GenAI in Instructional Design: A Practical Guide for Higher Education

Generative AI (GenAI) is rapidly transforming the educational landscape, presenting both opportunities and challenges for institutions, educators, instructional designers (IDs), and students. GenAI, powered by large language models (LLMs) and advanced algorithms, is redefining content creation across various formats, including text, images, audio, and code. This technological advancement is prompting educators and IDs to rethink traditional teaching, learning, and assessment practices. IDs are at the forefront of this transformation, tasked with integrating GenAI into educational environments while maintaining academic integrity and pedagogical rigor. This article explores the GenAI Intent and Orientation Model to provide a framework for leveraging GenAI to support and enhance teaching and learning outcomes from an ID perspective.

GenAI represents a significant technological advancement with profound implications for higher education. It enables systems to generate content in response to user prompts, utilizing large language models trained on vast datasets. Since its public introduction in 2022, GenAI technology has advanced rapidly, introducing both opportunities and challenges. The Educause Horizon Report (2023, 2024) identifies GenAI as a technology with a significant impact on learning and teaching, potentially allowing students to focus on higher-order thinking skills. However, concerns exist regarding over-reliance on technology, algorithmic biases, academic integrity, and student data privacy. Institutions are actively experimenting with GenAI, with some adopting principles for its use and others developing their own GenAI tools to ensure privacy and security.

GenAI promises to redefine the role of instructional designers (IDs), offering opportunities to enhance productivity and improve course design. IDs can leverage GenAI to create course outlines, align learning objectives with assessments and materials, and develop scripts for multimedia content. Collaboration between IDs and faculty is crucial for designing effective courses, integrating technology, and ensuring quality and accessibility. While active learning is desirable, the GenAI Intent and Orientation Model aims to promote effective interaction by utilizing GenAI in various educational contexts, such as a learning companion or a vehicle for task delivery.

The GenAI Intent and Orientation Model, introduced in mid-2024, explores the potential applications of GenAI within the instructor-student relationship. It considers the purpose (intention) of the actor using GenAI and the target audience. This model provides a conceptual framework that accounts for both the originator's purpose and the audience's needs. The model identifies four quadrants based on intention and orientation: Instructor Intention/Instructor Orientation (I>I), Learner Intention/Learner Orientation (L>L), Instructor Intention/Learner Orientation (I>L), and Learner Intention/Instructor Orientation (L>I).

In the I>I quadrant, the instructor or ID uses a GenAI platform to perform tasks involved in building a high-quality course. The user has a need and interacts with GenAI to complete a specific task. The ID is assumed to have knowledge and ability but still needs to collaborate with an instructor to create content that meets all needs, including appropriate learning objectives and content aligned with program requirements. The ID is considered an extension of the instructor in this context.

The L>L quadrant includes interactions where the learner uses GenAI to accelerate their learning or find an easy way out, potentially hindering the learning of key concepts. The appropriateness of use cases in this quadrant depends on the activity and the instructor. Clear communication of expectations regarding GenAI is crucial to ensure students understand the 'why' behind critical concepts before using GenAI to expand their capabilities.

In the I>L quadrant, the instructor creates materials, including sophisticated prompts and customized GPTs, to meet the learner's needs. The GenAI tool acts as an 'Instructor Proxy' at the moment of need. Integrating GenAI-based activities into the curriculum promotes GenAI literacy, preparing students for a work environment where they will collaborate with GenAI.

The L>I quadrant explores how a student might interact with GenAI to inform or improve the instructor's teaching. In L>I scenarios, the student uses GenAI to produce something, such as data or a report, that the instructor uses to make improved teaching decisions or provide personalized feedback. This quadrant positions the learner as an indirect but intentional contributor to instructional understanding. Learning analytics, where student interactions with course content are collected and analyzed, can be used by GenAI to provide instructors with actionable insights.

Instructional designers must adapt to the changing landscape by integrating GenAI tools into their workflows. Strategies include developing clear guidelines for GenAI use, providing training and support for faculty, and fostering collaboration between instructors and IDs. Addressing ethical concerns, such as academic integrity and data privacy, is also crucial. By embracing GenAI, IDs can enhance their productivity and create more engaging and effective learning experiences.

GenAI presents significant opportunities for transforming higher education and instructional design. By understanding the GenAI Intent and Orientation Model and implementing effective strategies, educators and IDs can leverage GenAI to create personalized, engaging, and effective learning experiences. Embracing GenAI requires a collaborative approach, addressing ethical concerns, and continuously adapting to the evolving technological landscape.