity. In this paper I present some reflections on an ethical challenge to Ethics Bowl, namely that applying the characteristic approach of Ethics Bowl to some issues of contemporary ethical controversy can be immoral. The concern is that treating certain topics as “open to debate” conveys disrespect for certain individuals, and risks normalizing harmful viewpoints. I argue that we should take this challenge seriously; indeed, the guiding values behind Ethics Bowl require that we do so. I conclude that conducting Ethical Bowl in an ethical manner requires caution and skill.

The article features an analytic protocol for examining controversial criminal cases in critical thinking courses. The rubric has been designed to be useful to busy critical thinking teachers who wish to draw on student interest in true crime. Six guidelines are presented that are intended to provide the instructor with a convenient scheme for quickly and easily framing classroom discussions. Due to their generality, the guidelines can be readily applied to a wide-range of cases, giving educators a high degree of flexibility in the selection of criminal affairs.