This project involved designing a four-module online course to help higher education instructors understand and effectively integrate generative AI into their teaching practice.
The course emphasizes not only familiarity with tools but also pedagogical application, ethical considerations, and reflective practice.
Generative AI tools are rapidly entering higher education, yet many educators remain hesitant to adopt them. A lack of familiarity with AI tools, combined with concerns about academic integrity, ethical use, and student overreliance, has created uncertainty about how to integrate these tools into teaching and learning. In some cases, institutions have responded by restricting or banning AI use altogether rather than providing structured guidance for faculty.
At the same time, many existing resources focus primarily on how to use AI tools, rather than how to teach with them in ways that are ethical, intentional, and pedagogically sound.
As a result, many instructors lack the foundational knowledge and practical strategies needed to confidently integrate GenAI into their courses.
Generative AI for Teaching & Learning was developed to address this gap by supporting higher education instructors in building both conceptual understanding and applied strategies for responsible AI integration.
Measurable Learning Objectives From The Course
Generative AI for Teaching & Learning was developed using the Successive Approximation Model (SAM), emphasizing iterative design, feedback, and continuous improvement. The course is designed to support higher education educators in developing practical, transferable skills for integrating generative AI into their teaching.
The course targets college and university instructors, adjunct faculty, lecturers, and other adult learning professionals with varying levels of technical familiarity and prior exposure to AI tools. To support this range, the course incorporates andragogical principles and self-directed learning strategies, allowing participants to engage with content at their own pace and level of experience.
The design is structured around four progressive learning goals:
Define foundational concepts of generative AI
Explore and practice using AI tools in guided, low-risk environments
Apply AI strategies within their own teaching contexts
Evaluate ethical considerations and responsible use in educational settings
These goals are supported by a scaffolded progression:
Comprehension → Exploration → Application → Reflection
This progression encourages learners to move from foundational knowledge to practical implementation, while promoting critical thinking and intentional use of AI in their teaching practice.
Generative AI for Teaching & Learning is organized into four scaffolded modules designed to guide learners from foundational knowledge to applied practice and critical reflection. Each module builds on the previous one, supporting a gradual progression from concept development to real-world implementation.
Module 1, Intro to GenAI, establishes foundational concepts - clarifying what generative AI is and is not - to build a shared baseline of knowledge. Module 2, Exploring GenAI Tools, shifts into guided exploration, incorporating modeled demonstrations and hands-on practice with commonly used tools in educational contexts. Module 3, Practical Application, moves learners into direct application through scenario-based activities, allowing them to experiment with integrating AI into their own teaching practices. Module 4, Ethical Considerations, encourages critical reflection on academic integrity, responsible use, and the broader implications of AI in education.
Home Screen - Module Select
Module 1 Introductory Video (Screenshot)
A Practice Prompt for Self-Exploration of GenAI Tools, with Optinal Voiceover Audio
The following challenges and constraints were taken into consideration during the development of this course:
Rapidly evolving AI landscape: The fast pace of change made it impractical to focus on static tool instruction. Instead, the course emphasizes foundational concepts, critical thinking, and adaptable strategies that remain relevant as tools evolve.
Balancing breadth vs. depth: Given the wide range of available GenAI tools, the course prioritizes depth of understanding over exhaustive coverage, focusing on transferable skills rather than tool-specific mastery.
Designing for varied experience levels: To accommodate both novice and more experienced users, the course incorporates flexible navigation, optional supports (e.g., voiceover), and reflective activities that allow learners to engage at their own level.
Tension between GenAI adoption and academic integrity: Ongoing concerns about misuse informed the inclusion of ethical considerations and reflection prompts, encouraging learners to think critically about responsible, intentional use in their own contexts.
Generative AI for Teaching & Learning was developed in Articulate Storyline 360 to support interactive, self-paced learning through branching scenarios and guided practice. This approach aligns with the needs of adult learners, allowing for flexibility, autonomy, and active engagement with course content.
Several key design decisions shaped the learning experience:
1. Instructor-First Design - The course prioritizes educators as the primary audience, equipping them to build both conceptual knowledge and practical strategies before applying these approaches in their own teaching contexts. This ensures that learning transfers beyond the course into real-world instruction.
2. Reflection-Based Assessment - Traditional knowledge checks were replaced with structured reflection prompts at the end of each module. This approach encourages learners to:
Examine their assumptions and experiences with GenAI
Make personal connections to their teaching practice
Apply concepts directly to real-world instructional scenarios
This decision supports deeper learning and aligns with the course’s emphasis on critical thinking over content recall.
3. Scaffolded Tool Exposure - GenAI tools such as ChatGPT, Perplexity, Gamma, and NotebookLM are introduced progressively to reduce cognitive overload and support skill development. Learners engage with tools through:
Modeled video demonstrations
Guided practice prompts
This structure allows learners to build confidence in a low-risk environment before independently applying tools in their own contexts.
4. Ethics as a Core Component - Ethical considerations are embedded throughout the course rather than presented as a standalone topic. This approach ensures that learners:
Develop habits of responsible AI use
Understand implications for academic integrity
Feel prepared to guide student behavior and navigate institutional expectations
The course was evaluated by three subject matter experts and three instructional designers to assess content accuracy, pacing, engagement, usability, and alignment with learning outcomes. Feedback was collected through Google Forms (both qualitative and quantitative), analyzed for patterns, and used to inform targeted design improvements.
Based on this feedback, several key enhancements were implemented:
Reducing cognitive load: Experts noted potential text fatigue, prompting the addition of an optional voiceover feature to support aural learners and provide flexibility in content consumption.
Improving navigation: Limited ability to move between sections prompted the addition of a persistent “Home” button on every slide, allowing users to easily return to the main menu.
Reinforcing learning goals: To support ongoing alignment with objectives, a “Learning Objectives” button was embedded throughout the course for quick reference.
Supporting reflection and retention: Learners’ inability to revisit their responses led to the creation of a downloadable reflection journal (PDF), enabling continued engagement beyond the course.
These revisions improved usability, flexibility, and overall learner experience, while better supporting the course’s goal of promoting intentional and reflective GenAI use.