Welcome to ASMCUE in Seattle, Washington! Registered attendees are able to download the mobile app onto their phone, create a log-in, and make a personalized schedule and chat with attendees. Questions? Email [email protected]
Log in to add sessions to your schedule and sync them to your phone or calendar.
Background: As artificial intelligence (AI) tools rapidly evolve, higher education faculty face the dual challenge of leveraging these technologies for course development while navigating their impact on student learning. Understanding how educators engage with these tools is essential for shaping future life science pedagogy. Research Question: To what extent, and for what specific pedagogical purposes, do immunology and microbiology faculty integrate AI tools into their teaching, and what are their primary concerns regarding student usage? Methods: A survey was developed and disseminated to faculty teaching immunology, microbiology, and related biological courses across diverse institutions. The survey collected respondent demographics, specific AI platforms utilized, administrative and pedagogical purposes of AI application, and faculty policies regarding student AI usage. Results: A striking 96% of surveyed faculty report using AI in their teaching practices in some form. Respondents primarily utilize AI tools as force multipliers for customized learning, content generation, and assessment optimization, which faculty note has increased overall student engagement. Regarding student usage, 60% of educators actively encourage students to use AI for spelling or grammar verification and foundational topic research. However, significant hurdles persist; faculty expressed profound concerns regarding threats to student academic integrity and the potential depletion of critical thinking skills. Conclusion: While AI serves as a powerful resource for immunology faculty productivity and engagement, its integration requires structural guardrails. To maximize the educational benefits of AI, institutions must establish clear, standardized guidelines that balance automated tool usage with robust student training in scientific literacy, critical data evaluation, and ethical use of AI.
