This session explores the integration of generative AI into instructional design models, enhancing data collection, content creation, and adaptive learning. Participants will gain practical knowledge of AI tools, strategies for integration, and actionable insights to create more personalized and effective learning experiences.

Introduction: Integrating generative AI into instructional design (ID) models presents a significant opportunity to enhance the effectiveness and efficiency of the design process. By leveraging AI technologies, instructional designers can augment traditional models such as ADDIE, Kemp, and SAM, creating more personalized and adaptive learning experiences. This presentation will explore how AI tools can significantly augment various aspects of the instructional design process, particularly in data collection and analysis, content development, and the creation of adaptive learning pathways. Relevance and Importance: Generative AI has the potential to revolutionize instructional design by automating data analysis, streamlining content development, and providing real-time feedback. With generative AI, organizations can streamline, automate, and enhance their company’s learning function. This process includes creating a new framework to identify generative AI opportunities and using tools to create a tailored, transparent, and scalable approach by reinventing the ADDIE model (Ramos, 2023). AI data collection and analysis tools enable instructional designers to gather and interpret large amounts of data more efficiently. This data can include learner performance metrics, engagement levels, and feedback, which can be used to tailor instructional strategies to meet the specific needs of learners. Additionally, AI-assisted content creation facilitates the development of customized learning materials. These tools can generate content aligned with learners' preferences and cultural contexts, enhancing the relevance and accessibility of the learning experience. This session is particularly relevant to the instructional design community as it addresses the increasing need for innovative approaches that can keep pace with technological advancements and evolving learner needs. By integrating AI into instructional design models, educators and designers can create more effective, engaging, and personalized learning experiences that better meet the diverse needs of today's learners. Interactivity and Engagement Strategy: To ensure a highly interactive and engaging session, the presentation will incorporate the following strategies: Interactive Polls and Q&A: Throughout the session, interactive polls will be used to gauge attendees' current use of AI in their instructional design practices and to gather real-time feedback on the topics discussed. A Q&A segment will be included to address specific questions and challenges faced by the participants. Hands-On Activities: Participants will engage in hands-on activities where they can experiment with AI tools in real time. This will include guided exercises on using AI for data analysis and content creation, allowing attendees to experience the practical applications of these technologies. Group Discussions: Attendees will participate in small group discussions to brainstorm and share ideas on how to integrate AI into their instructional design practices. This will foster peer learning and collaboration, encouraging participants to think critically about the opportunities and challenges associated with AI integration. Learning Outcomes: By the end of this session, participants will: Gain practical knowledge of AI tools and their applications in the ID process: Participants will learn about various AI tools and how they can be applied to different stages of the instructional design process, from data collection and analysis to content development and adaptive learning. Be able to identify opportunities for integrating AI into their instructional design practices: Attendees will be equipped with the knowledge to identify specific areas within their instructional design practices where AI can be effectively integrated to enhance learning experiences. Develop strategies for addressing challenges associated with AI integration: The session will provide practical strategies for addressing common challenges to AI integration. Leave with actionable insights and tools to implement AI-augmented ID models in their organizations: Participants will walk away with a set of actionable insights and practical tools that they can apply to their instructional design practices. Takeaways: Participants will take away a deeper understanding of the transformative potential of generative AI in instructional design, along with practical skills and strategies for implementing AI-augmented ID models. This session aims to empower instructional designers, faculty, and design thinkers to innovate and enhance the learning experiences they create, making them more effective, engaging, and personalized for learners. The knowledge and tools gained from this session will enable attendees to meet the evolving needs of their learners, ultimately improving the quality and impact of their instructional design efforts. Ramos, M. (2023, October 5). Transitioning your learning team to generative AI: Become the exemplar for your enterprise. https://www.chieflearningofficer.com/2023/10/05/transitioning-your-learning-team-to-generative-ai-become-the-exemplar-for-your-enterprise