AI-mpowered Teaching: A Model for GenAI Teaching Integration
General description of the project
The presenter will guide attendees through a process for integrating Generative AI (GenAI) into their teaching practices. Utilizing the ASSURE Instructional Design Model, participants will be able to follow a systematic approach for integrating appropriate GenAI tools into their teaching context. Integrating GenAI into my teaching practices has been an enlightening journey, revealing several key insights. First, GenAI has augmented my role as an educator, not replaced it. It enhanced the efficiency of content creation and feedback processes, allowing me more time to focus on personalized interactions with students. This integration necessitated a shift from traditional teaching methods to more innovative, tech-integrated approaches, ensuring a blend of human creativity and AI efficiency.
The integration of GenAI also highlighted the importance of a human-centered approach in teaching. Despite its ability to streamline content creation and provide diverse perspectives, the essence of education still relied heavily on human values, imagination, and creativity. Balancing GenAI’s capabilities with ethical considerations and institutional policies was crucial, as it necessitates a responsible and transparent use of technology in educational settings.
Technologies
Generative text AI (GenAI) tools can help you with everything from brainstorming course scenarios to aligning learning activities with outcomes and creating feedback loops to assist with assessing student learning. The presenter has used these tools to design and build online course content, anywhere from developing lesson materials to building assignments, and assessments to generating AI-bots to assist in the teaching and learning process.
Explain project results
Once I started integrating Generative AI (GenAI) into my teaching practices, I experienced a profound transformation in both my efficiency and the inclusivity of my curriculum. Initially, I was skeptical about the practicality and ethical implications of using AI in an educational setting. However, as I familiarized myself with the capabilities of GenAI, I began to see its potential.
The first notable change was in the preparation of my course materials. GenAI tools allowed me to streamline the content creation process, generating ideas, outlines, and even draft texts for lectures, assignments, and discussions. This efficiency freed up valuable time, which I could then invest in tailoring my materials to better suit the diverse needs and backgrounds of my students.
Perhaps the most significant impact of GenAI was its role in helping me create a more inclusive curriculum. With access to vast amounts of information and data, GenAI enabled me to incorporate a wider range of perspectives and voices into my course content. I could quickly gather and integrate materials that represented diverse cultures, histories, and viewpoints, making my courses more relatable and accessible to a broader student demographic.
In essence, integrating GenAI into my teaching practices has not only streamlined my workload but also enriched the educational experience for my students. It has enabled me to create a learning environment that is both more inclusive and adaptive to the diverse needs of today’s students, preparing them better for a rapidly evolving, technologically advanced world.
Why it should be considered best practice?
The integration of Generative AI (GenAI) into teaching practices is considered a best practice due to its multifaceted benefits and replicability across various educational contexts. GenAI tools support student learning in diverse ways, such as aiding in brainstorming ideas, providing real-time writing feedback, and enhancing understanding of language patterns and usage. This versatility makes GenAI a valuable asset in different teaching scenarios.
Also, the ease of integration and the universal applicability of GenAI tools contribute to their replicability in educational settings. Whether it’s enhancing literacy skills or providing equitable learning opportunities, GenAI’s adaptability to various educational needs and its ability to complement existing teaching methods make it a sustainable and effective practice.
Highlights of your proposed presentation
The session on integrating Generative AI (GenAI) into teaching practices was a comprehensive exploration of the transformative potential of GenAI tools in education. Key highlights include:
1. Understanding GenAI Capabilities: The session began with an overview of how GenAI can aid in various aspects of teaching, from brainstorming course scenarios to providing feedback and aligning learning activities with desired outcomes.
2. Applying the ASSURE Model: Participants learned to use the ASSURE Instructional Design Model as a framework for integrating GenAI into their teaching. This systematic approach involves analyzing learners, stating objectives, selecting methods and media, utilizing GenAI tools, requiring learner participation, and evaluating and revising teaching strategies.
3. Practical Examples of GenAI Integration: The presenter showcased practical applications of GenAI, such as designing content, creating assignments, formulating assessments, and even generating AI-bots to assist in teaching.
Lessons learned from the session include:
1. Enhancing Teaching Efficiency: GenAI tools can significantly improve the efficiency of the teaching process, allowing educators to focus more on interactive and personalized teaching aspects.
2. Fostering Inclusivity and Diversity: Through GenAI, educators can develop a more inclusive curriculum that caters to diverse learning needs and styles.
3. Balancing Technology with Traditional Methods: While GenAI offers innovative solutions, it’s crucial to balance these with traditional teaching methods for a well-rounded educational experience.
4. Continuous Learning and Adaptation: Educators need to continually update their skills and strategies to keep up with the rapid advancements in AI technology.
The Evaluation Committee will evaluate submitted proposals based on the following criteria. Each area will be rated on a scale from 1 to 7 (1= non-satisfactory; 7 =outstanding), for a maximum of 63 points.