Pioneering AI-Driven Instructional Design in Small College Settings
For institutions that lack the budget or staff expertise to utilize instructional design principles in online course development, generative AI may offer a way forward.
One does not need to be a higher ed insider to notice the increasing number of colleges integrating instructional design (ID) principles into their course design workflows. Historically a staple of the corporate Learning & Design (L&D) space, ID refers to the discipline that focuses on elements of course quality extending beyond the subject matter itself, to questions surrounding "how" that material is presented, through the students' perspective.
On a broader level, it is the discipline that bridges the gap between on-ground and online learning by examining the ways in which students engage with course content. The benefits of ID are well-documented in the literature: Students in ID-enhanced courses experience improved persistence, retention, and achievement of learning objectives.
Unfortunately, smaller colleges — arguably the institutions whose students are likely to benefit the most from ID enhancements — frequently find themselves excluded from authentically engaging in the ID arena due to tight budgets, limited faculty online course design expertise, and the lack of ID-specific staff roles. Despite this, recent developments in generative AI may offer these institutions a low-cost, tactical avenue to compete with more established players.
At Lackawanna College — a smaller institution of higher education in Northeast Pennsylvania — our team has been piloting the use of generative AI in course development workflows. Through our pilot, we have been able to develop impressive course content that is arguably on par with many larger online institutions — and may represent a sustainable ID path forward for similar colleges.
Defining the Rationale and Challenges
Our journey started by asking self-reflective questions, the most important of which was: "What is the highest yield and lowest cost ID intervention that we can implement from a student success standpoint?" This lens brought us back to the basics of examining "how," on a text level, the subject matter itself was being presented to students. We realized that many of the trendier ID-interventions of today, such as adaptive learning and gamification — while absolutely important to student success, and ones we will revisit in the future — may need to take a back seat to the most critical ingredient of all in effective course design: the language being used within the course.
We realized that our students resonated the most with courses written in a professional, yet conversational style. Instead of terse, third-person language — similar to reading a textbook — we realized that students were more likely to engage with lessons and assignment directions where they felt like the instructor was truly speaking "to" them, akin to being an audience member to a captivating speaker.
We also realized the importance of context cues within components of a learning module. For example, in an on-ground class, an instructor often naturally gesticulates for emphasis, and we realized that capturing these "off the cuff" gestures in written form should be a critical area of focus to make our online courses even more authentic and relevant for students.
At the same time, we also recognized that the lack of dedicated editor and content writer roles — aside from the subject-matter expert (SME) — would make it challenging to implement these language-based initiatives across the full suite of online courses, let alone in a single online course. These challenges were reinforced by time-on-task calculations performed by our Instructional Technologist. These hurdles certainly weren't unique to our institution; to the contrary, they are frequently the norm at smaller colleges and universities. Despite these obstacles, our eLearning team leaned into a solutions-focused approach.
Adapting AI to Instructional Design
The reality is that a news article is being published almost daily about higher ed faculty using ChatGPT to draft lesson plans, create interactive learning materials, and personalize educational content for different learning styles. At the same time, notable case study applications of these tools on the ID front are limited, or at best, restricted to theorical models or more niche applications (gamification, personalized learning, predictive feedback, and real-time assessment). We wanted to pilot the use of these tools on a more practical front — as a "great equalizer" — to determine if we could achieve and generate course quality exceeding the bounds of an institution's existing ID scale. In other words, rather than us singularly looking at AI as a niche strategy for enhancing specific elements of an online course, we wanted to pilot a few AI-driven ID interventions, and lay the groundwork for a broader conversation on how we continue to offer a high educational value proposition to our students.