The role of the instructional designer (ID) in higher education includes diverse responsibilities from design to evaluation (Ritzhaupt et al., 2010), requiring IDs to keep up with rapidly changing educational technology. This session explores how a team of IDs are integrating generative artificial intelligence (GenAI) into their workflows.

Background Generative artificial intelligence (GenAI) is the latest technology poised to make a significant impact on higher education. GenAI is a type of artificial intelligence that can produce various types of content in different formats such as text, data, images, coding, and audio based on user prompts (Peres et al., 2023). The recent and rapid diffusion of GenAI tools such as ChatGPT, Midjourney, and DALL-E have created a need for IDs to navigate how these new tools will transform not only their day-to-day tasks but teaching and learning altogether. GenAI is already having a profound impact on higher education, creating a myriad of challenges from academic integrity, algorithmic bias, to privacy concerns (Hodges & Ocak, 2023). While the implications of GenAI are yet to be fully understood, it is certain to have significant impacts on teaching and learning. The EDUCAUSE Horizon Report (2023) indicates that GenAI can open up opportunities for students to engage in higher-order thinking. Some studies have noted that GenAI can create new ways of thinking and new skill development (Eke, 2023). However, some have voiced concerns about biases and inaccuracies, overreliance on GenAI, and the potential of stifling creativity (Eapen et al, 2023; Lubowitz, 2023). Currently, GenAI cannot detect the accuracy of its outputs nor are there detectors that can accurately identify plagiarism in a student’s writing, however it is anticipated in the near future that accuracy will significantly improve (Peres et al., 2023). From the ID perspective, GenAI is already reshaping the creation of content, the personalization of learning experiences, and the automation of administrative tasks. All the while presenting new ethical use and data privacy challenges, necessitating the need for robust governance at the institutional leadership level (EDUCAUSE, 2024). Instructional designers essentially drive innovation in teaching and learning practices and ensure effective use of educational technology. To break down their tasks further, their work can involve designing and developing courses, facilitating faculty development, assisting with technology integration, developing assessment and evaluation, as well as managing all of these aspects (Ritzhaupt et al., 2010). The advent of GenAI has created a need for IDs to enhance their skills by leveraging these tools into their daily work tasks and processes. We are already seeing GenAI is being used to generate content, develop learning activities, assessment, and multimedia (EDUCAUSE, 2023), essentially redefining how they perform their tasks. ID’s have to keep up with trends in pedagogy and technology and often enhance their knowledge and skills through reading articles, websites, books, blogs, watching videos such as TED Talks, YouTube, as well as engaging with other IDs via conferences, listservs, and LinkedIn (Online Learning Consortium, 2016). They also utilize a variety of technologies to perform their tasks. For example, Canvas learning management systems for building courses, authoring tools for content development, editing tools to create and edit videos, and project management tools to help streamline projects. With such a broad range of tasks, tools, and skill sets, IDs are positioned to act as change agents across higher education institutions. Methods The sudden emergence of GenAI has further emphasized the importance of continuous learning in the ID field. ID’s now need to understand how to use GenAI in their roles and be able to guide faculty in its effective use. Utilizing the SAMR (Substitution, Augmentation, Modification, Redefinition) model, we gauge the depth of GenAI integration among ID’s. The SAMR Model is a technology integration framework used to help instructors and IDs thoughtfully design, create, integrate, and evaluate technology in the teaching and learning process (Puentedura, 2013). The SAMR model is composed of four tiers in order from the bottom enhancement tiers, which enhance, or exchange technology already used in a learning task (Hilton, 2016), to the top transformation tiers, which present opportunities not realized without technology (Kirkland, 2014). Using focus group methods, this study investigates how IDs are navigating the adoption and integrating generative AI (GenAI) tools and practices into their professional workflows. Through the lens of the SAMR model, we aim to answer the following questions: How are ID's developing their GenAI skills? What ID related tasks are they integrating GenAI? What level(s) of the SAMR Model are these integrations? What specific GenAI tools are they using in their ID tasks? By applying the SAMR model, we aim to systematically categorize and evaluate the extent to which GenAI is transforming instructional design practices among our ID team. This can help us enhance and redefine the tasks and tools IDs use in their day-to-day, from basic tasks to the creation of new transformative processes. Implications By mapping IDs’ GenAI integration across the SAMR continuum, we can gain insights to better support their professional development as well as better equip them to assist faculty with integrating GenAI. This study will identify areas and tasks where IDs are actively incorporating GenAI, such as content generation, instructional material development, and multimedia creation. This could help to identify opportunities for leveraging GenAI to automate or enhance instructional design processes, as well as identify tasks or processes that have been significantly modified or redefined through the use of GenAI. Furthermore, identifying the specific GenAI tools IDs are using as well as the capabilities and features they seek can guide the adoption of existing GenAI tools or inform the development of custom solutions tailored to their needs. Lastly, the findings could reveal opportunities for leveraging GenAI to automate or enhance certain instructional design processes (substitution and augmentation), as well as identify tasks or processes that have been significantly modified or redefined through the use of GenAI. Concisely, this study aims to help leverage GenAI in ID tasks by identifying how IDs are integrating the technology using the SAMR model as a way to analyze ID tasks and GenAI integration. Takeaways This session will review the findings of a small focus group consisting of an instructional design team with the purpose of identifying how the IDs are acquiring GenAI skills, what tasks they use GenAI for, and the tools they are using for these tasks. People who attend this session will walk away with ideas on how to incorporate GenAI into their own instructional design processes and better support faculty and students. References Eapen,T. T., Finkenstadt, D.J.; Folk, J.; Venkataswamy, L. (2023, July). How generative AI can augment human creativity. Harvard Business Review. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity. EDUCAUSE. (2023, August 16). 10 ways artificial intelligence is transforming instructional design. EDUCAUSE Review. https://er.educause.edu/articles/2023/8/10-ways-artificial-intelligence-is-transforming-instructional-design. EDUCAUSE. (2023). EDUCAUSE Horizon Report: Teaching and Learning Edition. EDUCAUSE. 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