STEM Faculty Perspectives on Generative AI in Higher Education

TL;DR

This study explores STEM faculty perspectives on generative AI in higher education, highlighting their adoption patterns, perceived benefits and challenges, and the need for institutional support to ensure responsible use.

Contribution

It provides empirical insights into faculty attitudes and practices regarding GenAI, emphasizing the importance of policy and pedagogical adjustments for effective integration.

Findings

Faculty use GenAI for content creation and assessment support.

Concerns about student learning, assessment validity, and academic integrity.

Need for institutional policies to guide responsible GenAI adoption.

Abstract

Generative artificial intelligence (GenAI) tools are increasingly present in higher education, yet their adoption has been largely student-driven, requiring instructors to respond to technologies already embedded in classroom practices. While some faculty have embraced GenAI for pedagogical purposes such as content generation, assessment support, and curriculum design, others approach these tools with caution, citing concerns about student learning, assessment validity, and academic integrity. Understanding faculty perspectives is therefore essential for informing effective pedagogical strategies and institutional policies. In this paper, we present findings from a focus group study with 29 STEM faculty members at a large public university in the United States. We examine how faculty integrate GenAI into their courses, the benefits and challenges they perceive for student learning, and the institutional support they identify as necessary for effective and responsible adoption. Our findings highlight key patterns in how STEM faculty engage with GenAI, reflecting both active adoption and cautious use. Faculty described a range of pedagogical applications alongside concerns about student learning, assessment, and academic integrity. Overall, the results suggest that effective integration of GenAI in higher education requires rethinking assessment, pedagogy, and institutional governance in addition to technical adoption.