Keywords in higher ed: AI authoring tools
During my graduate degree coursework in composition and rhetoric, I came across a book titled Keywords in Writing Studies, edited by Paul Heiker and my professor himself, Peter Vandenberg.
The book’s concept is given in its title: Keywords provides a fresh and concise array of essay entries, each packed with heavy research dedicated to unpacking an operative referent in the realm according to its related studies, theories, and applications.
As a student that has kept nearly every required textbook, I can reflect on the utility of such a cogent textbook concept, and now would like to transfer its reader-friendly approach to the great wide realm of instructional technologies—to start, within in the smaller realm of AI authoring tools for teaching and learning.
I anticipate my keywords approach will be much messier and less formal in scholarship, as the body of published works, studies, and opinions on AI authoring is sprawling and immense. However, the goal is to offer an ongoing collection of resources that facilitate your own research and dialogue around important questions about technology in teaching and learning.
With this keywords approach in mind, let’s begin!
AI authoring tools & learning
AI authoring tools such as ChatGPT, Bard, DALL-E3, and the like, pose immediate questions for rethinking how to teach core learning tasks and skills, particularly those assigning students to compose original work.
Though there is no direct teaching solution to safeguard against cheating, and worse, whether a student is actually demonstrating their learning, many conversations in higher education circle back to how assessments are designed for students to think critically about information and acquire digital literacy. Such classroom-rooted strategies and conversations about AI authoring are also recommended by the leading product developing company in AI writing detection, Turnitin.
Difficulties in regulating AI use & ethical concerns
Studies have noted areas of AI use that pose challenges for demarcating its ethical scope and regulation. Key questions implicated by AI machine learning and data science include responsibility for use, bias and discrimination within development, transparency in development, and responsibility for stakeholder action or policy.
From a corporate stance, the move towards regulation is difficult, if not impossible, as implementation of restrictions cannot be imposed on a scale that corresponds with its users. Though statements and calls to pause development have been made, much AI development is within the private sector, and those that might be in the position to draft such regulations do not necessarily understand the nature and scope of the technological developments to impose effective boundaries.
Ethical considerations with AI authoring tools that more directly relate to teaching and learning include biases against non-English speakers and replications that bypass creative attribution, such as the popular query of Greg Rutkowski styled outputs that mimic his aesthetic without his consent.
Academic integrity & teaching with AI
Because of its dominance in the assessment tools arena and Loyola’s adoptions of several products, Turnitin resources on academic integrity and AI writing are within the purview of technology-based assessment in higher education. Their latest webinar offering on how to include AI in institutional policy offers a puzzle map for approaching the complex issue of AI.
An Exigence for Faculty Development
A silver lining that AI authoring brings to our attention is the prompt for enriching faculty development through dialogue and creative learning design.
Though some find AI authoring tools a cause for panic, many specialized faculty in the fields of medicine and sciences are excited about the opportunities AI provides for teaching and learning.
Reflections in faculty panels, such as this one at Ole Miss University of Mississippi or professional higher ed groups, such as the AI in Education Google group.
While Loyola Instructional Technology and Research Support does not decide on the adoption of learning tools for the institution, we do invite ideas for teaching strategies, further research, and learning designs.