A single district, multisite case study about the lived experiences and best practices of faculty in incorporating artificial intelligence - Redden, Cameron (Kansas State University, 2024)
Artificial intelligence (AI) has demonstrated effectiveness in various educational contexts, offering adaptive evaluation mechanisms, personalized learning experiences, and increased learner engagement (Holstein et al., 2019; Woolf, 2019). This study examined the experiences of community college faculty incorporating AI technologies in teaching practices, how faculty engaged first-generation adult learners when incorporating AI technologies, and how AI technologies were addressed in college documents and policies. Given the context of this research, the chosen methodology for this study was a qualitative case study design comprised of interviews with faculty from a single-district multi-campus site in the United States. The interviews were semi-structured to allow the researcher to gather information on the experiences of faculty incorporating AI technologies into their teaching practices. The semi-structured interview method provided a fitting structure to explore the rich, complex, and detailed description of the experiences of community college faculty incorporating AI technologies in teaching practices. The researcher examined institutional archival documents to supplement semi-structured interviews and increase the credibility and reliability of the analyses. The researcher determined how AI technologies were addressed in institutional documents and policies using Huber's (1993) organizational change theory as the theoretical framework and Rogers' (1995) diffusion of innovation as the conceptual framework. Faculty concluded that AI could improve the quality of teaching practices by addressing uncertainties, bridging the digital divide, and promoting technology acceptance. Participants acknowledged that AI can foster creativity, facilitate experimental learning, support professional development, and enhance training to improve learner outcomes in the classroom. However, some faculty expressed concerns about integrating AI into teaching. Participants cited the potential long-term impact of learner critical thinking. They acknowledged that generative AI technology is limited and can create misleading or skewed information due to bias and hallucinations affecting student learning. Furthermore, faculty acknowledged the existence of the digital divide and its impact on first-generation learners. The participants agreed that first-generation learners could use AI technologies at the same levels as non-first-generation learners. However, some faculty noticed that first-generation learners associated guilt with AI usage or became hesitant to use the technology when the information generated was incorrect. Further research and ongoing support is necessary to continue advancing the integration of AI tools into educational practices. The findings of this study inform community college faculty about the multifaceted nature of integrating AI technologies into teaching practices. Community colleges interested in implementing or expanding faculty's use of AI in teaching practices could create a task force to develop policies and best practices and review and monitor academic integrity, ethical use, data privacy, and bias. Additionally, the institution can allocate resources for faculty professional development and training and launch pilot programs to learn how to use AI technologies in select areas. Keywords: organizational change theory, diffusion of innovation, Artificial Intelligence, personalized learning, adaptive learning, first-generation learner, community college, cultural capital, learner autonomy, andragogy, heutagogy, bias, hallucinations.
Keywords: Organizational change theory; Diffusion of innovation; Artifical intelligence; First-generation learner; Community college; Adaptive learning