In the previous edition of Online: Trending Now, we looked at the artificial intelligence (AI) tools and activities of the AI-augmented professor. Yet there are more staff members who support the learning process at most universities than there are those who directly teach the students. Let’s take a look at how AI will facilitate the work of the instructional designers, researchers, administrators and other nonteaching professionals in colleges and universities this fall.
This comes in the context of Microsoft and LinkedIn’s 2024 Work Trend Index Report which surveyed some 31,000 people across 31 countries, uncovering rather surprising facts along the way. Bret Kinsella writes in Synthedia: “The report found that 78 percent of knowledge workers bring their own AI (BYOAI) to work. For GenZ, the figure is 85 percent. However, even Baby Boomers are BYOAI at a rate of 73 percent.” This phenomenal trend has unfolded in just one year. Citing the same large survey by Microsoft and LinkedIn, Sabrina Ortiz writes in ZDNet that “AI skills are so much of a priority that the report suggests 66 percent of business leaders wouldn’t hire someone without AI skills, and 71 percent of leaders would prefer to hire a less experienced candidate with AI skills than a more experienced candidate without them.”
Many instructional designers are among the BYOAI group who use generative AI (GenAI) tools on a daily basis to create graphics, images and audio segments for university classes. Having an AI-enhanced toolset available at home as well as work allows for seamless hybrid working that appeals to many designers. While OpenAI’s Sora that generates full-motion videos from prompts, is only available to a limited number of testers now, it is expected to be released soon. Content generation can be tailored for transitions by instructional designers, who often use multiple chatbots with citations to ensure accurate and precise, quotable materials for classes. The flow and design of classes, culminating in learning outcomes, may also be recommended by GenAI to the instructional designer.
A favorite approach of mine to learning design is mastery learning. While mastery learning modules can be constructed manually, a number of automated, AI-driven systems are available to speed up the process and bring a standardized process to the delivery and assessment of learning, while enabling personalization of learning along the way.
University researchers have been using AI applications for years. The new release of multiple expansive large language models facilitates the collection and analysis of data that can be run in a secure environment, “air-gapped” from the public internet and secured on university computers that are not connected outside the research environment. The integration of many of the current chatbots with common tools of analysis, presentation and publication facilitate the creation and delivery of research reports. Further, GenAI can uncover less obvious correlations, causes, effects and trends that might otherwise not be obvious to researchers. Especially when working with multiple datasets, new associations can be uncovered that can make the research much more valuable.
Deans, directors and department heads or chairs also have much to gain from use of GenAI. Of course, we must be careful not to use confidential personnel or financial data in the prompts that we submit to open large language models. Depending on the level of security, volume, complexity and isolation of data that is required, there is, however, the advent of Small Language Models (SLMs) that are surprisingly powerful when operating on small platforms, such as smart phones. With connectivity turned off, these devices are not connected to the internet (air-gapped), maintaining security and privacy.
As Joinal Ahmed of Microsoft reports “Small Language Models (SLMs) represent a focused subset of artificial intelligence tailored for specific enterprise needs within Natural Language Processing (NLP). Unlike their larger counterparts like GPT-4, SLMs prioritize efficiency and precision over sheer computational power. They are trained on domain-specific datasets, enabling them to navigate industry-specific terminologies and nuances with accuracy.” This means that privacy is retained by maintaining all prompts, data, processing and results in an environment that is not connected to the cloud.
In this secure environment, administrators can ask apps to perform personnel, budget, and organizational comparisons, analyses and recommendations without exposing the data to possible discovery by others. For example, an administrator can input the college or department budget onto the app and perform an offline analysis of expenditures as well as prompt the app to run budget scenarios for 10% or 20% reductions while optimizing outcomes such as ROI, student enrollment and student success.
In less-secure outward-facing analyses, an administrator can ask for a search of public records to identify details of the student enrollments in analogous departments at competing universities. GenAI can compare publicly available enrollment demographic data to construct a more effective enrollment plan and associated marketing budget. In addition, administrators may want to project future industry hiring in fields associated with degrees and certificates offered or planned by the university. The GenAI tools can find and assemble data that we simply did not consider in the past for powerful analyses and recommendations.
In the highly competitive atmosphere of higher education recruiting, American universities are often pressured by uncertain funding and political interventions that may modify long-standing policies upon which the public had relied. In response to these pressures, universities must have access to the best data, the most powerful analyses, and the most innovative recommendations to enhance recruiting and enrollment in order to cover the ever-rising expenses. We are now facing the closure of one college or university every single week in 2024. GenAI can provide the creativity, backed by sound data and analyses that might make the difference between a balanced budget and the slippery slope toward closure.
