To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies

From threat to tool: AI self-efficacy and user profiles in higher education

Generative artificial intelligence (GenAI) has brought both challenges and opportunities for teachers in higher education. This study does not focus on restrictive methods; instead, it explores how educators can actively promote academic integrity. We contend that AI self-efficacy, characterized as a student’s confidence in their capacity to employ AI ethically and efficiently, constitutes a substantial determinant. This paper delineates significant findings derived from a survey administered by 177 university students. A moderation analysis indicates that the adverse correlation between AI usage frequency and academic integrity is markedly diminished among students exhibiting high AI self-efficacy. Moreover, a cluster analysis clearly delineates three distinct AI user profiles: Confident and Cautious Users, Pragmatic High Users, and Dependent Users. Demographic studies reveal a significant correlation between these profiles and the students’ academic disciplines. The results suggest that the focus of instruction should transition from a “one-size-fits-all” approach to tailored interventions designed to enhance student empowerment. This article outlines practical implications and customized strategies for each student profile.

Teaching Programming in the Age of AI: Pedagogical Strategies for Computational Sociology

As programming becomes increasingly central to sociological research and education, the rise of artificial intelligence (AI) presents both opportunities and challenges for instructors. AI tools such as ChatGPT and GitHub Copilot are transforming how students approach coding, raising important questions about what programming competencies matter most, how they should be taught, and how learning should be assessed. Drawing on emerging research, this conversation article examines the capabilities and limitations of AI tools and considers AI’s impact on student learning and skill development. We outline sociologically grounded learning goals that emphasize both technical fluency and critical judgment. We then offer evidence-informed strategies for incorporating active learning, developing metacognitive skills, and designing assessments suited to an AI-enhanced educational environment. Throughout, we argue that programming should be treated not only as a technical skill but also as a sociological practice that enables students to fully participate in computational inquiry while engaging critically and ethically.

What are we sharing? Faculty use of generative AI and the questions we have not yet asked in nursing education.

This paper begins from the author's own practice and observations among colleagues, then examines three sets of questions raised by faculty AI use that have not yet been adequately engaged in the nursing literature.

Using Classroom Surveys to Align Grading Practices and Self-Directed Learning in the Age of AI

The rapid saturation of research on Artificial Intelligence (AI) in the classroom reflects concerns with the even more rapidly developing AI in many aspects of society. Framing the parameters of AI literacy can serve as an important context within which educators can guide students toward critical engagement with AI tools in order to develop their self-directed learning. Before this can happen, however, an aspect to consider in order to harness the potential of AI tools includes the necessity of aligning grading practices with self-directed learning. This article explores how such an alignment may foster a learning environment that shifts the use of AI away from merely generating substitutive text and toward actually harnessing its potential as a tool for helping authentic learning to happen. Drawing on relevant research and two survey responses from secondary students, this article aims to establish a context in which reflective classroom surveys can help teachers implement process-based grading structures to empower students in their growth as authentic learners instead of defaulting to passive users of AI. In such a context, the secondary classroom would remove the barriers created by hierarchical grading practices, evolve with emergent technology, train students to interact meaningfully and ethically with it, and ultimately prepare them for the ongoing challenges of using AI in the humanities and society, overall.

AI Literacy as Experimental Practice: Students as Investigators

When AI Gets It Wrong: Conscious Competence Development through Student Design Reflections

Reflections in engineering are a fruitful tool for encouraging lifelong learning and provide instructors with insight into how to adapt the learning environment to meet students' needs. However, reflections are impractical for large classes. This research explores the use of a latent Dirichlet allocation model to automatically classify students' design reflections, thereby improving the efficacy of their reviews. The model showed promising quantitative performance, but from a human reviewer's perspective, it lacked interpretability. The contradicting results encouraged a reflective discussion of the potential misuses and risks of artificial intelligence (AI), both within and outside the classroom, without a qualitative review. Based on the authors' experiences and lessons from this study, this paper advocates for professional engineering licensure bodies to modernize policies to encourage thoughtful, rigorous evaluation of AI models before deployment. This article was written to encourage engineering educators to utilize AI critically and responsibly.