A synopsis of a paper I developed at a conference earlier this spring:
Generative artificial intelligence (GAI) arrived in schools in a serious way in the final months of 2022 when ChatGPT became widely available (Emmert-Streib, 2024). Since then, GAI has followed a pattern familiar to those who study technology. Commonly called the Gartner hype cycle (Dedehayir & Steinert, 2016), new technologies are met with a rich narrative describing fantastic potential to “change everything.” This is followed by a realization that the initial claims were hyperbole. Finally, practitioners discover the most useful applications of it, the most stable platforms, and the aspects of work that can be most affected by the tools. During this final phase, the technology is used to improve productivity and, for educators, is integrated into lessons and activities.
This presentation describes the experiences of a small group of professionals leading efforts to support faculty into this final stage of the cycle. We were interested in answering the question “what GAI tools are the easiest to use and most useful for faculty and students?”
Our examination of GAI tools was motivated by opinions shared by students that they wanted to learn how GAI tools can improve their learning. This opinion was expressed during a period when both our school and educators in general were expressing ambiguity about the role of GAI in school (Hsu, 2023). As a community of practitioners, we sought to help faculty identify useful GAI tools, adopt those that are useful, and adapt their teaching to reflect the responsible use of these tools.
The presentation focuses on four generalizations we have made based on nearly two years of focus groups, workshops, small and large group discussions at our school. Organized around iterative processes following phases based on educational design research (McKenny & Reeves, 2012), these discussions included faculty, students, and staff and focused on both emerging tools and local instantiations of GAI tools. We describe reframing the purpose of education, supplementing materials, customizing tools, and improving efficiency.
Reframing the Purpose of Education
A generation (or more) of students have experienced what we call outcomes-based teaching. Ostensibly, learning outcomes identify cognitive skills we expect the student to develop; we have found the reality is that the lessons become outputs based. Teachers, and thus their students, value test scores, research papers, or other evidence that can provide data supporting the conclusion the outcomes have been achieved. While those artifacts are valuable demonstrations of students’ learning, by framing GAI as a cognitive tool (Jonassen, 1995) rather than as a source of completed works, we have given both students and faculty an alternative way to view GAI.
Faculty who advocated banning GAI have begun to find ways to incorporate it in meaningful ways, and they attribute the change to this reframing. Students have also begun to see GAI as another tool they can use to better understand the material they are studying. In the presentation, we will share qualitative data indicating this change.
Creating Supplemental Materials
Our community values universal design for learning (Coffman & Draper, 2022), and interest in providing alternative modes of interacting with classroom materials continues to be strong at our school. Faculty at our school have made significant use of GAI to create audio interpretations of important resources for their courses. These have been used to both prime students for reading assignments and to review works after reading. This has proven to be one of the most popular GAI applications within our community; the presentation will include examples and demonstrate the platform we support.
Customizing Tools
Early in the hype cycle, some faculty explained reluctance to adopt GAI tools and actively discouraged students from using them reasoning they had no control over the content. In response to this, we have undertaken pilot projects in which faculty create customized chatbots for their courses. Beginning with their syllabus, we support faculty to upload articles they assign to students, open educational resources, add links, and otherwise build the resources specific for their courses. Currently, we are investigating options for expanding this option for additional faculty and exploring the curation of custom chatbots as a learning activity for students.
Improving Efficiency
Faculty in our institution use a range of open educational resources as primary texts as well as supplemental material for instruction. One challenge for those faculty is making ancillary material that is provided by textbook publishers. Faculty who uses teaching materials for which ancillary materials are not available have been using GAI to create study guides, test questions, and other support materials. This has both increased their efficiency and encourages faculty to consider adopting open resources as the primary text for courses.
References
Coffman, S., and Draper, C. (2022). Universal design for learning in higher education: A concept analysis. Teaching and Learning in Nursing 17(1), 36-41.
Dehair, O., & Steinert, M. (2016). The hype cycle model: A review and future directions. Technological Forecasting and Social Change, 108, 28-41. https://doi.org/10.1016/j.techfore.2016.04.005
Emmert-Streib, F. (2024). Is ChatGPT the way toward artificial general intelligence. Discover Artificial Intelligence 4, 32. https://doi.org/10.1007/s44163-024-00126-3
Hsu, J. (2023). Should schools ban AI chatbots? New Scientist 257(3422), 15.
Jonassen, D.H. Computers as cognitive tools: Learning with technology, not from technology. Journal of Computing in Higher Education 6, 40–73. https://doi.org/10.1007/BF02941038
McKenny, S., & Reeves, T. (2012). Conducting educational design research. New York: Routledge